Note: the module "R" cannot be unloaded because it was not loaded. WARNING: ignoring environment value of R_HOME R Under development (unstable) (2025-05-18 r88216) -- "Unsuffered Consequences" Copyright (C) 2025 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > cargs <- commandArgs(trailingOnly=TRUE) > if(length(cargs)==0){ + ## before running interactively, make sure to start emacs/R with + ## environment defined in /scratch/...check_one.sh, particularly + ## R_LIBS_USER=/tmp/... otherwise we get error when installing + ## data.table. + base <- "/scratch/th798/data.table-revdeps/*" + cargs <- c( + Sys.glob(file.path(base,"deps.csv")), + "349", + Sys.glob(file.path(base, "data.table_release_*tar.gz")), + Sys.glob(file.path(base, "data.table_master_*tar.gz")) + ) + } > names(cargs) <- c("deps.csv", "task.str", "release", "master") > dput(cargs) c(deps.csv = "/scratch/th798/data.table-revdeps/2025-05-19/deps.csv", task.str = "1149", release = "/scratch/th798/data.table-revdeps/2025-05-19/data.table_release_1.17.2.tar.gz", master = "/scratch/th798/data.table-revdeps/2025-05-19/data.table_master_1.17.99.c4ea09e099b1833fae0eacb50552bce6f051ed82.tar.gz" ) > (task.dir <- dirname(.libPaths()[1]))#should be /tmp/th798/slurmid/R-vers [1] "/tmp/th798/17369309/R-devel/1149" > if(requireNamespace("R.cache"))R.cache::getCachePath() Loading required namespace: R.cache [1] "/tmp/th798/17369309/R-devel/1149/R.cache" > task.id <- as.integer(cargs[["task.str"]]) > deps.df <- read.csv(cargs[["deps.csv"]]) > (rev.dep <- deps.df$Package[task.id]) [1] "rBiasCorrection" > job.dir <- file.path(dirname(cargs[["deps.csv"]]), "tasks", task.id) > setwd(task.dir) > .libPaths() [1] "/tmp/th798/17369309/R-devel/1149/library" [2] "/projects/genomic-ml/R/R-devel/library" > options(repos=c(#this should be in ~/.Rprofile too. + CRAN="http://cloud.r-project.org")) > print(Sys.time()) [1] "2025-05-19 00:32:07 MST" > install.time <- system.time({ + install.packages(rev.dep, dep=TRUE) + }) Installing package into '/tmp/th798/17369309/R-devel/1149/library' (as 'lib' is unspecified) also installing the dependencies 'xmlparsedata', 'lintr', 'quarto' trying URL 'http://cloud.r-project.org/src/contrib/xmlparsedata_1.0.5.tar.gz' trying URL 'http://cloud.r-project.org/src/contrib/lintr_3.2.0.tar.gz' trying URL 'http://cloud.r-project.org/src/contrib/quarto_1.4.4.tar.gz' trying URL 'http://cloud.r-project.org/src/contrib/rBiasCorrection_0.3.5.tar.gz' * installing *source* package 'xmlparsedata' ... ** this is package 'xmlparsedata' version '1.0.5' ** package 'xmlparsedata' successfully unpacked and MD5 sums checked ** using staged installation ** R ** byte-compile and prepare package for lazy loading ** help *** installing help indices ** building package indices ** testing if installed package can be loaded from temporary location ** testing if installed package can be loaded from final location ** testing if installed package keeps a record of temporary installation path * DONE (xmlparsedata) * installing *source* package 'quarto' ... ** this is package 'quarto' version '1.4.4' ** package 'quarto' successfully unpacked and MD5 sums checked ** using staged installation ** R ** inst ** byte-compile and prepare package for lazy loading ** help *** installing help indices *** copying figures ** building package indices ** installing vignettes ** testing if installed package can be loaded from temporary location ** testing if installed package can be loaded from final location ** testing if installed package keeps a record of temporary installation path * DONE (quarto) * installing *source* package 'rBiasCorrection' ... ** this is package 'rBiasCorrection' version '0.3.5' ** package 'rBiasCorrection' successfully unpacked and MD5 sums checked ** using staged installation ** R ** data *** moving datasets to lazyload DB ** inst ** byte-compile and prepare package for lazy loading ** help *** installing help indices ** building package indices ** installing vignettes ** testing if installed package can be loaded from temporary location ** testing if installed package can be loaded from final location ** testing if installed package keeps a record of temporary installation path * DONE (rBiasCorrection) * installing *source* package 'lintr' ... ** this is package 'lintr' version '3.2.0' ** package 'lintr' successfully unpacked and MD5 sums checked ** using staged installation ** R ** inst ** byte-compile and prepare package for lazy loading ** help *** installing help indices *** copying figures ** building package indices ** installing vignettes ** testing if installed package can be loaded from temporary location ** testing if installed package can be loaded from final location ** testing if installed package keeps a record of temporary installation path * DONE (lintr) The downloaded source packages are in '/tmp/th798/17369309/RtmpcaudWG/downloaded_packages' > cat("Time to install revdep:\n") Time to install revdep: > print(install.time) user system elapsed 25.762 2.113 83.019 > print(Sys.time()) [1] "2025-05-19 00:33:30 MST" > downloaded_packages <- file.path( + tempdir(), + "downloaded_packages") > dl.glob <- file.path( + downloaded_packages, + paste0(rev.dep,"_*.tar.gz")) > rev.dep.dl.row <- cbind(rev.dep, Sys.glob(dl.glob)) > colnames(rev.dep.dl.row) <- c("pkg","path") > rev.dep.release.tar.gz <- normalizePath(rev.dep.dl.row[,"path"], mustWork=TRUE) > pkg.Rcheck <- paste0(rev.dep, ".Rcheck") > > proj.dir <- "~/genomic-ml/data.table-revdeps" > source(file.path(proj.dir, "myStatus.R")) > Rvers <- gsub("[()]", "", gsub(" ", "_", R.version[["version.string"]])) > dir.create(Rvers, showWarnings=FALSE) > Rcheck.list <- list() > for(dt.version.short in c("release", "master")){ + dt.tar.gz <- cargs[[dt.version.short]] + dt.version <- gsub(".tar.gz|/.*?_", "", dt.tar.gz) + print(Sys.time()) + install.packages(dt.tar.gz, repos=NULL) + print(Sys.time()) + check.cmd <- get_check_cmd(rev.dep.release.tar.gz) + system(check.cmd) + print(Sys.time()) + dest.Rcheck <- file.path( + Rvers, + paste0(dt.version, ".Rcheck")) + unlink(dest.Rcheck, recursive=TRUE) + file.rename(pkg.Rcheck, dest.Rcheck) + Rcheck.list[[dt.version]] <- file.path(dest.Rcheck, "00check.log") + } [1] "2025-05-19 00:33:30 MST" Installing package into '/tmp/th798/17369309/R-devel/1149/library' (as 'lib' is unspecified) * installing *source* package 'data.table' ... ** this is package 'data.table' version '1.17.2' ** package 'data.table' successfully unpacked and MD5 sums checked ** using staged installation zlib 1.2.13 is available ok * checking if R installation supports OpenMP without any extra hints... yes ** libs using C compiler: 'gcc (Spack GCC) 12.2.0' /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c assign.c -o assign.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c between.c -o between.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c bmerge.c -o bmerge.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c chmatch.c -o chmatch.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c cj.c -o cj.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c coalesce.c -o coalesce.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c dogroups.c -o dogroups.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c fastmean.c -o fastmean.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c fcast.c -o fcast.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c fifelse.c -o fifelse.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c fmelt.c -o fmelt.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c forder.c -o forder.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c frank.c -o frank.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c fread.c -o fread.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c freadR.c -o freadR.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c froll.c -o froll.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c frollR.c -o frollR.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c frolladaptive.c -o frolladaptive.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c fsort.c -o fsort.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c fwrite.c -o fwrite.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c fwriteR.c -o fwriteR.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c gsumm.c -o gsumm.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c idatetime.c -o idatetime.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c ijoin.c -o ijoin.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c init.c -o init.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c inrange.c -o inrange.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c nafill.c -o nafill.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c negate.c -o negate.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c nqrecreateindices.c -o nqrecreateindices.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c openmp-utils.c -o openmp-utils.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c programming.c -o programming.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c quickselect.c -o quickselect.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c rbindlist.c -o rbindlist.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c reorder.c -o reorder.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c shift.c -o shift.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c snprintf.c -o snprintf.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c subset.c -o subset.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c transpose.c -o transpose.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c types.c -o types.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c uniqlist.c -o uniqlist.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c utils.c -o utils.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c vecseq.c -o vecseq.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c wrappers.c -o wrappers.o /packages/gcc/12.2.0-nnbserq/bin/gcc -shared -L/home/th798/.conda/envs/emacs1/lib -Wl,-rpath=/home/th798/.conda/envs/emacs1/lib -L/home/th798/lib -Wl,-rpath=/home/th798/lib -L/home/th798/lib64 -Wl,-rpath=/home/th798/lib64 -o data.table.so assign.o between.o bmerge.o chmatch.o cj.o coalesce.o dogroups.o fastmean.o fcast.o fifelse.o fmelt.o forder.o frank.o fread.o freadR.o froll.o frollR.o frolladaptive.o fsort.o fwrite.o fwriteR.o gsumm.o idatetime.o ijoin.o init.o inrange.o nafill.o negate.o nqrecreateindices.o openmp-utils.o programming.o quickselect.o rbindlist.o reorder.o shift.o snprintf.o subset.o transpose.o types.o uniqlist.o utils.o vecseq.o wrappers.o -fopenmp -L/home/th798/.conda/envs/emacs1/lib -lz PKG_CFLAGS = -fopenmp -I/home/th798/.conda/envs/emacs1/include PKG_LIBS = -fopenmp -L/home/th798/.conda/envs/emacs1/lib -lz if [ "data.table.so" != "data_table.so" ]; then mv data.table.so data_table.so; fi if [ "" != "Windows_NT" ] && [ `uname -s` = 'Darwin' ]; then install_name_tool -id data_table.so data_table.so; fi installing to /tmp/th798/17369309/R-devel/1149/library/00LOCK-data.table/00new/data.table/libs ** R ** inst ** byte-compile and prepare package for lazy loading ** help *** installing help indices ** building package indices ** installing vignettes ** testing if installed package can be loaded from temporary location ** checking absolute paths in shared objects and dynamic libraries ** testing if installed package can be loaded from final location ** testing if installed package keeps a record of temporary installation path * DONE (data.table) [1] "2025-05-19 00:33:55 MST" * using log directory '/tmp/th798/17369309/R-devel/1149/rBiasCorrection.Rcheck' * using R Under development (unstable) (2025-05-18 r88216) * using platform: x86_64-pc-linux-gnu * R was compiled by gcc (Spack GCC) 12.2.0 GNU Fortran (Spack GCC) 12.2.0 * running under: Red Hat Enterprise Linux 8.10 (Ootpa) * using session charset: ASCII * checking for file 'rBiasCorrection/DESCRIPTION' ... OK * this is package 'rBiasCorrection' version '0.3.5' * package encoding: UTF-8 * checking package namespace information ... OK * checking package dependencies ... OK * checking if this is a source package ... OK * checking if there is a namespace ... OK * checking for executable files ... OK * checking for hidden files and directories ... OK * checking for portable file names ... OK * checking for sufficient/correct file permissions ... OK * checking whether package 'rBiasCorrection' can be installed ... OK * checking installed package size ... OK * checking package directory ... OK * checking 'build' directory ... OK * checking DESCRIPTION meta-information ... OK * checking top-level files ... OK * checking for left-over files ... OK * checking index information ... OK * checking package subdirectories ... OK * checking code files for non-ASCII characters ... OK * checking R files for syntax errors ... OK * checking whether the package can be loaded ... OK * checking whether the package can be loaded with stated dependencies ... OK * checking whether the package can be unloaded cleanly ... OK * checking whether the namespace can be loaded with stated dependencies ... OK * checking whether the namespace can be unloaded cleanly ... OK * checking loading without being on the library search path ... OK * checking whether startup messages can be suppressed ... OK * checking dependencies in R code ... OK * checking S3 generic/method consistency ... OK * checking replacement functions ... OK * checking foreign function calls ... OK * checking R code for possible problems ... OK * checking Rd files ... OK * checking Rd metadata ... OK * checking Rd cross-references ... OK * checking for missing documentation entries ... OK * checking for code/documentation mismatches ... OK * checking Rd \usage sections ... OK * checking Rd contents ... OK * checking for unstated dependencies in examples ... OK * checking contents of 'data' directory ... OK * checking data for non-ASCII characters ... OK * checking LazyData ... OK * checking data for ASCII and uncompressed saves ... OK * checking installed files from 'inst/doc' ... OK * checking files in 'vignettes' ... OK * checking examples ... OK Examples with CPU (user + system) or elapsed time > 5s user system elapsed regression_utility 4.553 0.043 5.842 * checking for unstated dependencies in 'tests' ... OK * checking tests ... Running 'testthat.R' ERROR Running the tests in 'tests/testthat.R' failed. Complete output: > # https://github.com/Rdatatable/data.table/issues/5658 > Sys.setenv("OMP_THREAD_LIMIT" = 2) > Sys.setenv("Ncpu" = 2) > > library(testthat) > library(rBiasCorrection) > > local_edition(3) > > test_check("rBiasCorrection") [20250519_003523.]: Entered 'clean_dt'-Function [20250519_003523.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_003523.]: got experimental data [20250519_003523.]: Entered 'clean_dt'-Function [20250519_003523.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_003523.]: got calibration data [20250519_003523.]: ### Starting with regression calculations ### [20250519_003523.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_003523.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_003523.]: Logging df_agg: CpG#1 [20250519_003523.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003523.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_003523.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_003523.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_003523.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_003523.]: Entered 'hyperbolic_regression'-Function [20250519_003523.]: 'hyperbolic_regression': minmax = FALSE [20250519_003523.]: Entered 'cubic_regression'-Function [20250519_003523.]: 'cubic_regression': minmax = FALSE [20250519_003523.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_003523.]: Logging df_agg: CpG#2 [20250519_003523.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003523.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_003523.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_003523.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_003523.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_003523.]: Entered 'hyperbolic_regression'-Function [20250519_003523.]: 'hyperbolic_regression': minmax = FALSE [20250519_003523.]: Entered 'cubic_regression'-Function [20250519_003523.]: 'cubic_regression': minmax = FALSE [20250519_003524.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_003524.]: Logging df_agg: CpG#3 [20250519_003524.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003524.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_003524.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_003524.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_003524.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_003524.]: Entered 'hyperbolic_regression'-Function [20250519_003524.]: 'hyperbolic_regression': minmax = FALSE [20250519_003524.]: Entered 'cubic_regression'-Function [20250519_003524.]: 'cubic_regression': minmax = FALSE [20250519_003524.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_003524.]: Logging df_agg: CpG#4 [20250519_003524.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003524.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_003524.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_003524.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_003524.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_003524.]: Entered 'hyperbolic_regression'-Function [20250519_003524.]: 'hyperbolic_regression': minmax = FALSE [20250519_003524.]: Entered 'cubic_regression'-Function [20250519_003524.]: 'cubic_regression': minmax = FALSE [20250519_003524.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_003524.]: Logging df_agg: CpG#5 [20250519_003524.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003524.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_003524.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_003524.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_003524.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_003524.]: Entered 'hyperbolic_regression'-Function [20250519_003524.]: 'hyperbolic_regression': minmax = FALSE [20250519_003524.]: Entered 'cubic_regression'-Function [20250519_003524.]: 'cubic_regression': minmax = FALSE [20250519_003524.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_003524.]: Logging df_agg: CpG#6 [20250519_003524.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003524.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_003524.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_003524.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_003524.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_003524.]: Entered 'hyperbolic_regression'-Function [20250519_003524.]: 'hyperbolic_regression': minmax = FALSE [20250519_003525.]: Entered 'cubic_regression'-Function [20250519_003525.]: 'cubic_regression': minmax = FALSE [20250519_003525.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_003525.]: Logging df_agg: CpG#7 [20250519_003525.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003525.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_003525.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_003525.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_003525.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_003525.]: Entered 'hyperbolic_regression'-Function [20250519_003525.]: 'hyperbolic_regression': minmax = FALSE [20250519_003525.]: Entered 'cubic_regression'-Function [20250519_003525.]: 'cubic_regression': minmax = FALSE [20250519_003525.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_003525.]: Logging df_agg: CpG#8 [20250519_003525.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003525.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_003525.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_003525.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_003525.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_003525.]: Entered 'hyperbolic_regression'-Function [20250519_003525.]: 'hyperbolic_regression': minmax = FALSE [20250519_003525.]: Entered 'cubic_regression'-Function [20250519_003525.]: 'cubic_regression': minmax = FALSE [20250519_003525.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_003525.]: Logging df_agg: CpG#9 [20250519_003525.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003525.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_003525.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_003525.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_003525.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_003525.]: Entered 'hyperbolic_regression'-Function [20250519_003525.]: 'hyperbolic_regression': minmax = FALSE [20250519_003525.]: Entered 'cubic_regression'-Function [20250519_003525.]: 'cubic_regression': minmax = FALSE [20250519_003525.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_003525.]: Logging df_agg: row_means [20250519_003525.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003525.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_003525.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_003525.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_003525.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_003525.]: Entered 'hyperbolic_regression'-Function [20250519_003525.]: 'hyperbolic_regression': minmax = FALSE [20250519_003526.]: Entered 'cubic_regression'-Function [20250519_003526.]: 'cubic_regression': minmax = FALSE [20250519_003526.]: ### Starting with regression calculations ### [20250519_003526.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_003527.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_003527.]: Logging df_agg: CpG#1 [20250519_003527.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003527.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_003527.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_003527.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_003527.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_003527.]: Entered 'hyperbolic_regression'-Function [20250519_003527.]: 'hyperbolic_regression': minmax = FALSE [20250519_003527.]: Entered 'cubic_regression'-Function [20250519_003527.]: 'cubic_regression': minmax = FALSE [20250519_003527.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_003527.]: Logging df_agg: CpG#2 [20250519_003527.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003527.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_003527.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_003527.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_003527.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_003527.]: Entered 'hyperbolic_regression'-Function [20250519_003527.]: 'hyperbolic_regression': minmax = FALSE [20250519_003527.]: Entered 'cubic_regression'-Function [20250519_003527.]: 'cubic_regression': minmax = FALSE [20250519_003527.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_003527.]: Logging df_agg: CpG#3 [20250519_003527.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003527.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_003527.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_003527.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_003527.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_003527.]: Entered 'hyperbolic_regression'-Function [20250519_003527.]: 'hyperbolic_regression': minmax = FALSE [20250519_003527.]: Entered 'cubic_regression'-Function [20250519_003527.]: 'cubic_regression': minmax = FALSE [20250519_003527.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_003527.]: Logging df_agg: CpG#4 [20250519_003527.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003527.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_003527.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_003527.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_003527.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_003527.]: Entered 'hyperbolic_regression'-Function [20250519_003527.]: 'hyperbolic_regression': minmax = FALSE [20250519_003528.]: Entered 'cubic_regression'-Function [20250519_003528.]: 'cubic_regression': minmax = FALSE [20250519_003528.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_003528.]: Logging df_agg: CpG#5 [20250519_003528.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003528.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_003528.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_003528.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_003528.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_003528.]: Entered 'hyperbolic_regression'-Function [20250519_003528.]: 'hyperbolic_regression': minmax = FALSE [20250519_003528.]: Entered 'cubic_regression'-Function [20250519_003528.]: 'cubic_regression': minmax = FALSE [20250519_003528.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_003528.]: Logging df_agg: CpG#6 [20250519_003528.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003528.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_003528.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_003528.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_003528.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_003528.]: Entered 'hyperbolic_regression'-Function [20250519_003528.]: 'hyperbolic_regression': minmax = FALSE [20250519_003528.]: Entered 'cubic_regression'-Function [20250519_003528.]: 'cubic_regression': minmax = FALSE [20250519_003528.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_003528.]: Logging df_agg: CpG#7 [20250519_003528.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003528.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_003528.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_003528.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_003528.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_003528.]: Entered 'hyperbolic_regression'-Function [20250519_003528.]: 'hyperbolic_regression': minmax = FALSE [20250519_003528.]: Entered 'cubic_regression'-Function [20250519_003528.]: 'cubic_regression': minmax = FALSE [20250519_003528.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_003528.]: Logging df_agg: CpG#8 [20250519_003528.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003528.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_003528.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_003528.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_003528.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_003528.]: Entered 'hyperbolic_regression'-Function [20250519_003528.]: 'hyperbolic_regression': minmax = FALSE [20250519_003529.]: Entered 'cubic_regression'-Function [20250519_003529.]: 'cubic_regression': minmax = FALSE [20250519_003529.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_003529.]: Logging df_agg: CpG#9 [20250519_003529.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003529.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_003529.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_003529.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_003529.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_003529.]: Entered 'hyperbolic_regression'-Function [20250519_003529.]: 'hyperbolic_regression': minmax = FALSE [20250519_003529.]: Entered 'cubic_regression'-Function [20250519_003529.]: 'cubic_regression': minmax = FALSE [20250519_003529.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_003529.]: Logging df_agg: row_means [20250519_003529.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003529.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_003529.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_003529.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_003529.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_003529.]: Entered 'hyperbolic_regression'-Function [20250519_003529.]: 'hyperbolic_regression': minmax = FALSE [20250519_003529.]: Entered 'cubic_regression'-Function [20250519_003529.]: 'cubic_regression': minmax = FALSE [20250519_003531.]: ### Starting with regression calculations ### [20250519_003531.]: Entered 'regression_type1'-Function [20250519_003531.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_003531.]: Logging df_agg: CpG#1 [20250519_003531.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003531.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_003531.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_003531.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_003531.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_003531.]: Entered 'hyperbolic_regression'-Function [20250519_003531.]: 'hyperbolic_regression': minmax = FALSE [20250519_003531.]: Entered 'cubic_regression'-Function [20250519_003531.]: 'cubic_regression': minmax = FALSE [20250519_003531.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_003531.]: Logging df_agg: CpG#2 [20250519_003531.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003531.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_003531.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_003531.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_003531.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_003531.]: Entered 'hyperbolic_regression'-Function [20250519_003531.]: 'hyperbolic_regression': minmax = FALSE [20250519_003531.]: Entered 'cubic_regression'-Function [20250519_003531.]: 'cubic_regression': minmax = FALSE [20250519_003531.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_003531.]: Logging df_agg: CpG#3 [20250519_003531.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003531.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_003531.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_003531.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_003531.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_003531.]: Entered 'hyperbolic_regression'-Function [20250519_003531.]: 'hyperbolic_regression': minmax = FALSE [20250519_003531.]: Entered 'cubic_regression'-Function [20250519_003531.]: 'cubic_regression': minmax = FALSE [20250519_003531.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_003531.]: Logging df_agg: CpG#4 [20250519_003531.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003531.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_003531.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_003531.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_003531.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_003531.]: Entered 'hyperbolic_regression'-Function [20250519_003531.]: 'hyperbolic_regression': minmax = FALSE [20250519_003531.]: Entered 'cubic_regression'-Function [20250519_003531.]: 'cubic_regression': minmax = FALSE [20250519_003531.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_003531.]: Logging df_agg: CpG#5 [20250519_003531.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003531.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_003531.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_003531.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_003531.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_003531.]: Entered 'hyperbolic_regression'-Function [20250519_003531.]: 'hyperbolic_regression': minmax = FALSE [20250519_003531.]: Entered 'cubic_regression'-Function [20250519_003531.]: 'cubic_regression': minmax = FALSE [20250519_003531.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_003531.]: Logging df_agg: CpG#6 [20250519_003531.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003531.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_003531.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_003531.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_003531.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_003531.]: Entered 'hyperbolic_regression'-Function [20250519_003531.]: 'hyperbolic_regression': minmax = FALSE [20250519_003532.]: Entered 'cubic_regression'-Function [20250519_003532.]: 'cubic_regression': minmax = FALSE [20250519_003532.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_003532.]: Logging df_agg: CpG#7 [20250519_003532.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003532.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_003532.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_003532.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_003532.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_003532.]: Entered 'hyperbolic_regression'-Function [20250519_003532.]: 'hyperbolic_regression': minmax = FALSE [20250519_003532.]: Entered 'cubic_regression'-Function [20250519_003532.]: 'cubic_regression': minmax = FALSE [20250519_003532.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_003532.]: Logging df_agg: CpG#8 [20250519_003532.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003532.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_003532.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_003532.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_003532.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_003532.]: Entered 'hyperbolic_regression'-Function [20250519_003532.]: 'hyperbolic_regression': minmax = FALSE [20250519_003532.]: Entered 'cubic_regression'-Function [20250519_003532.]: 'cubic_regression': minmax = FALSE [20250519_003532.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_003532.]: Logging df_agg: CpG#9 [20250519_003532.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003532.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_003532.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_003532.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_003532.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_003532.]: Entered 'hyperbolic_regression'-Function [20250519_003532.]: 'hyperbolic_regression': minmax = FALSE [20250519_003532.]: Entered 'cubic_regression'-Function [20250519_003532.]: 'cubic_regression': minmax = FALSE [20250519_003532.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_003532.]: Logging df_agg: row_means [20250519_003532.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003532.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_003532.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_003532.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_003532.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_003532.]: Entered 'hyperbolic_regression'-Function [20250519_003532.]: 'hyperbolic_regression': minmax = FALSE [20250519_003532.]: Entered 'cubic_regression'-Function [20250519_003532.]: 'cubic_regression': minmax = FALSE [20250519_003534.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_003534.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_003534.]: Logging df_agg: CpG#1 [20250519_003534.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003534.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_003534.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_003534.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_003534.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_003534.]: Entered 'hyperbolic_regression'-Function [20250519_003534.]: 'hyperbolic_regression': minmax = FALSE [20250519_003534.]: Entered 'cubic_regression'-Function [20250519_003534.]: 'cubic_regression': minmax = FALSE [20250519_003534.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_003534.]: Logging df_agg: CpG#2 [20250519_003534.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003534.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_003534.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_003534.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_003534.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_003534.]: Entered 'hyperbolic_regression'-Function [20250519_003534.]: 'hyperbolic_regression': minmax = FALSE [20250519_003535.]: Entered 'cubic_regression'-Function [20250519_003535.]: 'cubic_regression': minmax = FALSE [20250519_003535.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_003535.]: Logging df_agg: CpG#3 [20250519_003535.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003535.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_003535.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_003535.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_003535.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_003535.]: Entered 'hyperbolic_regression'-Function [20250519_003535.]: 'hyperbolic_regression': minmax = FALSE [20250519_003535.]: Entered 'cubic_regression'-Function [20250519_003535.]: 'cubic_regression': minmax = FALSE [20250519_003535.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_003535.]: Logging df_agg: CpG#4 [20250519_003535.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003535.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_003535.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_003535.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_003535.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_003535.]: Entered 'hyperbolic_regression'-Function [20250519_003535.]: 'hyperbolic_regression': minmax = FALSE [20250519_003535.]: Entered 'cubic_regression'-Function [20250519_003535.]: 'cubic_regression': minmax = FALSE [20250519_003535.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_003535.]: Logging df_agg: CpG#5 [20250519_003535.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003535.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_003535.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_003535.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_003535.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_003535.]: Entered 'hyperbolic_regression'-Function [20250519_003535.]: 'hyperbolic_regression': minmax = FALSE [20250519_003535.]: Entered 'cubic_regression'-Function [20250519_003535.]: 'cubic_regression': minmax = FALSE [20250519_003535.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_003535.]: Logging df_agg: CpG#6 [20250519_003535.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003535.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_003535.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_003535.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_003535.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_003535.]: Entered 'hyperbolic_regression'-Function [20250519_003535.]: 'hyperbolic_regression': minmax = FALSE [20250519_003536.]: Entered 'cubic_regression'-Function [20250519_003536.]: 'cubic_regression': minmax = FALSE [20250519_003536.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_003536.]: Logging df_agg: CpG#7 [20250519_003536.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003536.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_003536.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_003536.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_003536.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_003536.]: Entered 'hyperbolic_regression'-Function [20250519_003536.]: 'hyperbolic_regression': minmax = FALSE [20250519_003536.]: Entered 'cubic_regression'-Function [20250519_003536.]: 'cubic_regression': minmax = FALSE [20250519_003536.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_003536.]: Logging df_agg: CpG#8 [20250519_003536.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003536.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_003536.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_003536.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_003536.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_003536.]: Entered 'hyperbolic_regression'-Function [20250519_003536.]: 'hyperbolic_regression': minmax = FALSE [20250519_003536.]: Entered 'cubic_regression'-Function [20250519_003536.]: 'cubic_regression': minmax = FALSE [20250519_003536.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_003536.]: Logging df_agg: CpG#9 [20250519_003536.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003536.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_003536.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_003536.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_003536.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_003536.]: Entered 'hyperbolic_regression'-Function [20250519_003536.]: 'hyperbolic_regression': minmax = FALSE [20250519_003537.]: Entered 'cubic_regression'-Function [20250519_003537.]: 'cubic_regression': minmax = FALSE [20250519_003537.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_003537.]: Logging df_agg: row_means [20250519_003537.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003537.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_003537.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_003537.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_003537.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_003537.]: Entered 'hyperbolic_regression'-Function [20250519_003537.]: 'hyperbolic_regression': minmax = FALSE [20250519_003537.]: Entered 'cubic_regression'-Function [20250519_003537.]: 'cubic_regression': minmax = FALSE [20250519_003538.]: Entered 'clean_dt'-Function [20250519_003538.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_003538.]: got experimental data [20250519_003538.]: Entered 'clean_dt'-Function [20250519_003538.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_003538.]: got calibration data [20250519_003538.]: ### Starting with regression calculations ### [20250519_003538.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_003538.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_003538.]: Logging df_agg: CpG#1 [20250519_003538.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003538.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_003538.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_003538.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_003538.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_003538.]: Entered 'hyperbolic_regression'-Function [20250519_003538.]: 'hyperbolic_regression': minmax = FALSE [20250519_003538.]: Entered 'cubic_regression'-Function [20250519_003538.]: 'cubic_regression': minmax = FALSE [20250519_003538.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_003538.]: Logging df_agg: CpG#2 [20250519_003538.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003538.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_003538.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_003538.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_003538.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_003538.]: Entered 'hyperbolic_regression'-Function [20250519_003538.]: 'hyperbolic_regression': minmax = FALSE [20250519_003538.]: Entered 'cubic_regression'-Function [20250519_003538.]: 'cubic_regression': minmax = FALSE [20250519_003538.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_003538.]: Logging df_agg: CpG#3 [20250519_003538.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003538.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_003538.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_003538.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_003538.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_003538.]: Entered 'hyperbolic_regression'-Function [20250519_003538.]: 'hyperbolic_regression': minmax = FALSE [20250519_003538.]: Entered 'cubic_regression'-Function [20250519_003538.]: 'cubic_regression': minmax = FALSE [20250519_003538.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_003538.]: Logging df_agg: CpG#4 [20250519_003538.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003538.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_003538.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_003538.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_003538.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_003538.]: Entered 'hyperbolic_regression'-Function [20250519_003538.]: 'hyperbolic_regression': minmax = FALSE [20250519_003539.]: Entered 'cubic_regression'-Function [20250519_003539.]: 'cubic_regression': minmax = FALSE [20250519_003539.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_003539.]: Logging df_agg: CpG#5 [20250519_003539.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003539.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_003539.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_003539.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_003539.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_003539.]: Entered 'hyperbolic_regression'-Function [20250519_003539.]: 'hyperbolic_regression': minmax = FALSE [20250519_003539.]: Entered 'cubic_regression'-Function [20250519_003539.]: 'cubic_regression': minmax = FALSE [20250519_003539.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_003539.]: Logging df_agg: CpG#6 [20250519_003539.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003539.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_003539.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_003539.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_003539.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_003539.]: Entered 'hyperbolic_regression'-Function [20250519_003539.]: 'hyperbolic_regression': minmax = FALSE [20250519_003539.]: Entered 'cubic_regression'-Function [20250519_003539.]: 'cubic_regression': minmax = FALSE [20250519_003539.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_003539.]: Logging df_agg: CpG#7 [20250519_003539.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003539.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_003539.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_003539.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_003539.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_003539.]: Entered 'hyperbolic_regression'-Function [20250519_003539.]: 'hyperbolic_regression': minmax = FALSE [20250519_003539.]: Entered 'cubic_regression'-Function [20250519_003539.]: 'cubic_regression': minmax = FALSE [20250519_003539.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_003539.]: Logging df_agg: CpG#8 [20250519_003539.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003539.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_003539.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_003539.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_003539.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_003539.]: Entered 'hyperbolic_regression'-Function [20250519_003539.]: 'hyperbolic_regression': minmax = FALSE [20250519_003540.]: Entered 'cubic_regression'-Function [20250519_003540.]: 'cubic_regression': minmax = FALSE [20250519_003540.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_003540.]: Logging df_agg: CpG#9 [20250519_003540.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003540.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_003540.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_003540.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_003540.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_003540.]: Entered 'hyperbolic_regression'-Function [20250519_003540.]: 'hyperbolic_regression': minmax = FALSE [20250519_003540.]: Entered 'cubic_regression'-Function [20250519_003540.]: 'cubic_regression': minmax = FALSE [20250519_003540.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_003540.]: Logging df_agg: row_means [20250519_003540.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003540.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_003540.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_003540.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_003540.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_003540.]: Entered 'hyperbolic_regression'-Function [20250519_003540.]: 'hyperbolic_regression': minmax = FALSE [20250519_003540.]: Entered 'cubic_regression'-Function [20250519_003540.]: 'cubic_regression': minmax = FALSE [20250519_003541.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_003541.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_003541.]: Logging df_agg: CpG#1 [20250519_003541.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003541.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_003541.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_003541.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_003541.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_003541.]: Entered 'hyperbolic_regression'-Function [20250519_003541.]: 'hyperbolic_regression': minmax = FALSE [20250519_003541.]: Entered 'cubic_regression'-Function [20250519_003541.]: 'cubic_regression': minmax = FALSE [20250519_003541.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_003541.]: Logging df_agg: CpG#2 [20250519_003541.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003541.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_003541.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_003541.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_003541.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_003541.]: Entered 'hyperbolic_regression'-Function [20250519_003541.]: 'hyperbolic_regression': minmax = FALSE [20250519_003541.]: Entered 'cubic_regression'-Function [20250519_003541.]: 'cubic_regression': minmax = FALSE [20250519_003541.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_003541.]: Logging df_agg: CpG#3 [20250519_003541.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003541.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_003541.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_003541.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_003541.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_003541.]: Entered 'hyperbolic_regression'-Function [20250519_003541.]: 'hyperbolic_regression': minmax = FALSE [20250519_003542.]: Entered 'cubic_regression'-Function [20250519_003542.]: 'cubic_regression': minmax = FALSE [20250519_003542.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_003542.]: Logging df_agg: CpG#4 [20250519_003542.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003542.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_003542.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_003542.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_003542.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_003542.]: Entered 'hyperbolic_regression'-Function [20250519_003542.]: 'hyperbolic_regression': minmax = FALSE [20250519_003542.]: Entered 'cubic_regression'-Function [20250519_003542.]: 'cubic_regression': minmax = FALSE [20250519_003542.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_003542.]: Logging df_agg: CpG#5 [20250519_003542.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003542.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_003542.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_003542.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_003542.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_003542.]: Entered 'hyperbolic_regression'-Function [20250519_003542.]: 'hyperbolic_regression': minmax = FALSE [20250519_003542.]: Entered 'cubic_regression'-Function [20250519_003542.]: 'cubic_regression': minmax = FALSE [20250519_003542.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_003542.]: Logging df_agg: CpG#6 [20250519_003542.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003542.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_003542.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_003542.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_003542.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_003542.]: Entered 'hyperbolic_regression'-Function [20250519_003542.]: 'hyperbolic_regression': minmax = FALSE [20250519_003542.]: Entered 'cubic_regression'-Function [20250519_003542.]: 'cubic_regression': minmax = FALSE [20250519_003542.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_003542.]: Logging df_agg: CpG#7 [20250519_003542.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003542.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_003542.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_003542.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_003542.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_003542.]: Entered 'hyperbolic_regression'-Function [20250519_003542.]: 'hyperbolic_regression': minmax = FALSE [20250519_003543.]: Entered 'cubic_regression'-Function [20250519_003543.]: 'cubic_regression': minmax = FALSE [20250519_003543.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_003543.]: Logging df_agg: CpG#8 [20250519_003543.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003543.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_003543.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_003543.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_003543.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_003543.]: Entered 'hyperbolic_regression'-Function [20250519_003543.]: 'hyperbolic_regression': minmax = FALSE [20250519_003543.]: Entered 'cubic_regression'-Function [20250519_003543.]: 'cubic_regression': minmax = FALSE [20250519_003543.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_003543.]: Logging df_agg: CpG#9 [20250519_003543.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003543.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_003543.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_003543.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_003543.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_003543.]: Entered 'hyperbolic_regression'-Function [20250519_003543.]: 'hyperbolic_regression': minmax = FALSE [20250519_003543.]: Entered 'cubic_regression'-Function [20250519_003543.]: 'cubic_regression': minmax = FALSE [20250519_003543.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_003543.]: Logging df_agg: row_means [20250519_003543.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003543.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_003543.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_003543.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_003543.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_003543.]: Entered 'hyperbolic_regression'-Function [20250519_003543.]: 'hyperbolic_regression': minmax = FALSE [20250519_003543.]: Entered 'cubic_regression'-Function [20250519_003543.]: 'cubic_regression': minmax = FALSE [20250519_003544.]: Entered 'solving_equations'-Function [20250519_003544.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: -2.23222837469517 [20250519_003544.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.232 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.1698391693126 [20250519_003544.]: Samplename: 12.5 Root: 12.17 --> Root in between the borders! Added to results. Hyperbolic solved: 24.4781729791561 [20250519_003544.]: Samplename: 25 Root: 24.478 --> Root in between the borders! Added to results. Hyperbolic solved: 38.1730159074047 [20250519_003544.]: Samplename: 37.5 Root: 38.173 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3348987967717 [20250519_003544.]: Samplename: 50 Root: 52.335 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4582305966058 [20250519_003544.]: Samplename: 62.5 Root: 65.458 --> Root in between the borders! Added to results. Hyperbolic solved: 75.0090269722885 [20250519_003544.]: Samplename: 75 Root: 75.009 --> Root in between the borders! Added to results. Hyperbolic solved: 81.527135738891 [20250519_003544.]: Samplename: 87.5 Root: 81.527 --> Root in between the borders! Added to results. Hyperbolic solved: 102.400825350044 [20250519_003544.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.401 --> '100 < root < 110' --> substitute 100 [20250519_003544.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 1.13663087266051 [20250519_003544.]: Samplename: 0 Root: 1.137 --> Root in between the borders! Added to results. Hyperbolic solved: 11.4130277963576 [20250519_003544.]: Samplename: 12.5 Root: 11.413 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1741025868157 [20250519_003544.]: Samplename: 25 Root: 26.174 --> Root in between the borders! Added to results. Hyperbolic solved: 35.1051721736724 [20250519_003544.]: Samplename: 37.5 Root: 35.105 --> Root in between the borders! Added to results. Hyperbolic solved: 47.6856613240114 [20250519_003544.]: Samplename: 50 Root: 47.686 --> Root in between the borders! Added to results. Hyperbolic solved: 67.1442585354696 [20250519_003544.]: Samplename: 62.5 Root: 67.144 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7646957109439 [20250519_003544.]: Samplename: 75 Root: 75.765 --> Root in between the borders! Added to results. Hyperbolic solved: 84.4056634834815 [20250519_003544.]: Samplename: 87.5 Root: 84.406 --> Root in between the borders! Added to results. Hyperbolic solved: 100.94855337669 [20250519_003544.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.949 --> '100 < root < 110' --> substitute 100 [20250519_003544.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 0.512348257854635 [20250519_003544.]: Samplename: 0 Root: 0.512 --> Root in between the borders! Added to results. Hyperbolic solved: 10.7523476241811 [20250519_003544.]: Samplename: 12.5 Root: 10.752 --> Root in between the borders! Added to results. Hyperbolic solved: 25.5218062516799 [20250519_003544.]: Samplename: 25 Root: 25.522 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5269315729339 [20250519_003544.]: Samplename: 37.5 Root: 36.527 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7907738274884 [20250519_003544.]: Samplename: 50 Root: 50.791 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8684489988589 [20250519_003544.]: Samplename: 62.5 Root: 64.868 --> Root in between the borders! Added to results. Hyperbolic solved: 77.5522101073979 [20250519_003544.]: Samplename: 75 Root: 77.552 --> Root in between the borders! Added to results. Hyperbolic solved: 80.4372474691229 [20250519_003544.]: Samplename: 87.5 Root: 80.437 --> Root in between the borders! Added to results. Hyperbolic solved: 102.703772810881 [20250519_003544.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.704 --> '100 < root < 110' --> substitute 100 [20250519_003544.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: -0.519513573026794 [20250519_003544.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.52 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.4933760051342 [20250519_003544.]: Samplename: 12.5 Root: 12.493 --> Root in between the borders! Added to results. Hyperbolic solved: 24.2684789512555 [20250519_003544.]: Samplename: 25 Root: 24.268 --> Root in between the borders! Added to results. Hyperbolic solved: 38.0816229393195 [20250519_003544.]: Samplename: 37.5 Root: 38.082 --> Root in between the borders! Added to results. Hyperbolic solved: 48.5842089589187 [20250519_003544.]: Samplename: 50 Root: 48.584 --> Root in between the borders! Added to results. Hyperbolic solved: 67.6720983549562 [20250519_003544.]: Samplename: 62.5 Root: 67.672 --> Root in between the borders! Added to results. Hyperbolic solved: 74.1547759694059 [20250519_003544.]: Samplename: 75 Root: 74.155 --> Root in between the borders! Added to results. Hyperbolic solved: 82.8820147905386 [20250519_003544.]: Samplename: 87.5 Root: 82.882 --> Root in between the borders! Added to results. Hyperbolic solved: 102.078935346876 [20250519_003544.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.079 --> '100 < root < 110' --> substitute 100 [20250519_003544.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 2.41558040143479 [20250519_003544.]: Samplename: 0 Root: 2.416 --> Root in between the borders! Added to results. Hyperbolic solved: 10.1649584830834 [20250519_003544.]: Samplename: 12.5 Root: 10.165 --> Root in between the borders! Added to results. Hyperbolic solved: 23.9830670020905 [20250519_003544.]: Samplename: 25 Root: 23.983 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2773406385708 [20250519_003544.]: Samplename: 37.5 Root: 37.277 --> Root in between the borders! Added to results. Hyperbolic solved: 50.8659103346102 [20250519_003544.]: Samplename: 50 Root: 50.866 --> Root in between the borders! Added to results. Hyperbolic solved: 62.4341926937382 [20250519_003544.]: Samplename: 62.5 Root: 62.434 --> Root in between the borders! Added to results. Hyperbolic solved: 76.3914832329149 [20250519_003544.]: Samplename: 75 Root: 76.391 --> Root in between the borders! Added to results. Hyperbolic solved: 86.1597399215782 [20250519_003544.]: Samplename: 87.5 Root: 86.16 --> Root in between the borders! Added to results. Hyperbolic solved: 100.267701841999 [20250519_003544.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.268 --> '100 < root < 110' --> substitute 100 [20250519_003544.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 0.138179963459196 [20250519_003544.]: Samplename: 0 Root: 0.138 --> Root in between the borders! Added to results. Hyperbolic solved: 11.8635989635202 [20250519_003544.]: Samplename: 12.5 Root: 11.864 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5108198460409 [20250519_003544.]: Samplename: 25 Root: 26.511 --> Root in between the borders! Added to results. Hyperbolic solved: 35.3206004021833 [20250519_003544.]: Samplename: 37.5 Root: 35.321 --> Root in between the borders! Added to results. Hyperbolic solved: 50.0571987473308 [20250519_003544.]: Samplename: 50 Root: 50.057 --> Root in between the borders! Added to results. Hyperbolic solved: 64.9604436607063 [20250519_003544.]: Samplename: 62.5 Root: 64.96 --> Root in between the borders! Added to results. Hyperbolic solved: 73.6690699266294 [20250519_003544.]: Samplename: 75 Root: 73.669 --> Root in between the borders! Added to results. Hyperbolic solved: 87.1267946879604 [20250519_003544.]: Samplename: 87.5 Root: 87.127 --> Root in between the borders! Added to results. Hyperbolic solved: 100.261842767865 [20250519_003544.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.262 --> '100 < root < 110' --> substitute 100 [20250519_003544.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: -1.37239249181669 [20250519_003544.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -1.372 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.199366215418 [20250519_003544.]: Samplename: 12.5 Root: 10.199 --> Root in between the borders! Added to results. Hyperbolic solved: 24.595295361327 [20250519_003544.]: Samplename: 25 Root: 24.595 --> Root in between the borders! Added to results. Hyperbolic solved: 37.8312095633391 [20250519_003544.]: Samplename: 37.5 Root: 37.831 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5590896582852 [20250519_003544.]: Samplename: 50 Root: 53.559 --> Root in between the borders! Added to results. Hyperbolic solved: 65.9367389282431 [20250519_003544.]: Samplename: 62.5 Root: 65.937 --> Root in between the borders! Added to results. Hyperbolic solved: 75.736370152841 [20250519_003544.]: Samplename: 75 Root: 75.736 --> Root in between the borders! Added to results. Hyperbolic solved: 79.433089359524 [20250519_003544.]: Samplename: 87.5 Root: 79.433 --> Root in between the borders! Added to results. Hyperbolic solved: 103.004516225662 [20250519_003544.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 103.005 --> '100 < root < 110' --> substitute 100 [20250519_003544.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 2.80067549526643 [20250519_003544.]: Samplename: 0 Root: 2.801 --> Root in between the borders! Added to results. Hyperbolic solved: 9.27534686003087 [20250519_003544.]: Samplename: 12.5 Root: 9.275 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4762624089952 [20250519_003544.]: Samplename: 25 Root: 25.476 --> Root in between the borders! Added to results. Hyperbolic solved: 34.0122098045585 [20250519_003544.]: Samplename: 37.5 Root: 34.012 --> Root in between the borders! Added to results. Hyperbolic solved: 51.784270967861 [20250519_003544.]: Samplename: 50 Root: 51.784 --> Root in between the borders! Added to results. Hyperbolic solved: 64.6732380258789 [20250519_003544.]: Samplename: 62.5 Root: 64.673 --> Root in between the borders! Added to results. Hyperbolic solved: 78.4327054483192 [20250519_003544.]: Samplename: 75 Root: 78.433 --> Root in between the borders! Added to results. Hyperbolic solved: 81.3427308951795 [20250519_003544.]: Samplename: 87.5 Root: 81.343 --> Root in between the borders! Added to results. Hyperbolic solved: 101.964413523995 [20250519_003544.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.964 --> '100 < root < 110' --> substitute 100 [20250519_003544.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: -2.13402763663736 [20250519_003544.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.134 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.5081558897243 [20250519_003544.]: Samplename: 12.5 Root: 10.508 --> Root in between the borders! Added to results. Hyperbolic solved: 26.9163121098627 [20250519_003544.]: Samplename: 25 Root: 26.916 --> Root in between the borders! Added to results. Hyperbolic solved: 36.8332915301825 [20250519_003544.]: Samplename: 37.5 Root: 36.833 --> Root in between the borders! Added to results. Hyperbolic solved: 52.00955 [20250519_003544.]: Samplename: 50 Root: 52.01 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8927781893359 [20250519_003544.]: Samplename: 62.5 Root: 64.893 --> Root in between the borders! Added to results. Hyperbolic solved: 74.566810799042 [20250519_003544.]: Samplename: 75 Root: 74.567 --> Root in between the borders! Added to results. Hyperbolic solved: 84.5291854856128 [20250519_003544.]: Samplename: 87.5 Root: 84.529 --> Root in between the borders! Added to results. Hyperbolic solved: 101.046823491232 [20250519_003544.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.047 --> '100 < root < 110' --> substitute 100 [20250519_003544.]: Solving hyperbolic regression for row_means Hyperbolic solved: 0.290936038655697 [20250519_003544.]: Samplename: 0 Root: 0.291 --> Root in between the borders! Added to results. Hyperbolic solved: 11.0412364555656 [20250519_003544.]: Samplename: 12.5 Root: 11.041 --> Root in between the borders! Added to results. Hyperbolic solved: 25.408146358228 [20250519_003544.]: Samplename: 25 Root: 25.408 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5243683648753 [20250519_003544.]: Samplename: 37.5 Root: 36.524 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7348788337891 [20250519_003544.]: Samplename: 50 Root: 50.735 --> Root in between the borders! Added to results. Hyperbolic solved: 65.3135169308955 [20250519_003544.]: Samplename: 62.5 Root: 65.314 --> Root in between the borders! Added to results. Hyperbolic solved: 75.5342663138126 [20250519_003544.]: Samplename: 75 Root: 75.534 --> Root in between the borders! Added to results. Hyperbolic solved: 83.2411177153911 [20250519_003544.]: Samplename: 87.5 Root: 83.241 --> Root in between the borders! Added to results. Hyperbolic solved: 101.666935931185 [20250519_003544.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.667 --> '100 < root < 110' --> substitute 100 [20250519_003544.]: ### Starting with regression calculations ### [20250519_003544.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_003545.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.1698391693126, 24.4781729791561, 38.1730159074047, 52.3348987967717, 65.4582305966058, 75.0090269722885, 81.527135738891, 100)c(0, 0.330160830687401, 0.521827020843901, 0.673015907404697, 2.3348987967717, 2.9582305966058, 0.00902697228849547, 5.972864261109, 0)c(NA, 2.64128664549921, 2.0873080833756, 1.79470908641252, 4.66979759354339, 4.73316895456928, 0.0120359630513273, 6.82613058412457, 0) [20250519_003545.]: Logging df_agg: CpG#1 [20250519_003545.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003545.]: c(0, 12.1698391693126, 24.4781729791561, 38.1730159074047, 52.3348987967717, 65.4582305966058, 75.0090269722885, 81.527135738891, 100)[20250519_003545.]: c(0, 0.330160830687401, 0.521827020843901, 0.673015907404697, 2.3348987967717, 2.9582305966058, 0.00902697228849547, 5.972864261109, 0)[20250519_003545.]: c(NA, 2.64128664549921, 2.0873080833756, 1.79470908641252, 4.66979759354339, 4.73316895456928, 0.0120359630513273, 6.82613058412457, 0) [20250519_003545.]: Entered 'hyperbolic_regression'-Function [20250519_003545.]: 'hyperbolic_regression': minmax = FALSE [20250519_003545.]: Entered 'cubic_regression'-Function [20250519_003545.]: 'cubic_regression': minmax = FALSE [20250519_003545.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.13663087266051, 11.4130277963576, 26.1741025868157, 35.1051721736724, 47.6856613240114, 67.1442585354696, 75.7646957109439, 84.4056634834815, 100)c(1.13663087266051, 1.0869722036424, 1.1741025868157, 2.3948278263276, 2.3143386759886, 4.6442585354696, 0.764695710943897, 3.0943365165185, 0)c(NA, 8.6957776291392, 4.6964103472628, 6.3862075368736, 4.6286773519772, 7.43081365675137, 1.01959428125853, 3.53638459030685, 0) [20250519_003545.]: Logging df_agg: CpG#2 [20250519_003545.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003545.]: c(1.13663087266051, 11.4130277963576, 26.1741025868157, 35.1051721736724, 47.6856613240114, 67.1442585354696, 75.7646957109439, 84.4056634834815, 100)[20250519_003545.]: c(1.13663087266051, 1.0869722036424, 1.1741025868157, 2.3948278263276, 2.3143386759886, 4.6442585354696, 0.764695710943897, 3.0943365165185, 0)[20250519_003545.]: c(NA, 8.6957776291392, 4.6964103472628, 6.3862075368736, 4.6286773519772, 7.43081365675137, 1.01959428125853, 3.53638459030685, 0) [20250519_003545.]: Entered 'hyperbolic_regression'-Function [20250519_003545.]: 'hyperbolic_regression': minmax = FALSE [20250519_003545.]: Entered 'cubic_regression'-Function [20250519_003545.]: 'cubic_regression': minmax = FALSE [20250519_003545.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.512348257854635, 10.7523476241811, 25.5218062516799, 36.5269315729339, 50.7907738274884, 64.8684489988589, 77.5522101073979, 80.4372474691229, 100)c(0.512348257854635, 1.7476523758189, 0.521806251679902, 0.973068427066103, 0.790773827488401, 2.3684489988589, 2.5522101073979, 7.06275253087711, 0)c(NA, 13.9812190065512, 2.08722500671961, 2.59484913884294, 1.5815476549768, 3.78951839817423, 3.40294680986386, 8.07171717814526, 0) [20250519_003545.]: Logging df_agg: CpG#3 [20250519_003545.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003545.]: c(0.512348257854635, 10.7523476241811, 25.5218062516799, 36.5269315729339, 50.7907738274884, 64.8684489988589, 77.5522101073979, 80.4372474691229, 100)[20250519_003545.]: c(0.512348257854635, 1.7476523758189, 0.521806251679902, 0.973068427066103, 0.790773827488401, 2.3684489988589, 2.5522101073979, 7.06275253087711, 0)[20250519_003545.]: c(NA, 13.9812190065512, 2.08722500671961, 2.59484913884294, 1.5815476549768, 3.78951839817423, 3.40294680986386, 8.07171717814526, 0) [20250519_003545.]: Entered 'hyperbolic_regression'-Function [20250519_003545.]: 'hyperbolic_regression': minmax = FALSE [20250519_003546.]: Entered 'cubic_regression'-Function [20250519_003546.]: 'cubic_regression': minmax = FALSE [20250519_003546.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.4933760051342, 24.2684789512555, 38.0816229393195, 48.5842089589187, 67.6720983549562, 74.1547759694059, 82.8820147905386, 100)c(0, 0.00662399486579979, 0.731521048744501, 0.581622939319502, 1.4157910410813, 5.1720983549562, 0.845224030594096, 4.61798520946139, 0)c(NA, 0.0529919589263983, 2.926084194978, 1.550994504852, 2.8315820821626, 8.27535736792993, 1.12696537412546, 5.27769738224159, 0) [20250519_003546.]: Logging df_agg: CpG#4 [20250519_003546.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003546.]: c(0, 12.4933760051342, 24.2684789512555, 38.0816229393195, 48.5842089589187, 67.6720983549562, 74.1547759694059, 82.8820147905386, 100)[20250519_003546.]: c(0, 0.00662399486579979, 0.731521048744501, 0.581622939319502, 1.4157910410813, 5.1720983549562, 0.845224030594096, 4.61798520946139, 0)[20250519_003546.]: c(NA, 0.0529919589263983, 2.926084194978, 1.550994504852, 2.8315820821626, 8.27535736792993, 1.12696537412546, 5.27769738224159, 0) [20250519_003546.]: Entered 'hyperbolic_regression'-Function [20250519_003546.]: 'hyperbolic_regression': minmax = FALSE [20250519_003546.]: Entered 'cubic_regression'-Function [20250519_003546.]: 'cubic_regression': minmax = FALSE [20250519_003546.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.41558040143479, 10.1649584830834, 23.9830670020905, 37.2773406385708, 50.8659103346102, 62.4341926937382, 76.3914832329149, 86.1597399215782, 100)c(2.41558040143479, 2.3350415169166, 1.0169329979095, 0.222659361429201, 0.865910334610199, 0.065807306261803, 1.39148323291489, 1.34026007842181, 0)c(NA, 18.6803321353328, 4.067731991638, 0.593758297144537, 1.7318206692204, 0.105291690018885, 1.85531097721986, 1.53172580391064, 0) [20250519_003546.]: Logging df_agg: CpG#5 [20250519_003546.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003546.]: c(2.41558040143479, 10.1649584830834, 23.9830670020905, 37.2773406385708, 50.8659103346102, 62.4341926937382, 76.3914832329149, 86.1597399215782, 100)[20250519_003546.]: c(2.41558040143479, 2.3350415169166, 1.0169329979095, 0.222659361429201, 0.865910334610199, 0.065807306261803, 1.39148323291489, 1.34026007842181, 0)[20250519_003546.]: c(NA, 18.6803321353328, 4.067731991638, 0.593758297144537, 1.7318206692204, 0.105291690018885, 1.85531097721986, 1.53172580391064, 0) [20250519_003546.]: Entered 'hyperbolic_regression'-Function [20250519_003546.]: 'hyperbolic_regression': minmax = FALSE [20250519_003546.]: Entered 'cubic_regression'-Function [20250519_003546.]: 'cubic_regression': minmax = FALSE [20250519_003546.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.138179963459196, 11.8635989635202, 26.5108198460409, 35.3206004021833, 50.0571987473308, 64.9604436607063, 73.6690699266294, 87.1267946879604, 100)c(0.138179963459196, 0.6364010364798, 1.5108198460409, 2.1793995978167, 0.0571987473308013, 2.4604436607063, 1.3309300733706, 0.373205312039602, 0)c(NA, 5.0912082918384, 6.0432793841636, 5.81173226084453, 0.114397494661603, 3.93670985713009, 1.77457343116079, 0.426520356616688, 0) [20250519_003546.]: Logging df_agg: CpG#6 [20250519_003546.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003546.]: c(0.138179963459196, 11.8635989635202, 26.5108198460409, 35.3206004021833, 50.0571987473308, 64.9604436607063, 73.6690699266294, 87.1267946879604, 100)[20250519_003546.]: c(0.138179963459196, 0.6364010364798, 1.5108198460409, 2.1793995978167, 0.0571987473308013, 2.4604436607063, 1.3309300733706, 0.373205312039602, 0)[20250519_003546.]: c(NA, 5.0912082918384, 6.0432793841636, 5.81173226084453, 0.114397494661603, 3.93670985713009, 1.77457343116079, 0.426520356616688, 0) [20250519_003546.]: Entered 'hyperbolic_regression'-Function [20250519_003546.]: 'hyperbolic_regression': minmax = FALSE [20250519_003546.]: Entered 'cubic_regression'-Function [20250519_003546.]: 'cubic_regression': minmax = FALSE [20250519_003546.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.199366215418, 24.595295361327, 37.8312095633391, 53.5590896582852, 65.9367389282431, 75.736370152841, 79.433089359524, 100)c(0, 2.300633784582, 0.404704638673, 0.331209563339101, 3.5590896582852, 3.4367389282431, 0.736370152841005, 8.066910640476, 0)c(NA, 18.405070276656, 1.618818554692, 0.883225502237603, 7.11817931657041, 5.49878228518896, 0.981826870454673, 9.21932644625828, 0) [20250519_003546.]: Logging df_agg: CpG#7 [20250519_003546.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003546.]: c(0, 10.199366215418, 24.595295361327, 37.8312095633391, 53.5590896582852, 65.9367389282431, 75.736370152841, 79.433089359524, 100)[20250519_003546.]: c(0, 2.300633784582, 0.404704638673, 0.331209563339101, 3.5590896582852, 3.4367389282431, 0.736370152841005, 8.066910640476, 0)[20250519_003546.]: c(NA, 18.405070276656, 1.618818554692, 0.883225502237603, 7.11817931657041, 5.49878228518896, 0.981826870454673, 9.21932644625828, 0) [20250519_003546.]: Entered 'hyperbolic_regression'-Function [20250519_003546.]: 'hyperbolic_regression': minmax = FALSE [20250519_003547.]: Entered 'cubic_regression'-Function [20250519_003547.]: 'cubic_regression': minmax = FALSE [20250519_003547.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.80067549526643, 9.27534686003087, 25.4762624089952, 34.0122098045585, 51.784270967861, 64.6732380258789, 78.4327054483192, 81.3427308951795, 100)c(2.80067549526643, 3.22465313996913, 0.476262408995201, 3.4877901954415, 1.784270967861, 2.1732380258789, 3.4327054483192, 6.1572691048205, 0)c(NA, 25.797225119753, 1.9050496359808, 9.30077385451066, 3.568541935722, 3.47718084140624, 4.57694059775893, 7.03687897693771, 0) [20250519_003547.]: Logging df_agg: CpG#8 [20250519_003547.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003547.]: c(2.80067549526643, 9.27534686003087, 25.4762624089952, 34.0122098045585, 51.784270967861, 64.6732380258789, 78.4327054483192, 81.3427308951795, 100)[20250519_003547.]: c(2.80067549526643, 3.22465313996913, 0.476262408995201, 3.4877901954415, 1.784270967861, 2.1732380258789, 3.4327054483192, 6.1572691048205, 0)[20250519_003547.]: c(NA, 25.797225119753, 1.9050496359808, 9.30077385451066, 3.568541935722, 3.47718084140624, 4.57694059775893, 7.03687897693771, 0) [20250519_003547.]: Entered 'hyperbolic_regression'-Function [20250519_003547.]: 'hyperbolic_regression': minmax = FALSE [20250519_003547.]: Entered 'cubic_regression'-Function [20250519_003547.]: 'cubic_regression': minmax = FALSE [20250519_003547.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.5081558897243, 26.9163121098627, 36.8332915301825, 52.00955, 64.8927781893359, 74.566810799042, 84.5291854856128, 100)c(0, 1.9918441102757, 1.9163121098627, 0.6667084698175, 2.00955, 2.3927781893359, 0.433189200957997, 2.9708145143872, 0)c(NA, 15.9347528822056, 7.66524843945081, 1.77788925284667, 4.01909999999999, 3.82844510293744, 0.57758560127733, 3.39521658787109, 0) [20250519_003547.]: Logging df_agg: CpG#9 [20250519_003547.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003547.]: c(0, 10.5081558897243, 26.9163121098627, 36.8332915301825, 52.00955, 64.8927781893359, 74.566810799042, 84.5291854856128, 100)[20250519_003547.]: c(0, 1.9918441102757, 1.9163121098627, 0.6667084698175, 2.00955, 2.3927781893359, 0.433189200957997, 2.9708145143872, 0)[20250519_003547.]: c(NA, 15.9347528822056, 7.66524843945081, 1.77788925284667, 4.01909999999999, 3.82844510293744, 0.57758560127733, 3.39521658787109, 0) [20250519_003547.]: Entered 'hyperbolic_regression'-Function [20250519_003547.]: 'hyperbolic_regression': minmax = FALSE [20250519_003547.]: Entered 'cubic_regression'-Function [20250519_003547.]: 'cubic_regression': minmax = FALSE [20250519_003547.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.290936038655697, 11.0412364555656, 25.408146358228, 36.5243683648753, 50.7348788337891, 65.3135169308955, 75.5342663138126, 83.2411177153911, 100)c(0.290936038655697, 1.4587635444344, 0.408146358227999, 0.975631635124699, 0.734878833789097, 2.8135169308955, 0.534266313812594, 4.2588822846089, 0)c(NA, 11.6701083554752, 1.63258543291199, 2.60168436033253, 1.46975766757819, 4.5016270894328, 0.712355085083459, 4.86729403955303, 0) [20250519_003547.]: Logging df_agg: row_means [20250519_003547.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003547.]: c(0.290936038655697, 11.0412364555656, 25.408146358228, 36.5243683648753, 50.7348788337891, 65.3135169308955, 75.5342663138126, 83.2411177153911, 100)[20250519_003547.]: c(0.290936038655697, 1.4587635444344, 0.408146358227999, 0.975631635124699, 0.734878833789097, 2.8135169308955, 0.534266313812594, 4.2588822846089, 0)[20250519_003547.]: c(NA, 11.6701083554752, 1.63258543291199, 2.60168436033253, 1.46975766757819, 4.5016270894328, 0.712355085083459, 4.86729403955303, 0) [20250519_003547.]: Entered 'hyperbolic_regression'-Function [20250519_003547.]: 'hyperbolic_regression': minmax = FALSE [20250519_003547.]: Entered 'cubic_regression'-Function [20250519_003547.]: 'cubic_regression': minmax = FALSE [20250519_003548.]: Entered 'solving_equations'-Function [20250519_003548.]: Solving cubic regression for CpG#1 Coefficients: -1.036Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20250519_003548.]: Samplename: 0 Root: 1.335 --> Root in between the borders! Added to results. Coefficients: -8.34133333333333Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20250519_003548.]: Samplename: 12.5 Root: 11.684 --> Root in between the borders! Added to results. Coefficients: -15.388Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20250519_003548.]: Samplename: 25 Root: 24.056 --> Root in between the borders! Added to results. Coefficients: -24.28Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20250519_003548.]: Samplename: 37.5 Root: 50.441 --> Root in between the borders! Added to results. Coefficients: -34.9005Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20250519_003548.]: Samplename: 50 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -46.354Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20250519_003548.]: Samplename: 62.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -55.893Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20250519_003548.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -63.098Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20250519_003548.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -91.046Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20250519_003548.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20250519_003548.]: Solving cubic regression for CpG#2 Coefficients: -0.283000000000001Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20250519_003548.]: Samplename: 0 Root: 0.548 --> Root in between the borders! Added to results. Coefficients: -6.33966666666667Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20250519_003548.]: Samplename: 12.5 Root: 11.535 --> Root in between the borders! Added to results. Coefficients: -15.939Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20250519_003548.]: Samplename: 25 Root: 26.774 --> Root in between the borders! Added to results. Coefficients: -22.337Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20250519_003548.]: Samplename: 37.5 Root: 35.875 --> Root in between the borders! Added to results. Coefficients: -32.228Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20250519_003548.]: Samplename: 50 Root: 48.74 --> Root in between the borders! Added to results. Coefficients: -49.963Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20250519_003548.]: Samplename: 62.5 Root: 69.156 --> Root in between the borders! Added to results. Coefficients: -58.9655Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20250519_003548.]: Samplename: 75 Root: 78.552 --> Root in between the borders! Added to results. Coefficients: -68.8363333333333Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20250519_003548.]: Samplename: 87.5 Root: 88.272 --> Root in between the borders! Added to results. Coefficients: -90.577Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20250519_003548.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 107.97 --> '100 < root < 110' --> substitute 100 [20250519_003548.]: Solving cubic regression for CpG#3 Coefficients: -0.903Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20250519_003548.]: Samplename: 0 Root: 1.441 --> Root in between the borders! Added to results. Coefficients: -6.573Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20250519_003548.]: Samplename: 12.5 Root: 10.647 --> Root in between the borders! Added to results. Coefficients: -15.429Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20250519_003548.]: Samplename: 25 Root: 25.613 --> Root in between the borders! Added to results. Coefficients: -22.613Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20250519_003548.]: Samplename: 37.5 Root: 38.35 --> Root in between the borders! Added to results. Coefficients: -32.7755Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20250519_003548.]: Samplename: 50 Root: 57.445 --> Root in between the borders! Added to results. Coefficients: -43.889Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20250519_003548.]: Samplename: 62.5 Root: 80.105 --> Root in between the borders! Added to results. Coefficients: -54.9755Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20250519_003548.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 105.145 --> '100 < root < 110' --> substitute 100 Coefficients: -57.6563333333333Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20250519_003548.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -80.665Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20250519_003548.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20250519_003548.]: Solving cubic regression for CpG#4 Coefficients: -0.597Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20250519_003548.]: Samplename: 0 Root: 0.859 --> Root in between the borders! Added to results. Coefficients: -8.25233333333333Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20250519_003548.]: Samplename: 12.5 Root: 12.272 --> Root in between the borders! Added to results. Coefficients: -15.803Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20250519_003548.]: Samplename: 25 Root: 24.378 --> Root in between the borders! Added to results. Coefficients: -25.527Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20250519_003548.]: Samplename: 37.5 Root: 41.587 --> Root in between the borders! Added to results. Coefficients: -33.6365Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20250519_003548.]: Samplename: 50 Root: 57.868 --> Root in between the borders! Added to results. Coefficients: -50.255Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20250519_003548.]: Samplename: 62.5 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.893 --> '100 < root < 110' --> substitute 100 Coefficients: -56.539Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20250519_003548.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -65.5923333333333Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20250519_003548.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -88.321Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20250519_003548.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20250519_003548.]: Solving cubic regression for CpG#5 Coefficients: -0.624000000000001Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20250519_003548.]: Samplename: 0 Root: 1.459 --> Root in between the borders! Added to results. Coefficients: -4.768Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20250519_003548.]: Samplename: 12.5 Root: 10.294 --> Root in between the borders! Added to results. Coefficients: -12.726Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20250519_003548.]: Samplename: 25 Root: 24.477 --> Root in between the borders! Added to results. Coefficients: -21.16Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20250519_003548.]: Samplename: 37.5 Root: 37.097 --> Root in between the borders! Added to results. Coefficients: -30.6955Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20250519_003548.]: Samplename: 50 Root: 49.507 --> Root in between the borders! Added to results. Coefficients: -39.658Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20250519_003548.]: Samplename: 62.5 Root: 59.931 --> Root in between the borders! Added to results. Coefficients: -51.683Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20250519_003548.]: Samplename: 75 Root: 72.563 --> Root in between the borders! Added to results. Coefficients: -61.0146666666667Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20250519_003548.]: Samplename: 87.5 Root: 81.549 --> Root in between the borders! Added to results. Coefficients: -76.07Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20250519_003548.]: Samplename: 100 Root: 94.898 --> Root in between the borders! Added to results. [20250519_003548.]: Solving cubic regression for CpG#6 Coefficients: -0.196000000000001Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20250519_003548.]: Samplename: 0 Root: 0.349 --> Root in between the borders! Added to results. Coefficients: -6.73866666666667Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20250519_003548.]: Samplename: 12.5 Root: 11.765 --> Root in between the borders! Added to results. Coefficients: -15.888Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20250519_003548.]: Samplename: 25 Root: 27.02 --> Root in between the borders! Added to results. Coefficients: -22Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20250519_003548.]: Samplename: 37.5 Root: 36.802 --> Root in between the borders! Added to results. Coefficients: -33.4445Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20250519_003548.]: Samplename: 50 Root: 54.35 --> Root in between the borders! Added to results. Coefficients: -46.9Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20250519_003548.]: Samplename: 62.5 Root: 73.873 --> Root in between the borders! Added to results. Coefficients: -55.832Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20250519_003548.]: Samplename: 75 Root: 86.259 --> Root in between the borders! Added to results. Coefficients: -71.5453333333333Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20250519_003548.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 107.089 --> '100 < root < 110' --> substitute 100 Coefficients: -89.656Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20250519_003548.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20250519_003548.]: Solving cubic regression for CpG#7 Coefficients: -1.215Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20250519_003548.]: Samplename: 0 Root: 2.126 --> Root in between the borders! Added to results. Coefficients: -5.39566666666667Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20250519_003548.]: Samplename: 12.5 Root: 9.991 --> Root in between the borders! Added to results. Coefficients: -11.265Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20250519_003548.]: Samplename: 25 Root: 23.104 --> Root in between the borders! Added to results. Coefficients: -17.451Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20250519_003548.]: Samplename: 37.5 Root: 42.609 --> Root in between the borders! Added to results. Coefficients: -26.0315Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20250519_003548.]: Samplename: 50 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -33.965Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20250519_003548.]: Samplename: 62.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -41.169Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20250519_003548.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -44.1356666666667Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20250519_003548.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -67.223Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20250519_003548.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20250519_003548.]: Solving cubic regression for CpG#8 Coefficients: -1.096Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20250519_003548.]: Samplename: 0 Root: 2.018 --> Root in between the borders! Added to results. Coefficients: -5.44666666666667Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20250519_003548.]: Samplename: 12.5 Root: 9.505 --> Root in between the borders! Added to results. Coefficients: -16.93Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20250519_003548.]: Samplename: 25 Root: 26.424 --> Root in between the borders! Added to results. Coefficients: -23.35Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20250519_003548.]: Samplename: 37.5 Root: 34.662 --> Root in between the borders! Added to results. Coefficients: -37.625Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20250519_003548.]: Samplename: 50 Root: 50.932 --> Root in between the borders! Added to results. Coefficients: -48.826Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20250519_003548.]: Samplename: 62.5 Root: 62.271 --> Root in between the borders! Added to results. Coefficients: -61.6675Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20250519_003548.]: Samplename: 75 Root: 74.154 --> Root in between the borders! Added to results. Coefficients: -64.51Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20250519_003548.]: Samplename: 87.5 Root: 76.651 --> Root in between the borders! Added to results. Coefficients: -86.06Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20250519_003548.]: Samplename: 100 Root: 94.331 --> Root in between the borders! Added to results. [20250519_003548.]: Solving cubic regression for CpG#9 Coefficients: -0.99Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20250519_003548.]: Samplename: 0 Root: 1.474 --> Root in between the borders! Added to results. Coefficients: -6.396Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20250519_003548.]: Samplename: 12.5 Root: 10.183 --> Root in between the borders! Added to results. Coefficients: -14.706Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20250519_003548.]: Samplename: 25 Root: 27.044 --> Root in between the borders! Added to results. Coefficients: -20.624Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20250519_003548.]: Samplename: 37.5 Root: 45.863 --> Root in between the borders! Added to results. Coefficients: -31.396Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20250519_003548.]: Samplename: 50 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -42.686Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20250519_003548.]: Samplename: 62.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -52.9035Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20250519_003548.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -65.4926666666667Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20250519_003548.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -92.99Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20250519_003548.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20250519_003548.]: Solving cubic regression for row_means Coefficients: -0.771111111111111Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20250519_003548.]: Samplename: 0 Root: 1.288 --> Root in between the borders! Added to results. Coefficients: -6.47237037037037Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20250519_003548.]: Samplename: 12.5 Root: 10.989 --> Root in between the borders! Added to results. Coefficients: -14.8971111111111Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20250519_003548.]: Samplename: 25 Root: 25.951 --> Root in between the borders! Added to results. Coefficients: -22.1491111111111Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20250519_003548.]: Samplename: 37.5 Root: 39.518 --> Root in between the borders! Added to results. Coefficients: -32.5258888888889Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20250519_003548.]: Samplename: 50 Root: 60.262 --> Root in between the borders! Added to results. Coefficients: -44.7217777777778Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20250519_003548.]: Samplename: 62.5 Root: 87.213 --> Root in between the borders! Added to results. Coefficients: -54.4031111111111Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20250519_003548.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -62.4312592592593Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20250519_003548.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -84.7342222222222Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20250519_003548.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20250519_003548.]: ### Starting with regression calculations ### [20250519_003548.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_003549.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5)c(1.3350694679926, 11.6842678967181, 24.056490215457, 50.4411280642076)c(1.3350694679926, 0.815732103281899, 0.943509784543, 12.9411280642076)c(NA, 6.52585682625519, 3.774039138172, 34.5096748378869) [20250519_003549.]: Logging df_agg: CpG#1 [20250519_003549.]: c(0, 12.5, 25, 37.5)[20250519_003549.]: c(1.3350694679926, 11.6842678967181, 24.056490215457, 50.4411280642076)[20250519_003549.]: c(1.3350694679926, 0.815732103281899, 0.943509784543, 12.9411280642076)[20250519_003549.]: c(NA, 6.52585682625519, 3.774039138172, 34.5096748378869) [20250519_003549.]: Entered 'hyperbolic_regression'-Function [20250519_003549.]: 'hyperbolic_regression': minmax = FALSE [20250519_003549.]: Entered 'cubic_regression'-Function [20250519_003549.]: 'cubic_regression': minmax = FALSE [20250519_003549.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.547766708541827, 11.5349537694669, 26.7737761718445, 35.875449331591, 48.7401699196391, 69.1560032174257, 78.551969470104, 88.2723976336729, 100)c(0.547766708541827, 0.965046230533099, 1.7737761718445, 1.624550668409, 1.2598300803609, 6.65600321742571, 3.551969470104, 0.772397633672895, 0)c(NA, 7.72036984426479, 7.095104687378, 4.33213511575733, 2.5196601607218, 10.6496051478811, 4.735959293472, 0.882740152769023, 0) [20250519_003549.]: Logging df_agg: CpG#2 [20250519_003549.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003549.]: c(0.547766708541827, 11.5349537694669, 26.7737761718445, 35.875449331591, 48.7401699196391, 69.1560032174257, 78.551969470104, 88.2723976336729, 100)[20250519_003549.]: c(0.547766708541827, 0.965046230533099, 1.7737761718445, 1.624550668409, 1.2598300803609, 6.65600321742571, 3.551969470104, 0.772397633672895, 0)[20250519_003549.]: c(NA, 7.72036984426479, 7.095104687378, 4.33213511575733, 2.5196601607218, 10.6496051478811, 4.735959293472, 0.882740152769023, 0) [20250519_003549.]: Entered 'hyperbolic_regression'-Function [20250519_003549.]: 'hyperbolic_regression': minmax = FALSE [20250519_003549.]: Entered 'cubic_regression'-Function [20250519_003549.]: 'cubic_regression': minmax = FALSE [20250519_003549.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75)c(1.44120553086336, 10.6470702300701, 25.6131070939596, 38.3498594232174, 57.444937683935, 80.104724288839, 100)c(1.44120553086336, 1.8529297699299, 0.613107093959599, 0.849859423217403, 7.444937683935, 17.604724288839, 25)c(NA, 14.8234381594392, 2.45242837583839, 2.26629179524641, 14.88987536787, 28.1675588621424, 33.3333333333333) [20250519_003549.]: Logging df_agg: CpG#3 [20250519_003549.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75)[20250519_003549.]: c(1.44120553086336, 10.6470702300701, 25.6131070939596, 38.3498594232174, 57.444937683935, 80.104724288839, 100)[20250519_003549.]: c(1.44120553086336, 1.8529297699299, 0.613107093959599, 0.849859423217403, 7.444937683935, 17.604724288839, 25)[20250519_003549.]: c(NA, 14.8234381594392, 2.45242837583839, 2.26629179524641, 14.88987536787, 28.1675588621424, 33.3333333333333) [20250519_003549.]: Entered 'hyperbolic_regression'-Function [20250519_003549.]: 'hyperbolic_regression': minmax = FALSE [20250519_003549.]: Entered 'cubic_regression'-Function [20250519_003549.]: 'cubic_regression': minmax = FALSE [20250519_003549.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5)c(0.858643525693588, 12.2719279243844, 24.3781797668421, 41.5866572861968, 57.86781708526, 100)c(0.858643525693588, 0.2280720756156, 0.621820233157901, 4.0866572861968, 7.86781708526, 37.5)c(NA, 1.8245766049248, 2.48728093263161, 10.8977527631915, 15.73563417052, 60) [20250519_003549.]: Logging df_agg: CpG#4 [20250519_003549.]: c(0, 12.5, 25, 37.5, 50, 62.5)[20250519_003549.]: c(0.858643525693588, 12.2719279243844, 24.3781797668421, 41.5866572861968, 57.86781708526, 100)[20250519_003549.]: c(0.858643525693588, 0.2280720756156, 0.621820233157901, 4.0866572861968, 7.86781708526, 37.5)[20250519_003549.]: c(NA, 1.8245766049248, 2.48728093263161, 10.8977527631915, 15.73563417052, 60) [20250519_003549.]: Entered 'hyperbolic_regression'-Function [20250519_003549.]: 'hyperbolic_regression': minmax = FALSE [20250519_003550.]: Entered 'cubic_regression'-Function [20250519_003550.]: 'cubic_regression': minmax = FALSE [20250519_003550.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.45850947960108, 10.2941313684311, 24.4773429557174, 37.0974401416089, 49.5067234953282, 59.9312428933937, 72.5628584782902, 81.5490289118528, 94.898442253889)c(1.45850947960108, 2.2058686315689, 0.5226570442826, 0.402559858391101, 0.493276504671798, 2.5687571066063, 2.4371415217098, 5.9509710881472, 5.101557746111)c(NA, 17.6469490525512, 2.0906281771304, 1.0734929557096, 0.986553009343595, 4.11001137057008, 3.24952202894641, 6.80110981502537, 5.101557746111) [20250519_003550.]: Logging df_agg: CpG#5 [20250519_003550.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003550.]: c(1.45850947960108, 10.2941313684311, 24.4773429557174, 37.0974401416089, 49.5067234953282, 59.9312428933937, 72.5628584782902, 81.5490289118528, 94.898442253889)[20250519_003550.]: c(1.45850947960108, 2.2058686315689, 0.5226570442826, 0.402559858391101, 0.493276504671798, 2.5687571066063, 2.4371415217098, 5.9509710881472, 5.101557746111)[20250519_003550.]: c(NA, 17.6469490525512, 2.0906281771304, 1.0734929557096, 0.986553009343595, 4.11001137057008, 3.24952202894641, 6.80110981502537, 5.101557746111) [20250519_003550.]: Entered 'hyperbolic_regression'-Function [20250519_003550.]: 'hyperbolic_regression': minmax = FALSE [20250519_003550.]: Entered 'cubic_regression'-Function [20250519_003550.]: 'cubic_regression': minmax = FALSE [20250519_003550.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)c(0.349158802372062, 11.7651478823068, 27.019511576095, 36.8015127603397, 54.3503397041729, 73.8730379134395, 86.2591171327579, 100)c(0.349158802372062, 0.734852117693199, 2.019511576095, 0.698487239660302, 4.3503397041729, 11.3730379134395, 11.2591171327579, 12.5)c(NA, 5.87881694154559, 8.07804630438, 1.86263263909414, 8.70067940834581, 18.1968606615032, 15.0121561770105, 14.2857142857143) [20250519_003550.]: Logging df_agg: CpG#6 [20250519_003550.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)[20250519_003550.]: c(0.349158802372062, 11.7651478823068, 27.019511576095, 36.8015127603397, 54.3503397041729, 73.8730379134395, 86.2591171327579, 100)[20250519_003550.]: c(0.349158802372062, 0.734852117693199, 2.019511576095, 0.698487239660302, 4.3503397041729, 11.3730379134395, 11.2591171327579, 12.5)[20250519_003550.]: c(NA, 5.87881694154559, 8.07804630438, 1.86263263909414, 8.70067940834581, 18.1968606615032, 15.0121561770105, 14.2857142857143) [20250519_003550.]: Entered 'hyperbolic_regression'-Function [20250519_003550.]: 'hyperbolic_regression': minmax = FALSE [20250519_003550.]: Entered 'cubic_regression'-Function [20250519_003550.]: 'cubic_regression': minmax = FALSE [20250519_003550.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5)c(2.12599911899281, 9.99133393413891, 23.1036438590861, 42.6085296447297)c(2.12599911899281, 2.50866606586109, 1.8963561409139, 5.1085296447297)c(NA, 20.0693285268887, 7.58542456365559, 13.6227457192792) [20250519_003550.]: Logging df_agg: CpG#7 [20250519_003550.]: c(0, 12.5, 25, 37.5)[20250519_003550.]: c(2.12599911899281, 9.99133393413891, 23.1036438590861, 42.6085296447297)[20250519_003550.]: c(2.12599911899281, 2.50866606586109, 1.8963561409139, 5.1085296447297)[20250519_003550.]: c(NA, 20.0693285268887, 7.58542456365559, 13.6227457192792) [20250519_003550.]: Entered 'hyperbolic_regression'-Function [20250519_003550.]: 'hyperbolic_regression': minmax = FALSE [20250519_003550.]: Entered 'cubic_regression'-Function [20250519_003550.]: 'cubic_regression': minmax = FALSE [20250519_003550.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.0181464298155, 9.50518258466437, 26.4243334649289, 34.6620160335629, 50.9321119457565, 62.2712954366094, 74.1538645100711, 76.6511844577497, 94.3306625091067)c(2.0181464298155, 2.99481741533563, 1.4243334649289, 2.8379839664371, 0.932111945756503, 0.2287045633906, 0.846135489928898, 10.8488155422503, 5.6693374908933)c(NA, 23.958539322685, 5.6973338597156, 7.56795724383227, 1.86422389151301, 0.365927301424961, 1.12818065323853, 12.3986463340004, 5.6693374908933) [20250519_003550.]: Logging df_agg: CpG#8 [20250519_003550.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003550.]: c(2.0181464298155, 9.50518258466437, 26.4243334649289, 34.6620160335629, 50.9321119457565, 62.2712954366094, 74.1538645100711, 76.6511844577497, 94.3306625091067)[20250519_003550.]: c(2.0181464298155, 2.99481741533563, 1.4243334649289, 2.8379839664371, 0.932111945756503, 0.2287045633906, 0.846135489928898, 10.8488155422503, 5.6693374908933)[20250519_003550.]: c(NA, 23.958539322685, 5.6973338597156, 7.56795724383227, 1.86422389151301, 0.365927301424961, 1.12818065323853, 12.3986463340004, 5.6693374908933) [20250519_003550.]: Entered 'hyperbolic_regression'-Function [20250519_003550.]: 'hyperbolic_regression': minmax = FALSE [20250519_003550.]: Entered 'cubic_regression'-Function [20250519_003550.]: 'cubic_regression': minmax = FALSE [20250519_003551.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5)c(1.47402616445883, 10.1833646164297, 27.0440334834678, 45.8634394698265)c(1.47402616445883, 2.3166353835703, 2.0440334834678, 8.3634394698265)c(NA, 18.5330830685624, 8.1761339338712, 22.3025052528707) [20250519_003551.]: Logging df_agg: CpG#9 [20250519_003551.]: c(0, 12.5, 25, 37.5)[20250519_003551.]: c(1.47402616445883, 10.1833646164297, 27.0440334834678, 45.8634394698265)[20250519_003551.]: c(1.47402616445883, 2.3166353835703, 2.0440334834678, 8.3634394698265)[20250519_003551.]: c(NA, 18.5330830685624, 8.1761339338712, 22.3025052528707) [20250519_003551.]: Entered 'hyperbolic_regression'-Function [20250519_003551.]: 'hyperbolic_regression': minmax = FALSE [20250519_003551.]: Entered 'cubic_regression'-Function [20250519_003551.]: 'cubic_regression': minmax = FALSE [20250519_003551.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5)c(1.28794987665983, 10.9885302801468, 25.950937077156, 39.5179682033924, 60.2624119769469, 87.2132000752344)c(1.28794987665983, 1.5114697198532, 0.950937077155999, 2.0179682033924, 10.2624119769469, 24.7132000752344)c(NA, 12.0917577588256, 3.80374830862399, 5.38124854237973, 20.5248239538938, 39.541120120375) [20250519_003551.]: Logging df_agg: row_means [20250519_003551.]: c(0, 12.5, 25, 37.5, 50, 62.5)[20250519_003551.]: c(1.28794987665983, 10.9885302801468, 25.950937077156, 39.5179682033924, 60.2624119769469, 87.2132000752344)[20250519_003551.]: c(1.28794987665983, 1.5114697198532, 0.950937077155999, 2.0179682033924, 10.2624119769469, 24.7132000752344)[20250519_003551.]: c(NA, 12.0917577588256, 3.80374830862399, 5.38124854237973, 20.5248239538938, 39.541120120375) [20250519_003551.]: Entered 'hyperbolic_regression'-Function [20250519_003551.]: 'hyperbolic_regression': minmax = FALSE [20250519_003551.]: Entered 'cubic_regression'-Function [20250519_003551.]: 'cubic_regression': minmax = FALSE [20250519_003551.]: Entered 'solving_equations'-Function [20250519_003552.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 79.8672902891399 [20250519_003552.]: Samplename: Sample#1 Root: 79.867 --> Root in between the borders! Added to results. Hyperbolic solved: 29.789995526108 [20250519_003552.]: Samplename: Sample#10 Root: 29.79 --> Root in between the borders! Added to results. Hyperbolic solved: 41.6525103283956 [20250519_003552.]: Samplename: Sample#2 Root: 41.653 --> Root in between the borders! Added to results. Hyperbolic solved: 57.4651672980431 [20250519_003552.]: Samplename: Sample#3 Root: 57.465 --> Root in between the borders! Added to results. Hyperbolic solved: 9.20070553821487 [20250519_003552.]: Samplename: Sample#4 Root: 9.201 --> Root in between the borders! Added to results. Hyperbolic solved: 21.8059429711637 [20250519_003552.]: Samplename: Sample#5 Root: 21.806 --> Root in between the borders! Added to results. Hyperbolic solved: 23.0837787089758 [20250519_003552.]: Samplename: Sample#6 Root: 23.084 --> Root in between the borders! Added to results. Hyperbolic solved: 45.5033907021902 [20250519_003552.]: Samplename: Sample#7 Root: 45.503 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6987316769908 [20250519_003552.]: Samplename: Sample#8 Root: 85.699 --> Root in between the borders! Added to results. Hyperbolic solved: -3.66512538562846 [20250519_003552.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -3.665 --> '-10 < root < 0' --> substitute 0 [20250519_003552.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 76.7230240612756 [20250519_003552.]: Samplename: Sample#1 Root: 76.723 --> Root in between the borders! Added to results. Hyperbolic solved: 30.992543297941 [20250519_003552.]: Samplename: Sample#10 Root: 30.993 --> Root in between the borders! Added to results. Hyperbolic solved: 42.6812361010669 [20250519_003552.]: Samplename: Sample#2 Root: 42.681 --> Root in between the borders! Added to results. Hyperbolic solved: 58.933980239834 [20250519_003552.]: Samplename: Sample#3 Root: 58.934 --> Root in between the borders! Added to results. Hyperbolic solved: 4.98024265777963 [20250519_003552.]: Samplename: Sample#4 Root: 4.98 --> Root in between the borders! Added to results. Hyperbolic solved: 20.2481111308688 [20250519_003552.]: Samplename: Sample#5 Root: 20.248 --> Root in between the borders! Added to results. Hyperbolic solved: 17.813386088098 [20250519_003552.]: Samplename: Sample#6 Root: 17.813 --> Root in between the borders! Added to results. Hyperbolic solved: 40.9178176381316 [20250519_003552.]: Samplename: Sample#7 Root: 40.918 --> Root in between the borders! Added to results. Hyperbolic solved: 86.0453226524415 [20250519_003552.]: Samplename: Sample#8 Root: 86.045 --> Root in between the borders! Added to results. Hyperbolic solved: 3.11582988095862 [20250519_003552.]: Samplename: Sample#9 Root: 3.116 --> Root in between the borders! Added to results. [20250519_003552.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 74.934721803959 [20250519_003552.]: Samplename: Sample#1 Root: 74.935 --> Root in between the borders! Added to results. Hyperbolic solved: 27.6843475257834 [20250519_003552.]: Samplename: Sample#10 Root: 27.684 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8518905782652 [20250519_003552.]: Samplename: Sample#2 Root: 41.852 --> Root in between the borders! Added to results. Hyperbolic solved: 55.8323554649537 [20250519_003552.]: Samplename: Sample#3 Root: 55.832 --> Root in between the borders! Added to results. Hyperbolic solved: 8.03516016616019 [20250519_003552.]: Samplename: Sample#4 Root: 8.035 --> Root in between the borders! Added to results. Hyperbolic solved: 24.1065510576898 [20250519_003552.]: Samplename: Sample#5 Root: 24.107 --> Root in between the borders! Added to results. Hyperbolic solved: 26.2418954231184 [20250519_003552.]: Samplename: Sample#6 Root: 26.242 --> Root in between the borders! Added to results. Hyperbolic solved: 44.0943580511304 [20250519_003552.]: Samplename: Sample#7 Root: 44.094 --> Root in between the borders! Added to results. Hyperbolic solved: 85.8277140531902 [20250519_003552.]: Samplename: Sample#8 Root: 85.828 --> Root in between the borders! Added to results. Hyperbolic solved: -0.666486811581751 [20250519_003552.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.666 --> '-10 < root < 0' --> substitute 0 [20250519_003552.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 76.349372671628 [20250519_003552.]: Samplename: Sample#1 Root: 76.349 --> Root in between the borders! Added to results. Hyperbolic solved: 28.2567843724913 [20250519_003552.]: Samplename: Sample#10 Root: 28.257 --> Root in between the borders! Added to results. Hyperbolic solved: 43.4088841408467 [20250519_003552.]: Samplename: Sample#2 Root: 43.409 --> Root in between the borders! Added to results. Hyperbolic solved: 58.5433972042602 [20250519_003552.]: Samplename: Sample#3 Root: 58.543 --> Root in between the borders! Added to results. Hyperbolic solved: 10.3086704325003 [20250519_003552.]: Samplename: Sample#4 Root: 10.309 --> Root in between the borders! Added to results. Hyperbolic solved: 22.182986320858 [20250519_003552.]: Samplename: Sample#5 Root: 22.183 --> Root in between the borders! Added to results. Hyperbolic solved: 27.1337081900258 [20250519_003552.]: Samplename: Sample#6 Root: 27.134 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8320127104856 [20250519_003552.]: Samplename: Sample#7 Root: 41.832 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6888498156134 [20250519_003552.]: Samplename: Sample#8 Root: 85.689 --> Root in between the borders! Added to results. Hyperbolic solved: 2.42230396811441 [20250519_003552.]: Samplename: Sample#9 Root: 2.422 --> Root in between the borders! Added to results. [20250519_003552.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 72.8126507590781 [20250519_003552.]: Samplename: Sample#1 Root: 72.813 --> Root in between the borders! Added to results. Hyperbolic solved: 26.4206774223762 [20250519_003552.]: Samplename: Sample#10 Root: 26.421 --> Root in between the borders! Added to results. Hyperbolic solved: 44.2738516315831 [20250519_003552.]: Samplename: Sample#2 Root: 44.274 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5324088748019 [20250519_003552.]: Samplename: Sample#3 Root: 53.532 --> Root in between the borders! Added to results. Hyperbolic solved: 10.0373628417333 [20250519_003552.]: Samplename: Sample#4 Root: 10.037 --> Root in between the borders! Added to results. Hyperbolic solved: 22.8732969704156 [20250519_003552.]: Samplename: Sample#5 Root: 22.873 --> Root in between the borders! Added to results. Hyperbolic solved: 25.9417756406497 [20250519_003552.]: Samplename: Sample#6 Root: 25.942 --> Root in between the borders! Added to results. Hyperbolic solved: 42.7944833740993 [20250519_003552.]: Samplename: Sample#7 Root: 42.794 --> Root in between the borders! Added to results. Hyperbolic solved: 88.8740708807914 [20250519_003552.]: Samplename: Sample#8 Root: 88.874 --> Root in between the borders! Added to results. Hyperbolic solved: 2.34646946564885 [20250519_003552.]: Samplename: Sample#9 Root: 2.346 --> Root in between the borders! Added to results. [20250519_003552.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 79.2782328649447 [20250519_003552.]: Samplename: Sample#1 Root: 79.278 --> Root in between the borders! Added to results. Hyperbolic solved: 30.2013285011234 [20250519_003552.]: Samplename: Sample#10 Root: 30.201 --> Root in between the borders! Added to results. Hyperbolic solved: 41.847545497608 [20250519_003552.]: Samplename: Sample#2 Root: 41.848 --> Root in between the borders! Added to results. Hyperbolic solved: 56.8424863688491 [20250519_003552.]: Samplename: Sample#3 Root: 56.842 --> Root in between the borders! Added to results. Hyperbolic solved: 8.87859681588532 [20250519_003552.]: Samplename: Sample#4 Root: 8.879 --> Root in between the borders! Added to results. Hyperbolic solved: 18.6902176438696 [20250519_003552.]: Samplename: Sample#5 Root: 18.69 --> Root in between the borders! Added to results. Hyperbolic solved: 29.9310083954849 [20250519_003552.]: Samplename: Sample#6 Root: 29.931 --> Root in between the borders! Added to results. Hyperbolic solved: 42.814964037839 [20250519_003552.]: Samplename: Sample#7 Root: 42.815 --> Root in between the borders! Added to results. Hyperbolic solved: 86.7503685829287 [20250519_003552.]: Samplename: Sample#8 Root: 86.75 --> Root in between the borders! Added to results. Hyperbolic solved: 1.51518138323643 [20250519_003552.]: Samplename: Sample#9 Root: 1.515 --> Root in between the borders! Added to results. [20250519_003552.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 78.2568233815803 [20250519_003552.]: Samplename: Sample#1 Root: 78.257 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4888594923858 [20250519_003552.]: Samplename: Sample#10 Root: 25.489 --> Root in between the borders! Added to results. Hyperbolic solved: 47.3714242177043 [20250519_003552.]: Samplename: Sample#2 Root: 47.371 --> Root in between the borders! Added to results. Hyperbolic solved: 58.3144949237501 [20250519_003552.]: Samplename: Sample#3 Root: 58.314 --> Root in between the borders! Added to results. Hyperbolic solved: 11.7212806729981 [20250519_003552.]: Samplename: Sample#4 Root: 11.721 --> Root in between the borders! Added to results. Hyperbolic solved: 25.3798682873976 [20250519_003552.]: Samplename: Sample#5 Root: 25.38 --> Root in between the borders! Added to results. Hyperbolic solved: 29.4096493828253 [20250519_003552.]: Samplename: Sample#6 Root: 29.41 --> Root in between the borders! Added to results. Hyperbolic solved: 44.5756969430888 [20250519_003552.]: Samplename: Sample#7 Root: 44.576 --> Root in between the borders! Added to results. Hyperbolic solved: 85.9631455147929 [20250519_003552.]: Samplename: Sample#8 Root: 85.963 --> Root in between the borders! Added to results. Hyperbolic solved: -4.16459229634232 [20250519_003552.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -4.165 --> '-10 < root < 0' --> substitute 0 [20250519_003552.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 72.9696678780879 [20250519_003552.]: Samplename: Sample#1 Root: 72.97 --> Root in between the borders! Added to results. Hyperbolic solved: 27.825096438612 [20250519_003552.]: Samplename: Sample#10 Root: 27.825 --> Root in between the borders! Added to results. Hyperbolic solved: 34.9068412412515 [20250519_003552.]: Samplename: Sample#2 Root: 34.907 --> Root in between the borders! Added to results. Hyperbolic solved: 59.1637501357335 [20250519_003552.]: Samplename: Sample#3 Root: 59.164 --> Root in between the borders! Added to results. Hyperbolic solved: 9.6464397671905 [20250519_003552.]: Samplename: Sample#4 Root: 9.646 --> Root in between the borders! Added to results. Hyperbolic solved: 19.3522593577541 [20250519_003552.]: Samplename: Sample#5 Root: 19.352 --> Root in between the borders! Added to results. Hyperbolic solved: 38.5571468548369 [20250519_003552.]: Samplename: Sample#6 Root: 38.557 --> Root in between the borders! Added to results. Hyperbolic solved: 44.775966890921 [20250519_003552.]: Samplename: Sample#7 Root: 44.776 --> Root in between the borders! Added to results. Hyperbolic solved: 84.8599747802978 [20250519_003552.]: Samplename: Sample#8 Root: 84.86 --> Root in between the borders! Added to results. Hyperbolic solved: -2.05045739684141 [20250519_003552.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.05 --> '-10 < root < 0' --> substitute 0 [20250519_003552.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 81.0413625074508 [20250519_003552.]: Samplename: Sample#1 Root: 81.041 --> Root in between the borders! Added to results. Hyperbolic solved: 26.6440232383638 [20250519_003552.]: Samplename: Sample#10 Root: 26.644 --> Root in between the borders! Added to results. Hyperbolic solved: 45.704400902509 [20250519_003552.]: Samplename: Sample#2 Root: 45.704 --> Root in between the borders! Added to results. Hyperbolic solved: 56.9765444860285 [20250519_003552.]: Samplename: Sample#3 Root: 56.977 --> Root in between the borders! Added to results. Hyperbolic solved: 6.81226926030202 [20250519_003552.]: Samplename: Sample#4 Root: 6.812 --> Root in between the borders! Added to results. Hyperbolic solved: 22.9283517775063 [20250519_003552.]: Samplename: Sample#5 Root: 22.928 --> Root in between the borders! Added to results. Hyperbolic solved: 38.8651260750996 [20250519_003552.]: Samplename: Sample#6 Root: 38.865 --> Root in between the borders! Added to results. Hyperbolic solved: 43.6853511825078 [20250519_003552.]: Samplename: Sample#7 Root: 43.685 --> Root in between the borders! Added to results. Hyperbolic solved: 88.0152224353095 [20250519_003552.]: Samplename: Sample#8 Root: 88.015 --> Root in between the borders! Added to results. Hyperbolic solved: -3.35608361520768 [20250519_003552.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -3.356 --> '-10 < root < 0' --> substitute 0 [20250519_003552.]: Solving hyperbolic regression for row_means Hyperbolic solved: 77.069275047056 [20250519_003552.]: Samplename: Sample#1 Root: 77.069 --> Root in between the borders! Added to results. Hyperbolic solved: 28.3620003757916 [20250519_003552.]: Samplename: Sample#10 Root: 28.362 --> Root in between the borders! Added to results. Hyperbolic solved: 42.5026135474906 [20250519_003552.]: Samplename: Sample#2 Root: 42.503 --> Root in between the borders! Added to results. Hyperbolic solved: 57.2972007822813 [20250519_003552.]: Samplename: Sample#3 Root: 57.297 --> Root in between the borders! Added to results. Hyperbolic solved: 8.82703592503651 [20250519_003552.]: Samplename: Sample#4 Root: 8.827 --> Root in between the borders! Added to results. Hyperbolic solved: 21.8102552608375 [20250519_003552.]: Samplename: Sample#5 Root: 21.81 --> Root in between the borders! Added to results. Hyperbolic solved: 28.7228620570755 [20250519_003552.]: Samplename: Sample#6 Root: 28.723 --> Root in between the borders! Added to results. Hyperbolic solved: 43.4105062814289 [20250519_003552.]: Samplename: Sample#7 Root: 43.411 --> Root in between the borders! Added to results. Hyperbolic solved: 86.4143497902066 [20250519_003552.]: Samplename: Sample#8 Root: 86.414 --> Root in between the borders! Added to results. Hyperbolic solved: -0.237025016559672 [20250519_003552.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.237 --> '-10 < root < 0' --> substitute 0 [20250519_003552.]: Entered 'solving_equations'-Function [20250519_003552.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: -2.23222837469517 [20250519_003552.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.232 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.1698391693126 [20250519_003552.]: Samplename: 12.5 Root: 12.17 --> Root in between the borders! Added to results. Hyperbolic solved: 24.4781729791561 [20250519_003552.]: Samplename: 25 Root: 24.478 --> Root in between the borders! Added to results. Hyperbolic solved: 38.1730159074047 [20250519_003552.]: Samplename: 37.5 Root: 38.173 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3348987967717 [20250519_003552.]: Samplename: 50 Root: 52.335 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4582305966058 [20250519_003552.]: Samplename: 62.5 Root: 65.458 --> Root in between the borders! Added to results. Hyperbolic solved: 75.0090269722885 [20250519_003552.]: Samplename: 75 Root: 75.009 --> Root in between the borders! Added to results. Hyperbolic solved: 81.527135738891 [20250519_003552.]: Samplename: 87.5 Root: 81.527 --> Root in between the borders! Added to results. Hyperbolic solved: 102.400825350044 [20250519_003552.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.401 --> '100 < root < 110' --> substitute 100 [20250519_003552.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 1.13663087266051 [20250519_003552.]: Samplename: 0 Root: 1.137 --> Root in between the borders! Added to results. Hyperbolic solved: 11.4130277963576 [20250519_003552.]: Samplename: 12.5 Root: 11.413 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1741025868157 [20250519_003552.]: Samplename: 25 Root: 26.174 --> Root in between the borders! Added to results. Hyperbolic solved: 35.1051721736724 [20250519_003552.]: Samplename: 37.5 Root: 35.105 --> Root in between the borders! Added to results. Hyperbolic solved: 47.6856613240114 [20250519_003552.]: Samplename: 50 Root: 47.686 --> Root in between the borders! Added to results. Hyperbolic solved: 67.1442585354696 [20250519_003552.]: Samplename: 62.5 Root: 67.144 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7646957109439 [20250519_003552.]: Samplename: 75 Root: 75.765 --> Root in between the borders! Added to results. Hyperbolic solved: 84.4056634834815 [20250519_003552.]: Samplename: 87.5 Root: 84.406 --> Root in between the borders! Added to results. Hyperbolic solved: 100.94855337669 [20250519_003552.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.949 --> '100 < root < 110' --> substitute 100 [20250519_003552.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 0.512348257854635 [20250519_003552.]: Samplename: 0 Root: 0.512 --> Root in between the borders! Added to results. Hyperbolic solved: 10.7523476241811 [20250519_003552.]: Samplename: 12.5 Root: 10.752 --> Root in between the borders! Added to results. Hyperbolic solved: 25.5218062516799 [20250519_003552.]: Samplename: 25 Root: 25.522 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5269315729339 [20250519_003552.]: Samplename: 37.5 Root: 36.527 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7907738274884 [20250519_003552.]: Samplename: 50 Root: 50.791 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8684489988589 [20250519_003552.]: Samplename: 62.5 Root: 64.868 --> Root in between the borders! Added to results. Hyperbolic solved: 77.5522101073979 [20250519_003552.]: Samplename: 75 Root: 77.552 --> Root in between the borders! Added to results. Hyperbolic solved: 80.4372474691229 [20250519_003552.]: Samplename: 87.5 Root: 80.437 --> Root in between the borders! Added to results. Hyperbolic solved: 102.703772810881 [20250519_003552.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.704 --> '100 < root < 110' --> substitute 100 [20250519_003552.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: -0.519513573026794 [20250519_003552.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.52 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.4933760051342 [20250519_003552.]: Samplename: 12.5 Root: 12.493 --> Root in between the borders! Added to results. Hyperbolic solved: 24.2684789512555 [20250519_003552.]: Samplename: 25 Root: 24.268 --> Root in between the borders! Added to results. Hyperbolic solved: 38.0816229393195 [20250519_003552.]: Samplename: 37.5 Root: 38.082 --> Root in between the borders! Added to results. Hyperbolic solved: 48.5842089589187 [20250519_003552.]: Samplename: 50 Root: 48.584 --> Root in between the borders! Added to results. Hyperbolic solved: 67.6720983549562 [20250519_003552.]: Samplename: 62.5 Root: 67.672 --> Root in between the borders! Added to results. Hyperbolic solved: 74.1547759694059 [20250519_003552.]: Samplename: 75 Root: 74.155 --> Root in between the borders! Added to results. Hyperbolic solved: 82.8820147905386 [20250519_003552.]: Samplename: 87.5 Root: 82.882 --> Root in between the borders! Added to results. Hyperbolic solved: 102.078935346876 [20250519_003552.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.079 --> '100 < root < 110' --> substitute 100 [20250519_003552.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 2.41558040143479 [20250519_003552.]: Samplename: 0 Root: 2.416 --> Root in between the borders! Added to results. Hyperbolic solved: 10.1649584830834 [20250519_003552.]: Samplename: 12.5 Root: 10.165 --> Root in between the borders! Added to results. Hyperbolic solved: 23.9830670020905 [20250519_003552.]: Samplename: 25 Root: 23.983 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2773406385708 [20250519_003552.]: Samplename: 37.5 Root: 37.277 --> Root in between the borders! Added to results. Hyperbolic solved: 50.8659103346102 [20250519_003552.]: Samplename: 50 Root: 50.866 --> Root in between the borders! Added to results. Hyperbolic solved: 62.4341926937382 [20250519_003552.]: Samplename: 62.5 Root: 62.434 --> Root in between the borders! Added to results. Hyperbolic solved: 76.3914832329149 [20250519_003552.]: Samplename: 75 Root: 76.391 --> Root in between the borders! Added to results. Hyperbolic solved: 86.1597399215782 [20250519_003552.]: Samplename: 87.5 Root: 86.16 --> Root in between the borders! Added to results. Hyperbolic solved: 100.267701841999 [20250519_003552.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.268 --> '100 < root < 110' --> substitute 100 [20250519_003552.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 0.138179963459196 [20250519_003552.]: Samplename: 0 Root: 0.138 --> Root in between the borders! Added to results. Hyperbolic solved: 11.8635989635202 [20250519_003552.]: Samplename: 12.5 Root: 11.864 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5108198460409 [20250519_003552.]: Samplename: 25 Root: 26.511 --> Root in between the borders! Added to results. Hyperbolic solved: 35.3206004021833 [20250519_003552.]: Samplename: 37.5 Root: 35.321 --> Root in between the borders! Added to results. Hyperbolic solved: 50.0571987473308 [20250519_003552.]: Samplename: 50 Root: 50.057 --> Root in between the borders! Added to results. Hyperbolic solved: 64.9604436607063 [20250519_003552.]: Samplename: 62.5 Root: 64.96 --> Root in between the borders! Added to results. Hyperbolic solved: 73.6690699266294 [20250519_003552.]: Samplename: 75 Root: 73.669 --> Root in between the borders! Added to results. Hyperbolic solved: 87.1267946879604 [20250519_003552.]: Samplename: 87.5 Root: 87.127 --> Root in between the borders! Added to results. Hyperbolic solved: 100.261842767865 [20250519_003552.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.262 --> '100 < root < 110' --> substitute 100 [20250519_003552.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: -1.37239249181669 [20250519_003552.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -1.372 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.199366215418 [20250519_003552.]: Samplename: 12.5 Root: 10.199 --> Root in between the borders! Added to results. Hyperbolic solved: 24.595295361327 [20250519_003552.]: Samplename: 25 Root: 24.595 --> Root in between the borders! Added to results. Hyperbolic solved: 37.8312095633391 [20250519_003552.]: Samplename: 37.5 Root: 37.831 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5590896582852 [20250519_003552.]: Samplename: 50 Root: 53.559 --> Root in between the borders! Added to results. Hyperbolic solved: 65.9367389282431 [20250519_003552.]: Samplename: 62.5 Root: 65.937 --> Root in between the borders! Added to results. Hyperbolic solved: 75.736370152841 [20250519_003552.]: Samplename: 75 Root: 75.736 --> Root in between the borders! Added to results. Hyperbolic solved: 79.433089359524 [20250519_003552.]: Samplename: 87.5 Root: 79.433 --> Root in between the borders! Added to results. Hyperbolic solved: 103.004516225662 [20250519_003552.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 103.005 --> '100 < root < 110' --> substitute 100 [20250519_003552.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 2.80067549526643 [20250519_003552.]: Samplename: 0 Root: 2.801 --> Root in between the borders! Added to results. Hyperbolic solved: 9.27534686003087 [20250519_003552.]: Samplename: 12.5 Root: 9.275 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4762624089952 [20250519_003552.]: Samplename: 25 Root: 25.476 --> Root in between the borders! Added to results. Hyperbolic solved: 34.0122098045585 [20250519_003552.]: Samplename: 37.5 Root: 34.012 --> Root in between the borders! Added to results. Hyperbolic solved: 51.784270967861 [20250519_003552.]: Samplename: 50 Root: 51.784 --> Root in between the borders! Added to results. Hyperbolic solved: 64.6732380258789 [20250519_003552.]: Samplename: 62.5 Root: 64.673 --> Root in between the borders! Added to results. Hyperbolic solved: 78.4327054483192 [20250519_003552.]: Samplename: 75 Root: 78.433 --> Root in between the borders! Added to results. Hyperbolic solved: 81.3427308951795 [20250519_003552.]: Samplename: 87.5 Root: 81.343 --> Root in between the borders! Added to results. Hyperbolic solved: 101.964413523995 [20250519_003552.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.964 --> '100 < root < 110' --> substitute 100 [20250519_003552.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: -2.13402763663736 [20250519_003552.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.134 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.5081558897243 [20250519_003552.]: Samplename: 12.5 Root: 10.508 --> Root in between the borders! Added to results. Hyperbolic solved: 26.9163121098627 [20250519_003552.]: Samplename: 25 Root: 26.916 --> Root in between the borders! Added to results. Hyperbolic solved: 36.8332915301825 [20250519_003552.]: Samplename: 37.5 Root: 36.833 --> Root in between the borders! Added to results. Hyperbolic solved: 52.00955 [20250519_003552.]: Samplename: 50 Root: 52.01 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8927781893359 [20250519_003552.]: Samplename: 62.5 Root: 64.893 --> Root in between the borders! Added to results. Hyperbolic solved: 74.566810799042 [20250519_003552.]: Samplename: 75 Root: 74.567 --> Root in between the borders! Added to results. Hyperbolic solved: 84.5291854856128 [20250519_003552.]: Samplename: 87.5 Root: 84.529 --> Root in between the borders! Added to results. Hyperbolic solved: 101.046823491232 [20250519_003552.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.047 --> '100 < root < 110' --> substitute 100 [20250519_003552.]: Solving hyperbolic regression for row_means Hyperbolic solved: 0.290936038655697 [20250519_003552.]: Samplename: 0 Root: 0.291 --> Root in between the borders! Added to results. Hyperbolic solved: 11.0412364555656 [20250519_003552.]: Samplename: 12.5 Root: 11.041 --> Root in between the borders! Added to results. Hyperbolic solved: 25.408146358228 [20250519_003552.]: Samplename: 25 Root: 25.408 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5243683648753 [20250519_003552.]: Samplename: 37.5 Root: 36.524 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7348788337891 [20250519_003552.]: Samplename: 50 Root: 50.735 --> Root in between the borders! Added to results. Hyperbolic solved: 65.3135169308955 [20250519_003552.]: Samplename: 62.5 Root: 65.314 --> Root in between the borders! Added to results. Hyperbolic solved: 75.5342663138126 [20250519_003552.]: Samplename: 75 Root: 75.534 --> Root in between the borders! Added to results. Hyperbolic solved: 83.2411177153911 [20250519_003552.]: Samplename: 87.5 Root: 83.241 --> Root in between the borders! Added to results. Hyperbolic solved: 101.666935931185 [20250519_003552.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.667 --> '100 < root < 110' --> substitute 100 [20250519_003552.]: Entered 'clean_dt'-Function [20250519_003552.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_003552.]: got experimental data [20250519_003552.]: Entered 'clean_dt'-Function [20250519_003552.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_003552.]: got calibration data [20250519_003552.]: ### Starting with regression calculations ### [20250519_003552.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_003552.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_003553.]: Logging df_agg: CpG#1 [20250519_003553.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003553.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_003553.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_003553.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_003553.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_003553.]: Entered 'hyperbolic_regression'-Function [20250519_003553.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003553.]: Entered 'cubic_regression'-Function [20250519_003553.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003553.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_003553.]: Logging df_agg: CpG#2 [20250519_003553.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003553.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_003553.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_003553.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_003553.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_003553.]: Entered 'hyperbolic_regression'-Function [20250519_003553.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003553.]: Entered 'cubic_regression'-Function [20250519_003553.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003553.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_003553.]: Logging df_agg: CpG#3 [20250519_003553.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003553.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_003553.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_003553.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_003553.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_003553.]: Entered 'hyperbolic_regression'-Function [20250519_003553.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003554.]: Entered 'cubic_regression'-Function [20250519_003554.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003554.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_003554.]: Logging df_agg: CpG#4 [20250519_003554.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003554.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_003554.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_003554.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_003554.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_003554.]: Entered 'hyperbolic_regression'-Function [20250519_003554.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003554.]: Entered 'cubic_regression'-Function [20250519_003554.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003554.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_003554.]: Logging df_agg: CpG#5 [20250519_003554.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003554.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_003554.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_003554.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_003554.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_003554.]: Entered 'hyperbolic_regression'-Function [20250519_003554.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003554.]: Entered 'cubic_regression'-Function [20250519_003554.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003554.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_003555.]: Logging df_agg: CpG#6 [20250519_003555.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003555.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_003555.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_003555.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_003555.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_003555.]: Entered 'hyperbolic_regression'-Function [20250519_003555.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003555.]: Entered 'cubic_regression'-Function [20250519_003555.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003555.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_003555.]: Logging df_agg: CpG#7 [20250519_003555.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003555.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_003555.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_003555.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_003555.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_003555.]: Entered 'hyperbolic_regression'-Function [20250519_003555.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003555.]: Entered 'cubic_regression'-Function [20250519_003555.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003555.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_003555.]: Logging df_agg: CpG#8 [20250519_003555.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003555.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_003555.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_003555.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_003555.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_003555.]: Entered 'hyperbolic_regression'-Function [20250519_003555.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003556.]: Entered 'cubic_regression'-Function [20250519_003556.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003556.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_003556.]: Logging df_agg: CpG#9 [20250519_003556.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003556.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_003556.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_003556.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_003556.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_003556.]: Entered 'hyperbolic_regression'-Function [20250519_003556.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003556.]: Entered 'cubic_regression'-Function [20250519_003556.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003556.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_003556.]: Logging df_agg: row_means [20250519_003556.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003556.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_003556.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_003556.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_003556.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_003556.]: Entered 'hyperbolic_regression'-Function [20250519_003556.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003556.]: Entered 'cubic_regression'-Function [20250519_003556.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003557.]: ### Starting with regression calculations ### [20250519_003557.]: Entered 'regression_type1'-Function [20250519_003557.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_003557.]: Logging df_agg: CpG#1 [20250519_003557.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003557.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_003557.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_003557.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_003557.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_003557.]: Entered 'hyperbolic_regression'-Function [20250519_003557.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003558.]: Entered 'cubic_regression'-Function [20250519_003558.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003558.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_003558.]: Logging df_agg: CpG#2 [20250519_003558.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003558.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_003558.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_003558.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_003558.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_003558.]: Entered 'hyperbolic_regression'-Function [20250519_003558.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003558.]: Entered 'cubic_regression'-Function [20250519_003558.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003558.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_003558.]: Logging df_agg: CpG#3 [20250519_003558.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003558.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_003558.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_003558.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_003558.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_003558.]: Entered 'hyperbolic_regression'-Function [20250519_003558.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003558.]: Entered 'cubic_regression'-Function [20250519_003558.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003559.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_003559.]: Logging df_agg: CpG#4 [20250519_003559.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003559.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_003559.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_003559.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_003559.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_003559.]: Entered 'hyperbolic_regression'-Function [20250519_003559.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003559.]: Entered 'cubic_regression'-Function [20250519_003559.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003559.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_003559.]: Logging df_agg: CpG#5 [20250519_003559.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003559.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_003559.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_003559.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_003559.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_003559.]: Entered 'hyperbolic_regression'-Function [20250519_003559.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003559.]: Entered 'cubic_regression'-Function [20250519_003559.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003559.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_003559.]: Logging df_agg: CpG#6 [20250519_003559.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003559.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_003559.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_003559.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_003559.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_003559.]: Entered 'hyperbolic_regression'-Function [20250519_003559.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003600.]: Entered 'cubic_regression'-Function [20250519_003600.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003600.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_003600.]: Logging df_agg: CpG#7 [20250519_003600.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003600.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_003600.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_003600.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_003600.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_003600.]: Entered 'hyperbolic_regression'-Function [20250519_003600.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003600.]: Entered 'cubic_regression'-Function [20250519_003600.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003600.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_003600.]: Logging df_agg: CpG#8 [20250519_003600.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003600.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_003600.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_003600.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_003600.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_003600.]: Entered 'hyperbolic_regression'-Function [20250519_003600.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003600.]: Entered 'cubic_regression'-Function [20250519_003600.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003600.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_003600.]: Logging df_agg: CpG#9 [20250519_003600.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003600.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_003600.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_003600.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_003600.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_003600.]: Entered 'hyperbolic_regression'-Function [20250519_003600.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003601.]: Entered 'cubic_regression'-Function [20250519_003601.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003601.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_003601.]: Logging df_agg: row_means [20250519_003601.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003601.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_003601.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_003601.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_003601.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_003601.]: Entered 'hyperbolic_regression'-Function [20250519_003601.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003601.]: Entered 'cubic_regression'-Function [20250519_003601.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003602.]: ### Starting with regression calculations ### [20250519_003602.]: Entered 'regression_type1'-Function [20250519_003603.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_003603.]: Logging df_agg: CpG#1 [20250519_003603.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003603.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_003603.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_003603.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_003603.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_003603.]: Entered 'hyperbolic_regression'-Function [20250519_003603.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003603.]: Entered 'cubic_regression'-Function [20250519_003603.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003603.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_003603.]: Logging df_agg: CpG#2 [20250519_003603.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003603.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_003603.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_003603.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_003603.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_003603.]: Entered 'hyperbolic_regression'-Function [20250519_003603.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003603.]: Entered 'cubic_regression'-Function [20250519_003603.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003603.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_003603.]: Logging df_agg: CpG#3 [20250519_003603.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003603.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_003603.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_003603.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_003603.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_003603.]: Entered 'hyperbolic_regression'-Function [20250519_003603.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003603.]: Entered 'cubic_regression'-Function [20250519_003603.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003603.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_003603.]: Logging df_agg: CpG#4 [20250519_003603.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003603.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_003603.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_003603.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_003603.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_003603.]: Entered 'hyperbolic_regression'-Function [20250519_003603.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003603.]: Entered 'cubic_regression'-Function [20250519_003603.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003603.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_003603.]: Logging df_agg: CpG#5 [20250519_003603.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003603.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_003603.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_003603.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_003603.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_003603.]: Entered 'hyperbolic_regression'-Function [20250519_003603.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003603.]: Entered 'cubic_regression'-Function [20250519_003603.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003603.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_003603.]: Logging df_agg: CpG#6 [20250519_003603.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003603.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_003603.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_003603.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_003603.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_003603.]: Entered 'hyperbolic_regression'-Function [20250519_003603.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003603.]: Entered 'cubic_regression'-Function [20250519_003603.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003603.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_003603.]: Logging df_agg: CpG#7 [20250519_003603.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003603.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_003603.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_003603.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_003603.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_003603.]: Entered 'hyperbolic_regression'-Function [20250519_003603.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003603.]: Entered 'cubic_regression'-Function [20250519_003603.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003603.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_003603.]: Logging df_agg: CpG#8 [20250519_003603.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003603.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_003603.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_003603.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_003603.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_003603.]: Entered 'hyperbolic_regression'-Function [20250519_003603.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003603.]: Entered 'cubic_regression'-Function [20250519_003603.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003603.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_003603.]: Logging df_agg: CpG#9 [20250519_003603.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003603.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_003603.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_003603.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_003603.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_003603.]: Entered 'hyperbolic_regression'-Function [20250519_003603.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003603.]: Entered 'cubic_regression'-Function [20250519_003603.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003603.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_003603.]: Logging df_agg: row_means [20250519_003603.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003603.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_003603.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_003603.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_003603.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_003603.]: Entered 'hyperbolic_regression'-Function [20250519_003603.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003603.]: Entered 'cubic_regression'-Function [20250519_003603.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003605.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_003605.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_003605.]: Logging df_agg: CpG#1 [20250519_003605.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003605.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_003605.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_003605.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_003605.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_003605.]: Entered 'hyperbolic_regression'-Function [20250519_003605.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003605.]: Entered 'cubic_regression'-Function [20250519_003605.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003606.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_003606.]: Logging df_agg: CpG#2 [20250519_003606.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003606.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_003606.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_003606.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_003606.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_003606.]: Entered 'hyperbolic_regression'-Function [20250519_003606.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003606.]: Entered 'cubic_regression'-Function [20250519_003606.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003606.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_003606.]: Logging df_agg: CpG#3 [20250519_003606.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003606.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_003606.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_003606.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_003606.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_003606.]: Entered 'hyperbolic_regression'-Function [20250519_003606.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003606.]: Entered 'cubic_regression'-Function [20250519_003606.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003606.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_003606.]: Logging df_agg: CpG#4 [20250519_003606.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003606.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_003606.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_003606.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_003606.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_003606.]: Entered 'hyperbolic_regression'-Function [20250519_003606.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003607.]: Entered 'cubic_regression'-Function [20250519_003607.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003607.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_003607.]: Logging df_agg: CpG#5 [20250519_003607.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003607.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_003607.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_003607.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_003607.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_003607.]: Entered 'hyperbolic_regression'-Function [20250519_003607.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003607.]: Entered 'cubic_regression'-Function [20250519_003607.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003607.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_003607.]: Logging df_agg: CpG#6 [20250519_003607.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003607.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_003607.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_003607.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_003607.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_003607.]: Entered 'hyperbolic_regression'-Function [20250519_003607.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003607.]: Entered 'cubic_regression'-Function [20250519_003607.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003607.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_003607.]: Logging df_agg: CpG#7 [20250519_003607.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003607.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_003607.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_003607.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_003607.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_003607.]: Entered 'hyperbolic_regression'-Function [20250519_003607.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003608.]: Entered 'cubic_regression'-Function [20250519_003608.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003608.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_003608.]: Logging df_agg: CpG#8 [20250519_003608.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003608.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_003608.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_003608.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_003608.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_003608.]: Entered 'hyperbolic_regression'-Function [20250519_003608.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003608.]: Entered 'cubic_regression'-Function [20250519_003608.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003608.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_003608.]: Logging df_agg: CpG#9 [20250519_003608.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003608.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_003608.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_003608.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_003608.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_003608.]: Entered 'hyperbolic_regression'-Function [20250519_003608.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003608.]: Entered 'cubic_regression'-Function [20250519_003608.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003608.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_003608.]: Logging df_agg: row_means [20250519_003608.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003608.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_003608.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_003608.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_003608.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_003608.]: Entered 'hyperbolic_regression'-Function [20250519_003608.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003609.]: Entered 'cubic_regression'-Function [20250519_003609.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003609.]: Entered 'clean_dt'-Function [20250519_003610.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_003610.]: got experimental data [20250519_003610.]: Entered 'clean_dt'-Function [20250519_003610.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_003610.]: got calibration data [20250519_003610.]: ### Starting with regression calculations ### [20250519_003610.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_003610.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_003610.]: Logging df_agg: CpG#1 [20250519_003610.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003610.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_003610.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_003610.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_003610.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_003610.]: Entered 'hyperbolic_regression'-Function [20250519_003610.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003610.]: Entered 'cubic_regression'-Function [20250519_003610.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003610.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_003610.]: Logging df_agg: CpG#2 [20250519_003610.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003610.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_003610.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_003610.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_003610.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_003610.]: Entered 'hyperbolic_regression'-Function [20250519_003610.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003610.]: Entered 'cubic_regression'-Function [20250519_003610.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003610.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_003610.]: Logging df_agg: CpG#3 [20250519_003610.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003610.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_003610.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_003610.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_003610.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_003610.]: Entered 'hyperbolic_regression'-Function [20250519_003610.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003611.]: Entered 'cubic_regression'-Function [20250519_003611.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003611.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_003611.]: Logging df_agg: CpG#4 [20250519_003611.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003611.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_003611.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_003611.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_003611.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_003611.]: Entered 'hyperbolic_regression'-Function [20250519_003611.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003611.]: Entered 'cubic_regression'-Function [20250519_003611.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003611.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_003611.]: Logging df_agg: CpG#5 [20250519_003611.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003611.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_003611.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_003611.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_003611.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_003611.]: Entered 'hyperbolic_regression'-Function [20250519_003611.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003611.]: Entered 'cubic_regression'-Function [20250519_003611.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003612.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_003612.]: Logging df_agg: CpG#6 [20250519_003612.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003612.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_003612.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_003612.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_003612.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_003612.]: Entered 'hyperbolic_regression'-Function [20250519_003612.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003612.]: Entered 'cubic_regression'-Function [20250519_003612.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003612.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_003612.]: Logging df_agg: CpG#7 [20250519_003612.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003612.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_003612.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_003612.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_003612.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_003612.]: Entered 'hyperbolic_regression'-Function [20250519_003612.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003612.]: Entered 'cubic_regression'-Function [20250519_003612.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003612.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_003612.]: Logging df_agg: CpG#8 [20250519_003612.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003612.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_003612.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_003612.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_003612.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_003612.]: Entered 'hyperbolic_regression'-Function [20250519_003612.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003613.]: Entered 'cubic_regression'-Function [20250519_003613.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003613.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_003613.]: Logging df_agg: CpG#9 [20250519_003613.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003613.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_003613.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_003613.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_003613.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_003613.]: Entered 'hyperbolic_regression'-Function [20250519_003613.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003613.]: Entered 'cubic_regression'-Function [20250519_003613.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003613.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_003613.]: Logging df_agg: row_means [20250519_003613.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003613.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_003613.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_003613.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_003613.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_003613.]: Entered 'hyperbolic_regression'-Function [20250519_003613.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003613.]: Entered 'cubic_regression'-Function [20250519_003613.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003614.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_003614.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_003614.]: Logging df_agg: CpG#1 [20250519_003614.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003614.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_003614.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_003614.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_003614.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_003614.]: Entered 'hyperbolic_regression'-Function [20250519_003614.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003615.]: Entered 'cubic_regression'-Function [20250519_003615.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003615.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_003615.]: Logging df_agg: CpG#2 [20250519_003615.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003615.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_003615.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_003615.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_003615.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_003615.]: Entered 'hyperbolic_regression'-Function [20250519_003615.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003615.]: Entered 'cubic_regression'-Function [20250519_003615.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003615.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_003615.]: Logging df_agg: CpG#3 [20250519_003615.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003615.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_003615.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_003615.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_003615.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_003615.]: Entered 'hyperbolic_regression'-Function [20250519_003615.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003615.]: Entered 'cubic_regression'-Function [20250519_003615.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003615.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_003615.]: Logging df_agg: CpG#4 [20250519_003615.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003615.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_003615.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_003615.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_003615.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_003615.]: Entered 'hyperbolic_regression'-Function [20250519_003615.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003616.]: Entered 'cubic_regression'-Function [20250519_003616.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003616.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_003616.]: Logging df_agg: CpG#5 [20250519_003616.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003616.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_003616.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_003616.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_003616.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_003616.]: Entered 'hyperbolic_regression'-Function [20250519_003616.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003616.]: Entered 'cubic_regression'-Function [20250519_003616.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003616.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_003616.]: Logging df_agg: CpG#6 [20250519_003616.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003616.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_003616.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_003616.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_003616.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_003616.]: Entered 'hyperbolic_regression'-Function [20250519_003616.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003617.]: Entered 'cubic_regression'-Function [20250519_003617.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003617.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_003617.]: Logging df_agg: CpG#7 [20250519_003617.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003617.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_003617.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_003617.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_003617.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_003617.]: Entered 'hyperbolic_regression'-Function [20250519_003617.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003617.]: Entered 'cubic_regression'-Function [20250519_003617.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003617.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_003617.]: Logging df_agg: CpG#8 [20250519_003617.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003617.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_003617.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_003617.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_003617.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_003617.]: Entered 'hyperbolic_regression'-Function [20250519_003617.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003617.]: Entered 'cubic_regression'-Function [20250519_003617.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003617.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_003617.]: Logging df_agg: CpG#9 [20250519_003617.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003617.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_003617.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_003617.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_003617.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_003617.]: Entered 'hyperbolic_regression'-Function [20250519_003617.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003618.]: Entered 'cubic_regression'-Function [20250519_003618.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003618.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_003618.]: Logging df_agg: row_means [20250519_003618.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003618.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_003618.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_003618.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_003618.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_003618.]: Entered 'hyperbolic_regression'-Function [20250519_003618.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003618.]: Entered 'cubic_regression'-Function [20250519_003618.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003619.]: Entered 'solving_equations'-Function [20250519_003619.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 0 [20250519_003619.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 14.1480167109277 [20250519_003619.]: Samplename: 12.5 Root: 14.148 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1398443790446 [20250519_003619.]: Samplename: 25 Root: 26.14 --> Root in between the borders! Added to results. Hyperbolic solved: 39.3762039523382 [20250519_003619.]: Samplename: 37.5 Root: 39.376 --> Root in between the borders! Added to results. Hyperbolic solved: 52.9476244028783 [20250519_003619.]: Samplename: 50 Root: 52.948 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4195105873419 [20250519_003619.]: Samplename: 62.5 Root: 65.42 --> Root in between the borders! Added to results. Hyperbolic solved: 74.433837698331 [20250519_003619.]: Samplename: 75 Root: 74.434 --> Root in between the borders! Added to results. Hyperbolic solved: 80.5559264925808 [20250519_003619.]: Samplename: 87.5 Root: 80.556 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_003619.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_003619.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 0 [20250519_003619.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 10.7812303822548 [20250519_003619.]: Samplename: 12.5 Root: 10.781 --> Root in between the borders! Added to results. Hyperbolic solved: 26.0648314401535 [20250519_003619.]: Samplename: 25 Root: 26.065 --> Root in between the borders! Added to results. Hyperbolic solved: 35.1980950192158 [20250519_003619.]: Samplename: 37.5 Root: 35.198 --> Root in between the borders! Added to results. Hyperbolic solved: 47.9203836090837 [20250519_003619.]: Samplename: 50 Root: 47.92 --> Root in between the borders! Added to results. Hyperbolic solved: 67.275750545092 [20250519_003619.]: Samplename: 62.5 Root: 67.276 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7278143684244 [20250519_003619.]: Samplename: 75 Root: 75.728 --> Root in between the borders! Added to results. Hyperbolic solved: 84.1258428690749 [20250519_003619.]: Samplename: 87.5 Root: 84.126 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_003619.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_003619.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 0 [20250519_003619.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 10.8467198180169 [20250519_003619.]: Samplename: 12.5 Root: 10.847 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1449700612555 [20250519_003619.]: Samplename: 25 Root: 26.145 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2865760013773 [20250519_003619.]: Samplename: 37.5 Root: 37.287 --> Root in between the borders! Added to results. Hyperbolic solved: 51.4114078772697 [20250519_003619.]: Samplename: 50 Root: 51.411 --> Root in between the borders! Added to results. Hyperbolic solved: 65.0139634978039 [20250519_003619.]: Samplename: 62.5 Root: 65.014 --> Root in between the borders! Added to results. Hyperbolic solved: 76.9921394830877 [20250519_003619.]: Samplename: 75 Root: 76.992 --> Root in between the borders! Added to results. Hyperbolic solved: 79.6808818589171 [20250519_003619.]: Samplename: 87.5 Root: 79.681 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_003619.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_003619.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 0 [20250519_003619.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 13.2494913489925 [20250519_003619.]: Samplename: 12.5 Root: 13.249 --> Root in between the borders! Added to results. Hyperbolic solved: 25.0914701740206 [20250519_003619.]: Samplename: 25 Root: 25.091 --> Root in between the borders! Added to results. Hyperbolic solved: 38.8081740444332 [20250519_003619.]: Samplename: 37.5 Root: 38.808 --> Root in between the borders! Added to results. Hyperbolic solved: 49.1133034150896 [20250519_003619.]: Samplename: 50 Root: 49.113 --> Root in between the borders! Added to results. Hyperbolic solved: 67.573566028728 [20250519_003619.]: Samplename: 62.5 Root: 67.574 --> Root in between the borders! Added to results. Hyperbolic solved: 73.7655861480302 [20250519_003619.]: Samplename: 75 Root: 73.766 --> Root in between the borders! Added to results. Hyperbolic solved: 82.0404940898918 [20250519_003619.]: Samplename: 87.5 Root: 82.04 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_003619.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_003619.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 0 [20250519_003619.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 8.36101271071658 [20250519_003619.]: Samplename: 12.5 Root: 8.361 --> Root in between the borders! Added to results. Hyperbolic solved: 23.0724671456609 [20250519_003619.]: Samplename: 25 Root: 23.072 --> Root in between the borders! Added to results. Hyperbolic solved: 36.9926902689013 [20250519_003619.]: Samplename: 37.5 Root: 36.993 --> Root in between the borders! Added to results. Hyperbolic solved: 50.9902012556783 [20250519_003619.]: Samplename: 50 Root: 50.99 --> Root in between the borders! Added to results. Hyperbolic solved: 62.7269420395599 [20250519_003619.]: Samplename: 62.5 Root: 62.727 --> Root in between the borders! Added to results. Hyperbolic solved: 76.6726263557269 [20250519_003619.]: Samplename: 75 Root: 76.673 --> Root in between the borders! Added to results. Hyperbolic solved: 86.2959101486824 [20250519_003619.]: Samplename: 87.5 Root: 86.296 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_003619.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_003619.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 0 [20250519_003619.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 11.8139244542506 [20250519_003619.]: Samplename: 12.5 Root: 11.814 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5330423964606 [20250519_003619.]: Samplename: 25 Root: 26.533 --> Root in between the borders! Added to results. Hyperbolic solved: 35.365455365759 [20250519_003619.]: Samplename: 37.5 Root: 35.365 --> Root in between the borders! Added to results. Hyperbolic solved: 50.1054345020203 [20250519_003619.]: Samplename: 50 Root: 50.105 --> Root in between the borders! Added to results. Hyperbolic solved: 64.9683746571925 [20250519_003619.]: Samplename: 62.5 Root: 64.968 --> Root in between the borders! Added to results. Hyperbolic solved: 73.6331726521876 [20250519_003619.]: Samplename: 75 Root: 73.633 --> Root in between the borders! Added to results. Hyperbolic solved: 86.9938602772804 [20250519_003619.]: Samplename: 87.5 Root: 86.994 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_003619.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_003619.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 0 [20250519_003619.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 11.7880491100164 [20250519_003619.]: Samplename: 12.5 Root: 11.788 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1959234034328 [20250519_003619.]: Samplename: 25 Root: 26.196 --> Root in between the borders! Added to results. Hyperbolic solved: 39.1978567589215 [20250519_003619.]: Samplename: 37.5 Root: 39.198 --> Root in between the borders! Added to results. Hyperbolic solved: 54.3513505291649 [20250519_003619.]: Samplename: 50 Root: 54.351 --> Root in between the borders! Added to results. Hyperbolic solved: 66.0567953704493 [20250519_003619.]: Samplename: 62.5 Root: 66.057 --> Root in between the borders! Added to results. Hyperbolic solved: 75.1900867269869 [20250519_003619.]: Samplename: 75 Root: 75.19 --> Root in between the borders! Added to results. Hyperbolic solved: 78.6051660349008 [20250519_003619.]: Samplename: 87.5 Root: 78.605 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_003619.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_003619.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 0 [20250519_003619.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 7.27382484148278 [20250519_003619.]: Samplename: 12.5 Root: 7.274 --> Root in between the borders! Added to results. Hyperbolic solved: 24.9765598282779 [20250519_003619.]: Samplename: 25 Root: 24.977 --> Root in between the borders! Added to results. Hyperbolic solved: 34.028313807905 [20250519_003619.]: Samplename: 37.5 Root: 34.028 --> Root in between the borders! Added to results. Hyperbolic solved: 52.2946423509726 [20250519_003619.]: Samplename: 50 Root: 52.295 --> Root in between the borders! Added to results. Hyperbolic solved: 65.0742432627639 [20250519_003619.]: Samplename: 62.5 Root: 65.074 --> Root in between the borders! Added to results. Hyperbolic solved: 78.3047550247862 [20250519_003619.]: Samplename: 75 Root: 78.305 --> Root in between the borders! Added to results. Hyperbolic solved: 81.0501991298613 [20250519_003619.]: Samplename: 87.5 Root: 81.05 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_003619.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_003619.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 0 [20250519_003619.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 12.2068234201026 [20250519_003619.]: Samplename: 12.5 Root: 12.207 --> Root in between the borders! Added to results. Hyperbolic solved: 28.0688737636578 [20250519_003619.]: Samplename: 25 Root: 28.069 --> Root in between the borders! Added to results. Hyperbolic solved: 37.6661823177513 [20250519_003619.]: Samplename: 37.5 Root: 37.666 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3684233460174 [20250519_003619.]: Samplename: 50 Root: 52.368 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8636916649907 [20250519_003619.]: Samplename: 62.5 Root: 64.864 --> Root in between the borders! Added to results. Hyperbolic solved: 74.2551330907062 [20250519_003619.]: Samplename: 75 Root: 74.255 --> Root in between the borders! Added to results. Hyperbolic solved: 83.9343288872042 [20250519_003619.]: Samplename: 87.5 Root: 83.934 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_003619.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_003619.]: Solving hyperbolic regression for row_means Hyperbolic solved: 0.000228160853766634 [20250519_003619.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 11.1587839347378 [20250519_003619.]: Samplename: 12.5 Root: 11.159 --> Root in between the borders! Added to results. Hyperbolic solved: 25.8570776700364 [20250519_003619.]: Samplename: 25 Root: 25.857 --> Root in between the borders! Added to results. Hyperbolic solved: 37.065004454281 [20250519_003619.]: Samplename: 37.5 Root: 37.065 --> Root in between the borders! Added to results. Hyperbolic solved: 51.1881763075298 [20250519_003619.]: Samplename: 50 Root: 51.188 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4439754555105 [20250519_003619.]: Samplename: 62.5 Root: 65.444 --> Root in between the borders! Added to results. Hyperbolic solved: 75.3005883283082 [20250519_003619.]: Samplename: 75 Root: 75.301 --> Root in between the borders! Added to results. Hyperbolic solved: 82.6591025877361 [20250519_003619.]: Samplename: 87.5 Root: 82.659 --> Root in between the borders! Added to results. Hyperbolic solved: 100.000153506599 [20250519_003619.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 [20250519_003619.]: ### Starting with regression calculations ### [20250519_003619.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_003619.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 14.1480167109277, 26.1398443790446, 39.3762039523382, 52.9476244028783, 65.4195105873419, 74.433837698331, 80.5559264925808, 100)c(0, 1.6480167109277, 1.1398443790446, 1.8762039523382, 2.9476244028783, 2.9195105873419, 0.566162301668996, 6.9440735074192, 0)c(NA, 13.1841336874216, 4.5593775161784, 5.00321053956854, 5.89524880575659, 4.67121693974705, 0.754883068891995, 7.93608400847909, 0) [20250519_003620.]: Logging df_agg: CpG#1 [20250519_003620.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003620.]: c(0, 14.1480167109277, 26.1398443790446, 39.3762039523382, 52.9476244028783, 65.4195105873419, 74.433837698331, 80.5559264925808, 100)[20250519_003620.]: c(0, 1.6480167109277, 1.1398443790446, 1.8762039523382, 2.9476244028783, 2.9195105873419, 0.566162301668996, 6.9440735074192, 0)[20250519_003620.]: c(NA, 13.1841336874216, 4.5593775161784, 5.00321053956854, 5.89524880575659, 4.67121693974705, 0.754883068891995, 7.93608400847909, 0) [20250519_003620.]: Entered 'hyperbolic_regression'-Function [20250519_003620.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003620.]: Entered 'cubic_regression'-Function [20250519_003620.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003620.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.7812303822548, 26.0648314401535, 35.1980950192158, 47.9203836090837, 67.275750545092, 75.7278143684244, 84.1258428690749, 100)c(0, 1.7187696177452, 1.0648314401535, 2.3019049807842, 2.0796163909163, 4.775750545092, 0.727814368424404, 3.3741571309251, 0)c(NA, 13.7501569419616, 4.259325760614, 6.1384132820912, 4.1592327818326, 7.64120087214719, 0.970419157899206, 3.85617957820011, 0) [20250519_003620.]: Logging df_agg: CpG#2 [20250519_003620.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003620.]: c(0, 10.7812303822548, 26.0648314401535, 35.1980950192158, 47.9203836090837, 67.275750545092, 75.7278143684244, 84.1258428690749, 100)[20250519_003620.]: c(0, 1.7187696177452, 1.0648314401535, 2.3019049807842, 2.0796163909163, 4.775750545092, 0.727814368424404, 3.3741571309251, 0)[20250519_003620.]: c(NA, 13.7501569419616, 4.259325760614, 6.1384132820912, 4.1592327818326, 7.64120087214719, 0.970419157899206, 3.85617957820011, 0) [20250519_003620.]: Entered 'hyperbolic_regression'-Function [20250519_003620.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003620.]: Entered 'cubic_regression'-Function [20250519_003620.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003620.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.8467198180169, 26.1449700612555, 37.2865760013773, 51.4114078772697, 65.0139634978039, 76.9921394830877, 79.6808818589171, 100)c(0, 1.6532801819831, 1.1449700612555, 0.213423998622702, 1.4114078772697, 2.5139634978039, 1.9921394830877, 7.8191181410829, 0)c(NA, 13.2262414558648, 4.579880245022, 0.569130662993871, 2.82281575453941, 4.02234159648624, 2.65618597745026, 8.93613501838045, 0) [20250519_003620.]: Logging df_agg: CpG#3 [20250519_003620.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003620.]: c(0, 10.8467198180169, 26.1449700612555, 37.2865760013773, 51.4114078772697, 65.0139634978039, 76.9921394830877, 79.6808818589171, 100)[20250519_003620.]: c(0, 1.6532801819831, 1.1449700612555, 0.213423998622702, 1.4114078772697, 2.5139634978039, 1.9921394830877, 7.8191181410829, 0)[20250519_003620.]: c(NA, 13.2262414558648, 4.579880245022, 0.569130662993871, 2.82281575453941, 4.02234159648624, 2.65618597745026, 8.93613501838045, 0) [20250519_003620.]: Entered 'hyperbolic_regression'-Function [20250519_003620.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003621.]: Entered 'cubic_regression'-Function [20250519_003621.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003621.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 13.2494913489925, 25.0914701740206, 38.8081740444332, 49.1133034150896, 67.573566028728, 73.7655861480302, 82.0404940898918, 100)c(0, 0.7494913489925, 0.0914701740205999, 1.3081740444332, 0.886696584910403, 5.073566028728, 1.2344138519698, 5.45950591010821, 0)c(NA, 5.99593079194, 0.365880696082399, 3.48846411848854, 1.77339316982081, 8.11770564596479, 1.64588513595974, 6.23943532583795, 0) [20250519_003621.]: Logging df_agg: CpG#4 [20250519_003621.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003621.]: c(0, 13.2494913489925, 25.0914701740206, 38.8081740444332, 49.1133034150896, 67.573566028728, 73.7655861480302, 82.0404940898918, 100)[20250519_003621.]: c(0, 0.7494913489925, 0.0914701740205999, 1.3081740444332, 0.886696584910403, 5.073566028728, 1.2344138519698, 5.45950591010821, 0)[20250519_003621.]: c(NA, 5.99593079194, 0.365880696082399, 3.48846411848854, 1.77339316982081, 8.11770564596479, 1.64588513595974, 6.23943532583795, 0) [20250519_003621.]: Entered 'hyperbolic_regression'-Function [20250519_003621.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003621.]: Entered 'cubic_regression'-Function [20250519_003621.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003621.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.36101271071658, 23.0724671456609, 36.9926902689013, 50.9902012556783, 62.7269420395599, 76.6726263557269, 86.2959101486824, 100)c(0, 4.13898728928342, 1.9275328543391, 0.507309731098701, 0.990201255678301, 0.226942039559901, 1.6726263557269, 1.2040898513176, 0)c(NA, 33.1118983142674, 7.71013141735639, 1.35282594959654, 1.9804025113566, 0.363107263295842, 2.23016847430254, 1.37610268722012, 0) [20250519_003621.]: Logging df_agg: CpG#5 [20250519_003621.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003621.]: c(0, 8.36101271071658, 23.0724671456609, 36.9926902689013, 50.9902012556783, 62.7269420395599, 76.6726263557269, 86.2959101486824, 100)[20250519_003621.]: c(0, 4.13898728928342, 1.9275328543391, 0.507309731098701, 0.990201255678301, 0.226942039559901, 1.6726263557269, 1.2040898513176, 0)[20250519_003621.]: c(NA, 33.1118983142674, 7.71013141735639, 1.35282594959654, 1.9804025113566, 0.363107263295842, 2.23016847430254, 1.37610268722012, 0) [20250519_003621.]: Entered 'hyperbolic_regression'-Function [20250519_003621.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003621.]: Entered 'cubic_regression'-Function [20250519_003621.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003622.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.8139244542506, 26.5330423964606, 35.365455365759, 50.1054345020203, 64.9683746571925, 73.6331726521876, 86.9938602772804, 100)c(0, 0.6860755457494, 1.5330423964606, 2.134544634241, 0.105434502020302, 2.4683746571925, 1.3668273478124, 0.506139722719595, 0)c(NA, 5.4886043659952, 6.13216958584241, 5.69211902464267, 0.210869004040603, 3.949399451508, 1.82243646374987, 0.578445397393823, 0) [20250519_003622.]: Logging df_agg: CpG#6 [20250519_003622.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003622.]: c(0, 11.8139244542506, 26.5330423964606, 35.365455365759, 50.1054345020203, 64.9683746571925, 73.6331726521876, 86.9938602772804, 100)[20250519_003622.]: c(0, 0.6860755457494, 1.5330423964606, 2.134544634241, 0.105434502020302, 2.4683746571925, 1.3668273478124, 0.506139722719595, 0)[20250519_003622.]: c(NA, 5.4886043659952, 6.13216958584241, 5.69211902464267, 0.210869004040603, 3.949399451508, 1.82243646374987, 0.578445397393823, 0) [20250519_003622.]: Entered 'hyperbolic_regression'-Function [20250519_003622.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003622.]: Entered 'cubic_regression'-Function [20250519_003622.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003622.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.7880491100164, 26.1959234034328, 39.1978567589215, 54.3513505291649, 66.0567953704493, 75.1900867269869, 78.6051660349008, 100)c(0, 0.7119508899836, 1.1959234034328, 1.6978567589215, 4.3513505291649, 3.55679537044929, 0.190086726986905, 8.8948339650992, 0)c(NA, 5.6956071198688, 4.7836936137312, 4.52761802379068, 8.70270105832979, 5.69087259271887, 0.253448969315874, 10.1655245315419, 0) [20250519_003622.]: Logging df_agg: CpG#7 [20250519_003622.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003622.]: c(0, 11.7880491100164, 26.1959234034328, 39.1978567589215, 54.3513505291649, 66.0567953704493, 75.1900867269869, 78.6051660349008, 100)[20250519_003622.]: c(0, 0.7119508899836, 1.1959234034328, 1.6978567589215, 4.3513505291649, 3.55679537044929, 0.190086726986905, 8.8948339650992, 0)[20250519_003622.]: c(NA, 5.6956071198688, 4.7836936137312, 4.52761802379068, 8.70270105832979, 5.69087259271887, 0.253448969315874, 10.1655245315419, 0) [20250519_003622.]: Entered 'hyperbolic_regression'-Function [20250519_003622.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003622.]: Entered 'cubic_regression'-Function [20250519_003622.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003622.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 7.27382484148278, 24.9765598282779, 34.028313807905, 52.2946423509726, 65.0742432627639, 78.3047550247862, 81.0501991298613, 100)c(0, 5.22617515851722, 0.0234401717221004, 3.471686192095, 2.2946423509726, 2.5742432627639, 3.3047550247862, 6.4498008701387, 0)c(NA, 41.8094012681378, 0.0937606868884018, 9.25782984558666, 4.5892847019452, 4.11878922042224, 4.40634003304827, 7.37120099444423, 0) [20250519_003622.]: Logging df_agg: CpG#8 [20250519_003622.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003622.]: c(0, 7.27382484148278, 24.9765598282779, 34.028313807905, 52.2946423509726, 65.0742432627639, 78.3047550247862, 81.0501991298613, 100)[20250519_003622.]: c(0, 5.22617515851722, 0.0234401717221004, 3.471686192095, 2.2946423509726, 2.5742432627639, 3.3047550247862, 6.4498008701387, 0)[20250519_003622.]: c(NA, 41.8094012681378, 0.0937606868884018, 9.25782984558666, 4.5892847019452, 4.11878922042224, 4.40634003304827, 7.37120099444423, 0) [20250519_003622.]: Entered 'hyperbolic_regression'-Function [20250519_003622.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003623.]: Entered 'cubic_regression'-Function [20250519_003623.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003623.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.2068234201026, 28.0688737636578, 37.6661823177513, 52.3684233460174, 64.8636916649907, 74.2551330907062, 83.9343288872042, 100)c(0, 0.2931765798974, 3.0688737636578, 0.1661823177513, 2.3684233460174, 2.3636916649907, 0.744866909293805, 3.5656711127958, 0)c(NA, 2.3454126391792, 12.2754950546312, 0.4431528473368, 4.7368466920348, 3.78190666398511, 0.993155879058406, 4.07505270033805, 0) [20250519_003623.]: Logging df_agg: CpG#9 [20250519_003623.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003623.]: c(0, 12.2068234201026, 28.0688737636578, 37.6661823177513, 52.3684233460174, 64.8636916649907, 74.2551330907062, 83.9343288872042, 100)[20250519_003623.]: c(0, 0.2931765798974, 3.0688737636578, 0.1661823177513, 2.3684233460174, 2.3636916649907, 0.744866909293805, 3.5656711127958, 0)[20250519_003623.]: c(NA, 2.3454126391792, 12.2754950546312, 0.4431528473368, 4.7368466920348, 3.78190666398511, 0.993155879058406, 4.07505270033805, 0) [20250519_003623.]: Entered 'hyperbolic_regression'-Function [20250519_003623.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003623.]: Entered 'cubic_regression'-Function [20250519_003623.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003623.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.000228160853766634, 11.1587839347378, 25.8570776700364, 37.065004454281, 51.1881763075298, 65.4439754555105, 75.3005883283082, 82.6591025877361, 100)c(0.000228160853766634, 1.3412160652622, 0.8570776700364, 0.434995545718998, 1.1881763075298, 2.9439754555105, 0.300588328308194, 4.8408974122639, 0)c(NA, 10.7297285220976, 3.4283106801456, 1.15998812191733, 2.3763526150596, 4.7103607288168, 0.400784437744259, 5.53245418544446, 0) [20250519_003623.]: Logging df_agg: row_means [20250519_003623.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003623.]: c(0.000228160853766634, 11.1587839347378, 25.8570776700364, 37.065004454281, 51.1881763075298, 65.4439754555105, 75.3005883283082, 82.6591025877361, 100)[20250519_003623.]: c(0.000228160853766634, 1.3412160652622, 0.8570776700364, 0.434995545718998, 1.1881763075298, 2.9439754555105, 0.300588328308194, 4.8408974122639, 0)[20250519_003623.]: c(NA, 10.7297285220976, 3.4283106801456, 1.15998812191733, 2.3763526150596, 4.7103607288168, 0.400784437744259, 5.53245418544446, 0) [20250519_003623.]: Entered 'hyperbolic_regression'-Function [20250519_003623.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003623.]: Entered 'cubic_regression'-Function [20250519_003623.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003624.]: Entered 'solving_equations'-Function [20250519_003624.]: Solving cubic regression for CpG#1 Coefficients: 0Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20250519_003624.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -7.30533333333333Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20250519_003624.]: Samplename: 12.5 Root: 4.661 --> Root in between the borders! Added to results. Coefficients: -14.352Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20250519_003624.]: Samplename: 25 Root: 9.352 --> Root in between the borders! Added to results. Coefficients: -23.244Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20250519_003624.]: Samplename: 37.5 Root: 15.59 --> Root in between the borders! Added to results. Coefficients: -33.8645Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20250519_003624.]: Samplename: 50 Root: 23.601 --> Root in between the borders! Added to results. Coefficients: -45.318Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20250519_003624.]: Samplename: 62.5 Root: 33.121 --> Root in between the borders! Added to results. Coefficients: -54.857Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20250519_003624.]: Samplename: 75 Root: 42.001 --> Root in between the borders! Added to results. Coefficients: -62.062Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20250519_003624.]: Samplename: 87.5 Root: 49.51 --> Root in between the borders! Added to results. Coefficients: -90.01Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20250519_003624.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 [20250519_003624.]: Solving cubic regression for CpG#2 Coefficients: 0Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20250519_003624.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -6.05666666666666Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20250519_003624.]: Samplename: 12.5 Root: 8.415 --> Root in between the borders! Added to results. Coefficients: -15.656Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20250519_003624.]: Samplename: 25 Root: 21.016 --> Root in between the borders! Added to results. Coefficients: -22.054Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20250519_003624.]: Samplename: 37.5 Root: 28.984 --> Root in between the borders! Added to results. Coefficients: -31.945Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20250519_003624.]: Samplename: 50 Root: 40.726 --> Root in between the borders! Added to results. Coefficients: -49.68Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20250519_003624.]: Samplename: 62.5 Root: 60.322 --> Root in between the borders! Added to results. Coefficients: -58.6825Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20250519_003624.]: Samplename: 75 Root: 69.671 --> Root in between the borders! Added to results. Coefficients: -68.5533333333333Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20250519_003624.]: Samplename: 87.5 Root: 79.529 --> Root in between the borders! Added to results. Coefficients: -90.294Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20250519_003624.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 [20250519_003624.]: Solving cubic regression for CpG#3 Coefficients: 0Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20250519_003624.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -5.67Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20250519_003624.]: Samplename: 12.5 Root: 5.75 --> Root in between the borders! Added to results. Coefficients: -14.526Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20250519_003624.]: Samplename: 25 Root: 15.012 --> Root in between the borders! Added to results. Coefficients: -21.71Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20250519_003624.]: Samplename: 37.5 Root: 22.804 --> Root in between the borders! Added to results. Coefficients: -31.8725Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20250519_003624.]: Samplename: 50 Root: 34.308 --> Root in between the borders! Added to results. Coefficients: -42.986Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20250519_003624.]: Samplename: 62.5 Root: 47.638 --> Root in between the borders! Added to results. Coefficients: -54.0725Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20250519_003624.]: Samplename: 75 Root: 61.877 --> Root in between the borders! Added to results. Coefficients: -56.7533333333333Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20250519_003624.]: Samplename: 87.5 Root: 65.486 --> Root in between the borders! Added to results. Coefficients: -79.762Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20250519_003624.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 [20250519_003624.]: Solving cubic regression for CpG#4 Coefficients: 0Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20250519_003624.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -7.65533333333333Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20250519_003624.]: Samplename: 12.5 Root: 6.614 --> Root in between the borders! Added to results. Coefficients: -15.206Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20250519_003624.]: Samplename: 25 Root: 13.372 --> Root in between the borders! Added to results. Coefficients: -24.93Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20250519_003624.]: Samplename: 37.5 Root: 22.462 --> Root in between the borders! Added to results. Coefficients: -33.0395Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20250519_003624.]: Samplename: 50 Root: 30.424 --> Root in between the borders! Added to results. Coefficients: -49.658Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20250519_003624.]: Samplename: 62.5 Root: 48.07 --> Root in between the borders! Added to results. Coefficients: -55.942Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20250519_003624.]: Samplename: 75 Root: 55.318 --> Root in between the borders! Added to results. Coefficients: -64.9953333333333Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20250519_003624.]: Samplename: 87.5 Root: 66.469 --> Root in between the borders! Added to results. Coefficients: -87.724Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20250519_003624.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_003624.]: Solving cubic regression for CpG#5 Coefficients: 0Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_003624.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -4.144Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_003624.]: Samplename: 12.5 Root: 10.457 --> Root in between the borders! Added to results. Coefficients: -12.102Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_003624.]: Samplename: 25 Root: 26.323 --> Root in between the borders! Added to results. Coefficients: -20.536Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_003624.]: Samplename: 37.5 Root: 39.937 --> Root in between the borders! Added to results. Coefficients: -30.0715Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_003624.]: Samplename: 50 Root: 53.063 --> Root in between the borders! Added to results. Coefficients: -39.034Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_003624.]: Samplename: 62.5 Root: 63.959 --> Root in between the borders! Added to results. Coefficients: -51.059Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_003624.]: Samplename: 75 Root: 77.051 --> Root in between the borders! Added to results. Coefficients: -60.3906666666667Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_003624.]: Samplename: 87.5 Root: 86.31 --> Root in between the borders! Added to results. Coefficients: -75.446Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_003624.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_003624.]: Solving cubic regression for CpG#6 Coefficients: 0Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20250519_003624.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -6.54266666666667Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20250519_003624.]: Samplename: 12.5 Root: 8.15 --> Root in between the borders! Added to results. Coefficients: -15.692Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20250519_003624.]: Samplename: 25 Root: 19.28 --> Root in between the borders! Added to results. Coefficients: -21.804Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20250519_003624.]: Samplename: 37.5 Root: 26.553 --> Root in between the borders! Added to results. Coefficients: -33.2485Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20250519_003624.]: Samplename: 50 Root: 39.845 --> Root in between the borders! Added to results. Coefficients: -46.704Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20250519_003624.]: Samplename: 62.5 Root: 54.973 --> Root in between the borders! Added to results. Coefficients: -55.636Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20250519_003624.]: Samplename: 75 Root: 64.743 --> Root in between the borders! Added to results. Coefficients: -71.3493333333333Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20250519_003624.]: Samplename: 87.5 Root: 81.445 --> Root in between the borders! Added to results. Coefficients: -89.46Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20250519_003624.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_003624.]: Solving cubic regression for CpG#7 Coefficients: 0Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20250519_003624.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -4.18066666666667Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20250519_003624.]: Samplename: 12.5 Root: 3.662 --> Root in between the borders! Added to results. Coefficients: -10.05Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20250519_003624.]: Samplename: 25 Root: 9.013 --> Root in between the borders! Added to results. Coefficients: -16.236Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20250519_003624.]: Samplename: 37.5 Root: 14.96 --> Root in between the borders! Added to results. Coefficients: -24.8165Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20250519_003624.]: Samplename: 50 Root: 23.842 --> Root in between the borders! Added to results. Coefficients: -32.75Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20250519_003624.]: Samplename: 62.5 Root: 32.894 --> Root in between the borders! Added to results. Coefficients: -39.954Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20250519_003624.]: Samplename: 75 Root: 42.068 --> Root in between the borders! Added to results. Coefficients: -42.9206666666667Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20250519_003624.]: Samplename: 87.5 Root: 46.196 --> Root in between the borders! Added to results. Coefficients: -66.008Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20250519_003624.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_003624.]: Solving cubic regression for CpG#8 Coefficients: 0Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_003624.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -4.35066666666667Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_003624.]: Samplename: 12.5 Root: 8.961 --> Root in between the borders! Added to results. Coefficients: -15.834Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_003624.]: Samplename: 25 Root: 28.16 --> Root in between the borders! Added to results. Coefficients: -22.254Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_003624.]: Samplename: 37.5 Root: 37.189 --> Root in between the borders! Added to results. Coefficients: -36.529Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_003624.]: Samplename: 50 Root: 54.661 --> Root in between the borders! Added to results. Coefficients: -47.73Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_003624.]: Samplename: 62.5 Root: 66.642 --> Root in between the borders! Added to results. Coefficients: -60.5715Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_003624.]: Samplename: 75 Root: 79.079 --> Root in between the borders! Added to results. Coefficients: -63.414Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_003624.]: Samplename: 87.5 Root: 81.681 --> Root in between the borders! Added to results. Coefficients: -84.964Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_003624.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_003624.]: Solving cubic regression for CpG#9 Coefficients: 0Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20250519_003624.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -5.406Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20250519_003624.]: Samplename: 12.5 Root: 3.608 --> Root in between the borders! Added to results. Coefficients: -13.716Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20250519_003624.]: Samplename: 25 Root: 9.37 --> Root in between the borders! Added to results. Coefficients: -19.634Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20250519_003624.]: Samplename: 37.5 Root: 13.653 --> Root in between the borders! Added to results. Coefficients: -30.406Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20250519_003624.]: Samplename: 50 Root: 21.897 --> Root in between the borders! Added to results. Coefficients: -41.696Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20250519_003624.]: Samplename: 62.5 Root: 31.298 --> Root in between the borders! Added to results. Coefficients: -51.9135Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20250519_003624.]: Samplename: 75 Root: 40.689 --> Root in between the borders! Added to results. Coefficients: -64.5026666666667Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20250519_003624.]: Samplename: 87.5 Root: 53.907 --> Root in between the borders! Added to results. Coefficients: -92Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20250519_003624.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_003624.]: Solving cubic regression for row_means Coefficients: -0.000111111111110951Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20250519_003624.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -5.70137037037037Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20250519_003624.]: Samplename: 12.5 Root: 6.107 --> Root in between the borders! Added to results. Coefficients: -14.1261111111111Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20250519_003624.]: Samplename: 25 Root: 15.282 --> Root in between the borders! Added to results. Coefficients: -21.3781111111111Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20250519_003624.]: Samplename: 37.5 Root: 23.331 --> Root in between the borders! Added to results. Coefficients: -31.7548888888889Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20250519_003624.]: Samplename: 50 Root: 35.107 --> Root in between the borders! Added to results. Coefficients: -43.9507777777778Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20250519_003624.]: Samplename: 62.5 Root: 49.368 --> Root in between the borders! Added to results. Coefficients: -53.6321111111111Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20250519_003624.]: Samplename: 75 Root: 61.044 --> Root in between the borders! Added to results. Coefficients: -61.6602592592593Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20250519_003624.]: Samplename: 87.5 Root: 70.984 --> Root in between the borders! Added to results. Coefficients: -83.9632222222222Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20250519_003624.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 [20250519_003624.]: ### Starting with regression calculations ### [20250519_003624.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 [20250519_003625.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 4.66057108506072, 9.3520572622014, 15.5898391276712, 23.6006773278817, 33.1210812975699, 42.000790440049, 49.5097019721065, 100)c(0, 7.83942891493928, 15.6479427377986, 21.9101608723288, 26.3993226721183, 29.3789187024301, 32.999209559951, 37.9902980278935, 0)c(NA, 62.7154313195142, 62.5917709511944, 58.4270956595435, 52.7986453442366, 47.0062699238882, 43.9989460799347, 43.4174834604497, 0) [20250519_003625.]: Logging df_agg: CpG#1 [20250519_003625.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003625.]: c(0, 4.66057108506072, 9.3520572622014, 15.5898391276712, 23.6006773278817, 33.1210812975699, 42.000790440049, 49.5097019721065, 100)[20250519_003625.]: c(0, 7.83942891493928, 15.6479427377986, 21.9101608723288, 26.3993226721183, 29.3789187024301, 32.999209559951, 37.9902980278935, 0)[20250519_003625.]: c(NA, 62.7154313195142, 62.5917709511944, 58.4270956595435, 52.7986453442366, 47.0062699238882, 43.9989460799347, 43.4174834604497, 0) [20250519_003625.]: Entered 'hyperbolic_regression'-Function [20250519_003625.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003625.]: Entered 'cubic_regression'-Function [20250519_003625.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003625.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.41473135558285, 21.0155787157944, 28.98380769729, 40.7258283847219, 60.321752127534, 69.670868358929, 79.5285048174076, 100)c(0, 4.08526864441715, 3.9844212842056, 8.51619230271, 9.2741716152781, 2.178247872466, 5.329131641071, 7.97149518259241, 0)c(NA, 32.6821491553372, 15.9376851368224, 22.70984614056, 18.5483432305562, 3.4851965959456, 7.10550885476133, 9.11028020867704, 0) [20250519_003625.]: Logging df_agg: CpG#2 [20250519_003625.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003625.]: c(0, 8.41473135558285, 21.0155787157944, 28.98380769729, 40.7258283847219, 60.321752127534, 69.670868358929, 79.5285048174076, 100)[20250519_003625.]: c(0, 4.08526864441715, 3.9844212842056, 8.51619230271, 9.2741716152781, 2.178247872466, 5.329131641071, 7.97149518259241, 0)[20250519_003625.]: c(NA, 32.6821491553372, 15.9376851368224, 22.70984614056, 18.5483432305562, 3.4851965959456, 7.10550885476133, 9.11028020867704, 0) [20250519_003625.]: Entered 'hyperbolic_regression'-Function [20250519_003625.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003626.]: Entered 'cubic_regression'-Function [20250519_003626.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003626.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 5.74980505230347, 15.0124787349221, 22.8043512255536, 34.3082682140428, 47.6381691654944, 61.8773912993076, 65.4860304554385, 100)c(0, 6.75019494769653, 9.9875212650779, 14.6956487744464, 15.6917317859572, 14.8618308345056, 13.1226087006924, 22.0139695445615, 0)c(NA, 54.0015595815722, 39.9500850603116, 39.1883967318571, 31.3834635719144, 23.778929335209, 17.4968116009232, 25.1588223366417, 0) [20250519_003626.]: Logging df_agg: CpG#3 [20250519_003626.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003626.]: c(0, 5.74980505230347, 15.0124787349221, 22.8043512255536, 34.3082682140428, 47.6381691654944, 61.8773912993076, 65.4860304554385, 100)[20250519_003626.]: c(0, 6.75019494769653, 9.9875212650779, 14.6956487744464, 15.6917317859572, 14.8618308345056, 13.1226087006924, 22.0139695445615, 0)[20250519_003626.]: c(NA, 54.0015595815722, 39.9500850603116, 39.1883967318571, 31.3834635719144, 23.778929335209, 17.4968116009232, 25.1588223366417, 0) [20250519_003626.]: Entered 'hyperbolic_regression'-Function [20250519_003626.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003626.]: Entered 'cubic_regression'-Function [20250519_003626.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003626.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 6.6142663267093, 13.3723444572473, 22.4624407354305, 30.4240096190196, 48.070278909378, 55.3176226954937, 66.4686809672836, 100)c(0, 5.8857336732907, 11.6276555427527, 15.0375592645695, 19.5759903809804, 14.429721090622, 19.6823773045063, 21.0313190327164, 0)c(NA, 47.0858693863256, 46.5106221710108, 40.100158038852, 39.1519807619608, 23.0875537449952, 26.2431697393417, 24.0357931802473, 0) [20250519_003626.]: Logging df_agg: CpG#4 [20250519_003626.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003626.]: c(0, 6.6142663267093, 13.3723444572473, 22.4624407354305, 30.4240096190196, 48.070278909378, 55.3176226954937, 66.4686809672836, 100)[20250519_003626.]: c(0, 5.8857336732907, 11.6276555427527, 15.0375592645695, 19.5759903809804, 14.429721090622, 19.6823773045063, 21.0313190327164, 0)[20250519_003626.]: c(NA, 47.0858693863256, 46.5106221710108, 40.100158038852, 39.1519807619608, 23.0875537449952, 26.2431697393417, 24.0357931802473, 0) [20250519_003626.]: Entered 'hyperbolic_regression'-Function [20250519_003626.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003626.]: Entered 'cubic_regression'-Function [20250519_003626.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003627.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.4570373964758, 26.3229081557653, 39.9371114374682, 53.0630784939681, 63.9590902125397, 77.051078420522, 86.3095786035144, 100)c(0, 2.0429626035242, 1.3229081557653, 2.4371114374682, 3.0630784939681, 1.4590902125397, 2.051078420522, 1.1904213964856, 0)c(NA, 16.3437008281936, 5.2916326230612, 6.49896383324852, 6.1261569879362, 2.33454434006352, 2.73477122736267, 1.36048159598355, 0) [20250519_003627.]: Logging df_agg: CpG#5 [20250519_003627.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003627.]: c(0, 10.4570373964758, 26.3229081557653, 39.9371114374682, 53.0630784939681, 63.9590902125397, 77.051078420522, 86.3095786035144, 100)[20250519_003627.]: c(0, 2.0429626035242, 1.3229081557653, 2.4371114374682, 3.0630784939681, 1.4590902125397, 2.051078420522, 1.1904213964856, 0)[20250519_003627.]: c(NA, 16.3437008281936, 5.2916326230612, 6.49896383324852, 6.1261569879362, 2.33454434006352, 2.73477122736267, 1.36048159598355, 0) [20250519_003627.]: Entered 'hyperbolic_regression'-Function [20250519_003627.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003627.]: Entered 'cubic_regression'-Function [20250519_003627.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003627.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.15031343762292, 19.2804724179951, 26.5529108075111, 39.8450445790276, 54.9734681583699, 64.742519688394, 81.444841573602, 100)c(0, 4.34968656237708, 5.7195275820049, 10.9470891924889, 10.1549554209724, 7.5265318416301, 10.257480311606, 6.05515842639799, 0)c(NA, 34.7974924990166, 22.8781103280196, 29.1922378466371, 20.3099108419448, 12.0424509466082, 13.6766404154747, 6.92018105874057, 0) [20250519_003627.]: Logging df_agg: CpG#6 [20250519_003627.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003627.]: c(0, 8.15031343762292, 19.2804724179951, 26.5529108075111, 39.8450445790276, 54.9734681583699, 64.742519688394, 81.444841573602, 100)[20250519_003627.]: c(0, 4.34968656237708, 5.7195275820049, 10.9470891924889, 10.1549554209724, 7.5265318416301, 10.257480311606, 6.05515842639799, 0)[20250519_003627.]: c(NA, 34.7974924990166, 22.8781103280196, 29.1922378466371, 20.3099108419448, 12.0424509466082, 13.6766404154747, 6.92018105874057, 0) [20250519_003627.]: Entered 'hyperbolic_regression'-Function [20250519_003627.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003627.]: Entered 'cubic_regression'-Function [20250519_003627.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003627.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 3.66155933245296, 9.01334562885863, 14.9602090097958, 23.8420840440459, 32.8944868748769, 42.0684566960277, 46.1959470960303, 99.9999999999999)c(0, 8.83844066754704, 15.9866543711414, 22.5397909902042, 26.1579159559541, 29.6055131251231, 32.9315433039723, 41.3040529039697, 9.9475983006414e-14)c(NA, 70.7075253403763, 63.9466174845655, 60.1061093072112, 52.3158319119082, 47.368821000197, 43.9087244052964, 47.2046318902511, 9.9475983006414e-14) [20250519_003627.]: Logging df_agg: CpG#7 [20250519_003627.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003627.]: c(0, 3.66155933245296, 9.01334562885863, 14.9602090097958, 23.8420840440459, 32.8944868748769, 42.0684566960277, 46.1959470960303, 99.9999999999999)[20250519_003627.]: c(0, 8.83844066754704, 15.9866543711414, 22.5397909902042, 26.1579159559541, 29.6055131251231, 32.9315433039723, 41.3040529039697, 9.9475983006414e-14)[20250519_003627.]: c(NA, 70.7075253403763, 63.9466174845655, 60.1061093072112, 52.3158319119082, 47.368821000197, 43.9087244052964, 47.2046318902511, 9.9475983006414e-14) [20250519_003627.]: Entered 'hyperbolic_regression'-Function [20250519_003627.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003628.]: Entered 'cubic_regression'-Function [20250519_003628.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003628.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.96146907472175, 28.1602892189673, 37.1893361505397, 54.6609191320759, 66.6424746382684, 79.0794300908275, 81.6808296744085, 100)c(0, 3.53853092527825, 3.1602892189673, 0.310663849460298, 4.6609191320759, 4.1424746382684, 4.0794300908275, 5.8191703255915, 0)c(NA, 28.308247402226, 12.6411568758692, 0.828436931894127, 9.32183826415179, 6.62795942122943, 5.43924012110333, 6.65048037210457, 0) [20250519_003628.]: Logging df_agg: CpG#8 [20250519_003628.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003628.]: c(0, 8.96146907472175, 28.1602892189673, 37.1893361505397, 54.6609191320759, 66.6424746382684, 79.0794300908275, 81.6808296744085, 100)[20250519_003628.]: c(0, 3.53853092527825, 3.1602892189673, 0.310663849460298, 4.6609191320759, 4.1424746382684, 4.0794300908275, 5.8191703255915, 0)[20250519_003628.]: c(NA, 28.308247402226, 12.6411568758692, 0.828436931894127, 9.32183826415179, 6.62795942122943, 5.43924012110333, 6.65048037210457, 0) [20250519_003628.]: Entered 'hyperbolic_regression'-Function [20250519_003628.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003628.]: Entered 'cubic_regression'-Function [20250519_003628.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003628.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 3.60796342124625, 9.37027096567147, 13.6529015267019, 21.8965479410597, 31.2983747080124, 40.6887948365007, 53.9067876123417, 100)c(0, 8.89203657875375, 15.6297290343285, 23.8470984732981, 28.1034520589403, 31.2016252919876, 34.3112051634993, 33.5932123876583, 0)c(NA, 71.13629263003, 62.5189161373141, 63.5922625954616, 56.2069041178806, 49.9226004671802, 45.7482735513324, 38.3922427287523, 0) [20250519_003628.]: Logging df_agg: CpG#9 [20250519_003628.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003628.]: c(0, 3.60796342124625, 9.37027096567147, 13.6529015267019, 21.8965479410597, 31.2983747080124, 40.6887948365007, 53.9067876123417, 100)[20250519_003628.]: c(0, 8.89203657875375, 15.6297290343285, 23.8470984732981, 28.1034520589403, 31.2016252919876, 34.3112051634993, 33.5932123876583, 0)[20250519_003628.]: c(NA, 71.13629263003, 62.5189161373141, 63.5922625954616, 56.2069041178806, 49.9226004671802, 45.7482735513324, 38.3922427287523, 0) [20250519_003628.]: Entered 'hyperbolic_regression'-Function [20250519_003628.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003629.]: Entered 'cubic_regression'-Function [20250519_003629.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003629.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.000118249869729556, 6.10737244290489, 15.2822474794705, 23.3309318476314, 35.1067698776441, 49.3683845454072, 61.0436420088989, 70.9843655625978, 100)c(0.000118249869729556, 6.39262755709511, 9.7177525205295, 14.1690681523686, 14.8932301223559, 13.1316154545928, 13.9563579911011, 16.5156344374022, 0)c(NA, 51.1410204567609, 38.871010082118, 37.7841817396496, 29.7864602447118, 21.0105847273485, 18.6084773214681, 18.8750107856025, 0) [20250519_003629.]: Logging df_agg: row_means [20250519_003629.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003629.]: c(0.000118249869729556, 6.10737244290489, 15.2822474794705, 23.3309318476314, 35.1067698776441, 49.3683845454072, 61.0436420088989, 70.9843655625978, 100)[20250519_003629.]: c(0.000118249869729556, 6.39262755709511, 9.7177525205295, 14.1690681523686, 14.8932301223559, 13.1316154545928, 13.9563579911011, 16.5156344374022, 0)[20250519_003629.]: c(NA, 51.1410204567609, 38.871010082118, 37.7841817396496, 29.7864602447118, 21.0105847273485, 18.6084773214681, 18.8750107856025, 0) [20250519_003629.]: Entered 'hyperbolic_regression'-Function [20250519_003629.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003629.]: Entered 'cubic_regression'-Function [20250519_003629.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_003630.]: Entered 'solving_equations'-Function [20250519_003630.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 78.9992198550223 [20250519_003630.]: Samplename: Sample#1 Root: 78.999 --> Root in between the borders! Added to results. Hyperbolic solved: 31.2870579236875 [20250519_003630.]: Samplename: Sample#10 Root: 31.287 --> Root in between the borders! Added to results. Hyperbolic solved: 42.7215290847412 [20250519_003630.]: Samplename: Sample#2 Root: 42.722 --> Root in between the borders! Added to results. Hyperbolic solved: 57.8350974981729 [20250519_003630.]: Samplename: Sample#3 Root: 57.835 --> Root in between the borders! Added to results. Hyperbolic solved: 11.2415690196975 [20250519_003630.]: Samplename: Sample#4 Root: 11.242 --> Root in between the borders! Added to results. Hyperbolic solved: 23.5440570752846 [20250519_003630.]: Samplename: Sample#5 Root: 23.544 --> Root in between the borders! Added to results. Hyperbolic solved: 24.7858714956021 [20250519_003630.]: Samplename: Sample#6 Root: 24.786 --> Root in between the borders! Added to results. Hyperbolic solved: 46.4156211496502 [20250519_003630.]: Samplename: Sample#7 Root: 46.416 --> Root in between the borders! Added to results. Hyperbolic solved: 84.4614182359021 [20250519_003630.]: Samplename: Sample#8 Root: 84.461 --> Root in between the borders! Added to results. Hyperbolic solved: -1.41454023853738 [20250519_003630.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -1.415 --> '-10 < root < 0' --> substitute 0 [20250519_003630.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 76.6628425084681 [20250519_003630.]: Samplename: Sample#1 Root: 76.663 --> Root in between the borders! Added to results. Hyperbolic solved: 31.002922067723 [20250519_003630.]: Samplename: Sample#10 Root: 31.003 --> Root in between the borders! Added to results. Hyperbolic solved: 42.8794162472613 [20250519_003630.]: Samplename: Sample#2 Root: 42.879 --> Root in between the borders! Added to results. Hyperbolic solved: 59.1562251576939 [20250519_003630.]: Samplename: Sample#3 Root: 59.156 --> Root in between the borders! Added to results. Hyperbolic solved: 4.04613842773285 [20250519_003630.]: Samplename: Sample#4 Root: 4.046 --> Root in between the borders! Added to results. Hyperbolic solved: 19.9574827771543 [20250519_003630.]: Samplename: Sample#5 Root: 19.957 --> Root in between the borders! Added to results. Hyperbolic solved: 17.4372350357645 [20250519_003630.]: Samplename: Sample#6 Root: 17.437 --> Root in between the borders! Added to results. Hyperbolic solved: 41.0968872148931 [20250519_003630.]: Samplename: Sample#7 Root: 41.097 --> Root in between the borders! Added to results. Hyperbolic solved: 85.7111018674234 [20250519_003630.]: Samplename: Sample#8 Root: 85.711 --> Root in between the borders! Added to results. Hyperbolic solved: 2.08554114625032 [20250519_003630.]: Samplename: Sample#9 Root: 2.086 --> Root in between the borders! Added to results. [20250519_003630.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 74.5413598656994 [20250519_003630.]: Samplename: Sample#1 Root: 74.541 --> Root in between the borders! Added to results. Hyperbolic solved: 28.3514323412579 [20250519_003630.]: Samplename: Sample#10 Root: 28.351 --> Root in between the borders! Added to results. Hyperbolic solved: 42.6007641487945 [20250519_003630.]: Samplename: Sample#2 Root: 42.601 --> Root in between the borders! Added to results. Hyperbolic solved: 56.3207791919885 [20250519_003630.]: Samplename: Sample#3 Root: 56.321 --> Root in between the borders! Added to results. Hyperbolic solved: 7.98800399083 [20250519_003630.]: Samplename: Sample#4 Root: 7.988 --> Root in between the borders! Added to results. Hyperbolic solved: 24.6963927948962 [20250519_003630.]: Samplename: Sample#5 Root: 24.696 --> Root in between the borders! Added to results. Hyperbolic solved: 26.8806215804592 [20250519_003630.]: Samplename: Sample#6 Root: 26.881 --> Root in between the borders! Added to results. Hyperbolic solved: 44.8239489304102 [20250519_003630.]: Samplename: Sample#7 Root: 44.824 --> Root in between the borders! Added to results. Hyperbolic solved: 84.669654917361 [20250519_003630.]: Samplename: Sample#8 Root: 84.67 --> Root in between the borders! Added to results. Hyperbolic solved: -1.26160051687973 [20250519_003630.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -1.262 --> '-10 < root < 0' --> substitute 0 [20250519_003630.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 75.8530019120299 [20250519_003630.]: Samplename: Sample#1 Root: 75.853 --> Root in between the borders! Added to results. Hyperbolic solved: 29.0711677507654 [20250519_003630.]: Samplename: Sample#10 Root: 29.071 --> Root in between the borders! Added to results. Hyperbolic solved: 44.0485537238583 [20250519_003630.]: Samplename: Sample#2 Root: 44.049 --> Root in between the borders! Added to results. Hyperbolic solved: 58.7878536128746 [20250519_003630.]: Samplename: Sample#3 Root: 58.788 --> Root in between the borders! Added to results. Hyperbolic solved: 11.0370782280591 [20250519_003630.]: Samplename: Sample#4 Root: 11.037 --> Root in between the borders! Added to results. Hyperbolic solved: 23.0042107924666 [20250519_003630.]: Samplename: Sample#5 Root: 23.004 --> Root in between the borders! Added to results. Hyperbolic solved: 27.9521038368967 [20250519_003630.]: Samplename: Sample#6 Root: 27.952 --> Root in between the borders! Added to results. Hyperbolic solved: 42.50025622677 [20250519_003630.]: Samplename: Sample#7 Root: 42.5 --> Root in between the borders! Added to results. Hyperbolic solved: 84.6870942263369 [20250519_003630.]: Samplename: Sample#8 Root: 84.687 --> Root in between the borders! Added to results. Hyperbolic solved: 3.01041300417756 [20250519_003630.]: Samplename: Sample#9 Root: 3.01 --> Root in between the borders! Added to results. [20250519_003630.]: Solving cubic regression for CpG#5 Coefficients: -47.8373333333333Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_003630.]: Samplename: Sample#1 Root: 73.686 --> Root in between the borders! Added to results. Coefficients: -13.588Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_003630.]: Samplename: Sample#10 Root: 28.906 --> Root in between the borders! Added to results. Coefficients: -25.3211428571429Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_003630.]: Samplename: Sample#2 Root: 46.761 --> Root in between the borders! Added to results. Coefficients: -32.064Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_003630.]: Samplename: Sample#3 Root: 55.588 --> Root in between the borders! Added to results. Coefficients: -4.074Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_003630.]: Samplename: Sample#4 Root: 10.297 --> Root in between the borders! Added to results. Coefficients: -11.434Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_003630.]: Samplename: Sample#5 Root: 25.131 --> Root in between the borders! Added to results. Coefficients: -13.294Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_003630.]: Samplename: Sample#6 Root: 28.402 --> Root in between the borders! Added to results. Coefficients: -24.288Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_003630.]: Samplename: Sample#7 Root: 45.332 --> Root in between the borders! Added to results. Coefficients: -63.134Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_003630.]: Samplename: Sample#8 Root: 88.909 --> Root in between the borders! Added to results. Coefficients: 0.0360000000000005Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_003630.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.102 --> '-10 < root < 0' --> substitute 0 [20250519_003630.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 79.2062097176894 [20250519_003630.]: Samplename: Sample#1 Root: 79.206 --> Root in between the borders! Added to results. Hyperbolic solved: 30.234912908094 [20250519_003630.]: Samplename: Sample#10 Root: 30.235 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8992139020538 [20250519_003630.]: Samplename: Sample#2 Root: 41.899 --> Root in between the borders! Added to results. Hyperbolic solved: 56.8778124304882 [20250519_003630.]: Samplename: Sample#3 Root: 56.878 --> Root in between the borders! Added to results. Hyperbolic solved: 8.80900690226663 [20250519_003630.]: Samplename: Sample#4 Root: 8.809 --> Root in between the borders! Added to results. Hyperbolic solved: 18.6793858198319 [20250519_003630.]: Samplename: Sample#5 Root: 18.679 --> Root in between the borders! Added to results. Hyperbolic solved: 29.9638526705022 [20250519_003630.]: Samplename: Sample#6 Root: 29.964 --> Root in between the borders! Added to results. Hyperbolic solved: 42.8669225173731 [20250519_003630.]: Samplename: Sample#7 Root: 42.867 --> Root in between the borders! Added to results. Hyperbolic solved: 86.6206311389839 [20250519_003630.]: Samplename: Sample#8 Root: 86.621 --> Root in between the borders! Added to results. Hyperbolic solved: 1.38882504570857 [20250519_003630.]: Samplename: Sample#9 Root: 1.389 --> Root in between the borders! Added to results. [20250519_003630.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 77.5203002291111 [20250519_003630.]: Samplename: Sample#1 Root: 77.52 --> Root in between the borders! Added to results. Hyperbolic solved: 27.0810020209347 [20250519_003630.]: Samplename: Sample#10 Root: 27.081 --> Root in between the borders! Added to results. Hyperbolic solved: 48.4274819445682 [20250519_003630.]: Samplename: Sample#2 Root: 48.427 --> Root in between the borders! Added to results. Hyperbolic solved: 58.8711296078074 [20250519_003630.]: Samplename: Sample#3 Root: 58.871 --> Root in between the borders! Added to results. Hyperbolic solved: 13.3245830390029 [20250519_003630.]: Samplename: Sample#4 Root: 13.325 --> Root in between the borders! Added to results. Hyperbolic solved: 26.9731029843141 [20250519_003630.]: Samplename: Sample#5 Root: 26.973 --> Root in between the borders! Added to results. Hyperbolic solved: 30.9519756409676 [20250519_003630.]: Samplename: Sample#6 Root: 30.952 --> Root in between the borders! Added to results. Hyperbolic solved: 45.7349250650843 [20250519_003630.]: Samplename: Sample#7 Root: 45.735 --> Root in between the borders! Added to results. Hyperbolic solved: 84.5977214697661 [20250519_003630.]: Samplename: Sample#8 Root: 84.598 --> Root in between the borders! Added to results. Hyperbolic solved: -2.87252278311384 [20250519_003630.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.873 --> '-10 < root < 0' --> substitute 0 [20250519_003630.]: Solving cubic regression for CpG#8 Coefficients: -55.3573333333333Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_003630.]: Samplename: Sample#1 Root: 74.173 --> Root in between the borders! Added to results. Coefficients: -17.574Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_003630.]: Samplename: Sample#10 Root: 30.7 --> Root in between the borders! Added to results. Coefficients: -22.9425714285714Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_003630.]: Samplename: Sample#2 Root: 38.106 --> Root in between the borders! Added to results. Coefficients: -42.849Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_003630.]: Samplename: Sample#3 Root: 61.571 --> Root in between the borders! Added to results. Coefficients: -4.604Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_003630.]: Samplename: Sample#4 Root: 9.446 --> Root in between the borders! Added to results. Coefficients: -11.389Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_003630.]: Samplename: Sample#5 Root: 21.295 --> Root in between the borders! Added to results. Coefficients: -25.784Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_003630.]: Samplename: Sample#6 Root: 41.8 --> Root in between the borders! Added to results. Coefficients: -30.746Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_003630.]: Samplename: Sample#7 Root: 47.937 --> Root in between the borders! Added to results. Coefficients: -66.912Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_003630.]: Samplename: Sample#8 Root: 84.816 --> Root in between the borders! Added to results. Coefficients: 3.176Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_003630.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -7.574 --> '-10 < root < 0' --> substitute 0 [20250519_003630.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 80.5447416574246 [20250519_003630.]: Samplename: Sample#1 Root: 80.545 --> Root in between the borders! Added to results. Hyperbolic solved: 27.8054725532271 [20250519_003630.]: Samplename: Sample#10 Root: 27.805 --> Root in between the borders! Added to results. Hyperbolic solved: 46.257978181877 [20250519_003630.]: Samplename: Sample#2 Root: 46.258 --> Root in between the borders! Added to results. Hyperbolic solved: 57.1842723611193 [20250519_003630.]: Samplename: Sample#3 Root: 57.184 --> Root in between the borders! Added to results. Hyperbolic solved: 8.63689944440251 [20250519_003630.]: Samplename: Sample#4 Root: 8.637 --> Root in between the borders! Added to results. Hyperbolic solved: 24.2116725772085 [20250519_003630.]: Samplename: Sample#5 Root: 24.212 --> Root in between the borders! Added to results. Hyperbolic solved: 39.6334888107959 [20250519_003630.]: Samplename: Sample#6 Root: 39.633 --> Root in between the borders! Added to results. Hyperbolic solved: 44.3019478766814 [20250519_003630.]: Samplename: Sample#7 Root: 44.302 --> Root in between the borders! Added to results. Hyperbolic solved: 87.3231552867261 [20250519_003630.]: Samplename: Sample#8 Root: 87.323 --> Root in between the borders! Added to results. Hyperbolic solved: -1.1792994162273 [20250519_003630.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -1.179 --> '-10 < root < 0' --> substitute 0 [20250519_003630.]: Solving hyperbolic regression for row_means Hyperbolic solved: 76.7712420256811 [20250519_003630.]: Samplename: Sample#1 Root: 76.771 --> Root in between the borders! Added to results. Hyperbolic solved: 28.8491793177963 [20250519_003630.]: Samplename: Sample#10 Root: 28.849 --> Root in between the borders! Added to results. Hyperbolic solved: 43.0342096405004 [20250519_003630.]: Samplename: Sample#2 Root: 43.034 --> Root in between the borders! Added to results. Hyperbolic solved: 57.634070615517 [20250519_003630.]: Samplename: Sample#3 Root: 57.634 --> Root in between the borders! Added to results. Hyperbolic solved: 8.87182398849477 [20250519_003630.]: Samplename: Sample#4 Root: 8.872 --> Root in between the borders! Added to results. Hyperbolic solved: 22.1989155573519 [20250519_003630.]: Samplename: Sample#5 Root: 22.199 --> Root in between the borders! Added to results. Hyperbolic solved: 29.2140219298625 [20250519_003630.]: Samplename: Sample#6 Root: 29.214 --> Root in between the borders! Added to results. Hyperbolic solved: 43.9371957594463 [20250519_003630.]: Samplename: Sample#7 Root: 43.937 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6706924479325 [20250519_003630.]: Samplename: Sample#8 Root: 85.671 --> Root in between the borders! Added to results. Hyperbolic solved: -0.55137023392917 [20250519_003630.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.551 --> '-10 < root < 0' --> substitute 0 [20250519_003630.]: Entered 'solving_equations'-Function [20250519_003630.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 0 [20250519_003630.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 14.1480167109277 [20250519_003630.]: Samplename: 12.5 Root: 14.148 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1398443790446 [20250519_003630.]: Samplename: 25 Root: 26.14 --> Root in between the borders! Added to results. Hyperbolic solved: 39.3762039523382 [20250519_003630.]: Samplename: 37.5 Root: 39.376 --> Root in between the borders! Added to results. Hyperbolic solved: 52.9476244028783 [20250519_003630.]: Samplename: 50 Root: 52.948 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4195105873419 [20250519_003630.]: Samplename: 62.5 Root: 65.42 --> Root in between the borders! Added to results. Hyperbolic solved: 74.433837698331 [20250519_003630.]: Samplename: 75 Root: 74.434 --> Root in between the borders! Added to results. Hyperbolic solved: 80.5559264925808 [20250519_003630.]: Samplename: 87.5 Root: 80.556 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_003630.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_003630.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 0 [20250519_003630.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 10.7812303822548 [20250519_003630.]: Samplename: 12.5 Root: 10.781 --> Root in between the borders! Added to results. Hyperbolic solved: 26.0648314401535 [20250519_003630.]: Samplename: 25 Root: 26.065 --> Root in between the borders! Added to results. Hyperbolic solved: 35.1980950192158 [20250519_003630.]: Samplename: 37.5 Root: 35.198 --> Root in between the borders! Added to results. Hyperbolic solved: 47.9203836090837 [20250519_003630.]: Samplename: 50 Root: 47.92 --> Root in between the borders! Added to results. Hyperbolic solved: 67.275750545092 [20250519_003630.]: Samplename: 62.5 Root: 67.276 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7278143684244 [20250519_003630.]: Samplename: 75 Root: 75.728 --> Root in between the borders! Added to results. Hyperbolic solved: 84.1258428690749 [20250519_003630.]: Samplename: 87.5 Root: 84.126 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_003630.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_003630.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 0 [20250519_003630.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 10.8467198180169 [20250519_003630.]: Samplename: 12.5 Root: 10.847 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1449700612555 [20250519_003630.]: Samplename: 25 Root: 26.145 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2865760013773 [20250519_003630.]: Samplename: 37.5 Root: 37.287 --> Root in between the borders! Added to results. Hyperbolic solved: 51.4114078772697 [20250519_003630.]: Samplename: 50 Root: 51.411 --> Root in between the borders! Added to results. Hyperbolic solved: 65.0139634978039 [20250519_003630.]: Samplename: 62.5 Root: 65.014 --> Root in between the borders! Added to results. Hyperbolic solved: 76.9921394830877 [20250519_003630.]: Samplename: 75 Root: 76.992 --> Root in between the borders! Added to results. Hyperbolic solved: 79.6808818589171 [20250519_003630.]: Samplename: 87.5 Root: 79.681 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_003630.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_003630.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 0 [20250519_003630.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 13.2494913489925 [20250519_003630.]: Samplename: 12.5 Root: 13.249 --> Root in between the borders! Added to results. Hyperbolic solved: 25.0914701740206 [20250519_003630.]: Samplename: 25 Root: 25.091 --> Root in between the borders! Added to results. Hyperbolic solved: 38.8081740444332 [20250519_003630.]: Samplename: 37.5 Root: 38.808 --> Root in between the borders! Added to results. Hyperbolic solved: 49.1133034150896 [20250519_003630.]: Samplename: 50 Root: 49.113 --> Root in between the borders! Added to results. Hyperbolic solved: 67.573566028728 [20250519_003630.]: Samplename: 62.5 Root: 67.574 --> Root in between the borders! Added to results. Hyperbolic solved: 73.7655861480302 [20250519_003630.]: Samplename: 75 Root: 73.766 --> Root in between the borders! Added to results. Hyperbolic solved: 82.0404940898918 [20250519_003630.]: Samplename: 87.5 Root: 82.04 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_003630.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_003630.]: Solving cubic regression for CpG#5 Coefficients: 0Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_003630.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -4.144Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_003630.]: Samplename: 12.5 Root: 10.457 --> Root in between the borders! Added to results. Coefficients: -12.102Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_003630.]: Samplename: 25 Root: 26.323 --> Root in between the borders! Added to results. Coefficients: -20.536Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_003630.]: Samplename: 37.5 Root: 39.937 --> Root in between the borders! Added to results. Coefficients: -30.0715Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_003630.]: Samplename: 50 Root: 53.063 --> Root in between the borders! Added to results. Coefficients: -39.034Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_003630.]: Samplename: 62.5 Root: 63.959 --> Root in between the borders! Added to results. Coefficients: -51.059Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_003630.]: Samplename: 75 Root: 77.051 --> Root in between the borders! Added to results. Coefficients: -60.3906666666667Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_003630.]: Samplename: 87.5 Root: 86.31 --> Root in between the borders! Added to results. Coefficients: -75.446Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_003630.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_003630.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 0 [20250519_003630.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 11.8139244542506 [20250519_003630.]: Samplename: 12.5 Root: 11.814 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5330423964606 [20250519_003630.]: Samplename: 25 Root: 26.533 --> Root in between the borders! Added to results. Hyperbolic solved: 35.365455365759 [20250519_003630.]: Samplename: 37.5 Root: 35.365 --> Root in between the borders! Added to results. Hyperbolic solved: 50.1054345020203 [20250519_003630.]: Samplename: 50 Root: 50.105 --> Root in between the borders! Added to results. Hyperbolic solved: 64.9683746571925 [20250519_003630.]: Samplename: 62.5 Root: 64.968 --> Root in between the borders! Added to results. Hyperbolic solved: 73.6331726521876 [20250519_003630.]: Samplename: 75 Root: 73.633 --> Root in between the borders! Added to results. Hyperbolic solved: 86.9938602772804 [20250519_003630.]: Samplename: 87.5 Root: 86.994 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_003630.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_003630.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 0 [20250519_003630.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 11.7880491100164 [20250519_003630.]: Samplename: 12.5 Root: 11.788 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1959234034328 [20250519_003630.]: Samplename: 25 Root: 26.196 --> Root in between the borders! Added to results. Hyperbolic solved: 39.1978567589215 [20250519_003630.]: Samplename: 37.5 Root: 39.198 --> Root in between the borders! Added to results. Hyperbolic solved: 54.3513505291649 [20250519_003630.]: Samplename: 50 Root: 54.351 --> Root in between the borders! Added to results. Hyperbolic solved: 66.0567953704493 [20250519_003630.]: Samplename: 62.5 Root: 66.057 --> Root in between the borders! Added to results. Hyperbolic solved: 75.1900867269869 [20250519_003630.]: Samplename: 75 Root: 75.19 --> Root in between the borders! Added to results. Hyperbolic solved: 78.6051660349008 [20250519_003630.]: Samplename: 87.5 Root: 78.605 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_003630.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_003630.]: Solving cubic regression for CpG#8 Coefficients: 0Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_003630.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -4.35066666666667Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_003630.]: Samplename: 12.5 Root: 8.961 --> Root in between the borders! Added to results. Coefficients: -15.834Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_003630.]: Samplename: 25 Root: 28.16 --> Root in between the borders! Added to results. Coefficients: -22.254Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_003630.]: Samplename: 37.5 Root: 37.189 --> Root in between the borders! Added to results. Coefficients: -36.529Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_003630.]: Samplename: 50 Root: 54.661 --> Root in between the borders! Added to results. Coefficients: -47.73Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_003630.]: Samplename: 62.5 Root: 66.642 --> Root in between the borders! Added to results. Coefficients: -60.5715Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_003630.]: Samplename: 75 Root: 79.079 --> Root in between the borders! Added to results. Coefficients: -63.414Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_003630.]: Samplename: 87.5 Root: 81.681 --> Root in between the borders! Added to results. Coefficients: -84.964Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_003630.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_003630.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 0 [20250519_003630.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 12.2068234201026 [20250519_003630.]: Samplename: 12.5 Root: 12.207 --> Root in between the borders! Added to results. Hyperbolic solved: 28.0688737636578 [20250519_003630.]: Samplename: 25 Root: 28.069 --> Root in between the borders! Added to results. Hyperbolic solved: 37.6661823177513 [20250519_003630.]: Samplename: 37.5 Root: 37.666 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3684233460174 [20250519_003630.]: Samplename: 50 Root: 52.368 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8636916649907 [20250519_003630.]: Samplename: 62.5 Root: 64.864 --> Root in between the borders! Added to results. Hyperbolic solved: 74.2551330907062 [20250519_003630.]: Samplename: 75 Root: 74.255 --> Root in between the borders! Added to results. Hyperbolic solved: 83.9343288872042 [20250519_003630.]: Samplename: 87.5 Root: 83.934 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_003630.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_003630.]: Solving hyperbolic regression for row_means Hyperbolic solved: 0.000228160853766634 [20250519_003630.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 11.1587839347378 [20250519_003630.]: Samplename: 12.5 Root: 11.159 --> Root in between the borders! Added to results. Hyperbolic solved: 25.8570776700364 [20250519_003630.]: Samplename: 25 Root: 25.857 --> Root in between the borders! Added to results. Hyperbolic solved: 37.065004454281 [20250519_003630.]: Samplename: 37.5 Root: 37.065 --> Root in between the borders! Added to results. Hyperbolic solved: 51.1881763075298 [20250519_003630.]: Samplename: 50 Root: 51.188 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4439754555105 [20250519_003630.]: Samplename: 62.5 Root: 65.444 --> Root in between the borders! Added to results. Hyperbolic solved: 75.3005883283082 [20250519_003630.]: Samplename: 75 Root: 75.301 --> Root in between the borders! Added to results. Hyperbolic solved: 82.6591025877361 [20250519_003630.]: Samplename: 87.5 Root: 82.659 --> Root in between the borders! Added to results. Hyperbolic solved: 100.000153506599 [20250519_003630.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 [20250519_003635.]: on_start: using future::plan("sequential") [20250519_003635.]: Entered 'clean_dt'-Function [20250519_003635.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_003635.]: got experimental data [20250519_003635.]: Entered 'clean_dt'-Function [20250519_003635.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_003635.]: got calibration data [20250519_003635.]: ### Starting with regression calculations ### [20250519_003635.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_003635.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_003635.]: Logging df_agg: CpG#1 [20250519_003635.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003635.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_003635.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_003635.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_003635.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_003635.]: Entered 'hyperbolic_regression'-Function [20250519_003635.]: 'hyperbolic_regression': minmax = FALSE [20250519_003635.]: Entered 'cubic_regression'-Function [20250519_003635.]: 'cubic_regression': minmax = FALSE [20250519_003635.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_003635.]: Logging df_agg: CpG#2 [20250519_003635.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003635.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_003635.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_003635.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_003635.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_003635.]: Entered 'hyperbolic_regression'-Function [20250519_003635.]: 'hyperbolic_regression': minmax = FALSE [20250519_003636.]: Entered 'cubic_regression'-Function [20250519_003636.]: 'cubic_regression': minmax = FALSE [20250519_003636.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_003636.]: Logging df_agg: CpG#3 [20250519_003636.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003636.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_003636.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_003636.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_003636.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_003636.]: Entered 'hyperbolic_regression'-Function [20250519_003636.]: 'hyperbolic_regression': minmax = FALSE [20250519_003636.]: Entered 'cubic_regression'-Function [20250519_003636.]: 'cubic_regression': minmax = FALSE [20250519_003636.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_003636.]: Logging df_agg: CpG#4 [20250519_003636.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003636.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_003636.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_003636.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_003636.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_003636.]: Entered 'hyperbolic_regression'-Function [20250519_003636.]: 'hyperbolic_regression': minmax = FALSE [20250519_003636.]: Entered 'cubic_regression'-Function [20250519_003636.]: 'cubic_regression': minmax = FALSE [20250519_003636.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_003636.]: Logging df_agg: CpG#5 [20250519_003636.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003636.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_003636.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_003636.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_003636.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_003636.]: Entered 'hyperbolic_regression'-Function [20250519_003636.]: 'hyperbolic_regression': minmax = FALSE [20250519_003636.]: Entered 'cubic_regression'-Function [20250519_003636.]: 'cubic_regression': minmax = FALSE [20250519_003636.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_003636.]: Logging df_agg: CpG#6 [20250519_003636.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003636.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_003636.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_003636.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_003636.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_003636.]: Entered 'hyperbolic_regression'-Function [20250519_003636.]: 'hyperbolic_regression': minmax = FALSE [20250519_003637.]: Entered 'cubic_regression'-Function [20250519_003637.]: 'cubic_regression': minmax = FALSE [20250519_003637.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_003637.]: Logging df_agg: CpG#7 [20250519_003637.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003637.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_003637.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_003637.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_003637.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_003637.]: Entered 'hyperbolic_regression'-Function [20250519_003637.]: 'hyperbolic_regression': minmax = FALSE [20250519_003637.]: Entered 'cubic_regression'-Function [20250519_003637.]: 'cubic_regression': minmax = FALSE [20250519_003637.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_003637.]: Logging df_agg: CpG#8 [20250519_003637.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003637.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_003637.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_003637.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_003637.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_003637.]: Entered 'hyperbolic_regression'-Function [20250519_003637.]: 'hyperbolic_regression': minmax = FALSE [20250519_003637.]: Entered 'cubic_regression'-Function [20250519_003637.]: 'cubic_regression': minmax = FALSE [20250519_003637.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_003637.]: Logging df_agg: CpG#9 [20250519_003637.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003637.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_003637.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_003637.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_003637.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_003637.]: Entered 'hyperbolic_regression'-Function [20250519_003637.]: 'hyperbolic_regression': minmax = FALSE [20250519_003637.]: Entered 'cubic_regression'-Function [20250519_003637.]: 'cubic_regression': minmax = FALSE [20250519_003637.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723122, 2.47760998435345)c(5.94911111111111, 0.84962962962963, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703704, 19.6995555555555, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_003637.]: Logging df_agg: row_means [20250519_003637.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003637.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_003637.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723122, 2.47760998435345)[20250519_003637.]: c(5.94911111111111, 0.84962962962963, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_003637.]: c(NA, 6.79703703703704, 19.6995555555555, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_003637.]: Entered 'hyperbolic_regression'-Function [20250519_003637.]: 'hyperbolic_regression': minmax = FALSE [20250519_003638.]: Entered 'cubic_regression'-Function [20250519_003638.]: 'cubic_regression': minmax = FALSE [20250519_003638.]: ### Starting with plotting ### [20250519_003638.]: Creating plot No. 1 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG1.png [20250519_003638.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -937.719419567728 , a = -108.56796364622 , b = -937.719419567728 , d = -232.057111323599 [20250519_003638.]: # CpG-site: CpG#1 Cubic: Using a = 6.53413423120091e-05 , b = -0.0055806968734969 , c = 0.784061853455188 , d = 1.93182659932656 [20250519_003641.]: Creating plot No. 2 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG2.png [20250519_003641.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = -2393.1426335766 , a = -141.862264327279 , b = -2393.1426335766 , d = -268.331583599043 [20250519_003641.]: # CpG-site: CpG#2 Cubic: Using a = 8.04237934904601e-06 , b = 0.00293158941798942 , c = 0.514821742825076 , d = 9.27667003367003 [20250519_003642.]: Creating plot No. 3 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG3.png [20250519_003642.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = -1477.46855437885 , a = -170.424507883158 , b = -1477.46855437885 , d = -327.190157171162 [20250519_003642.]: # CpG-site: CpG#3 Cubic: Using a = 2.30555869809204e-05 , b = -0.000797009331409346 , c = 0.62783129228796 , d = 3.88705218855218 [20250519_003644.]: Creating plot No. 4 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG4.png [20250519_003644.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = -2115.87792930847 , a = -162.169768036114 , b = -2115.87792930847 , d = -299.662268971294 [20250519_003644.]: # CpG-site: CpG#4 Cubic: Using a = 3.2166356902357e-05 , b = -0.0015913142857143 , c = 0.697398364598366 , d = 6.17055050505048 [20250519_003645.]: Creating plot No. 5 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG5.png [20250519_003645.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = -1383.32738741788 , a = -151.440067063617 , b = -1383.32738741788 , d = -304.200253800186 [20250519_003645.]: # CpG-site: CpG#5 Cubic: Using a = -4.48459708193036e-06 , b = 0.00375371236171235 , c = 0.422446384479718 , d = 5.17203872053872 [20250519_003647.]: Creating plot No. 6 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG6.png [20250519_003647.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = -1550.59307603208 , a = -125.016146072952 , b = -1550.59307603208 , d = -247.279679418643 [20250519_003647.]: # CpG-site: CpG#6 Cubic: Using a = 2.38852884399552e-05 , b = 0.000918637037037021 , c = 0.561022132435467 , d = 6.1479276094276 [20250519_003648.]: Creating plot No. 7 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG7.png [20250519_003648.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -772.844192345493 , a = -75.2724875322984 , b = -772.844192345493 , d = -226.673147125363 [20250519_003648.]: # CpG-site: CpG#7 Cubic: Using a = 4.98010505050505e-05 , b = -0.00436152150072151 , c = 0.579588917748918 , d = 1.72104545454544 [20250519_003650.]: Creating plot No. 8 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG8.png [20250519_003650.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = -3872.73616547768 , a = -278.017275815885 , b = -3872.73616547768 , d = -438.894268920994 [20250519_003650.]: # CpG-site: CpG#8 Cubic: Using a = -1.50060965207632e-05 , b = 0.00447978143338143 , c = 0.534897737694404 , d = 9.56981481481482 [20250519_003651.]: Creating plot No. 9 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG9.png [20250519_003651.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -843.925451494751 , a = -70.6796095478879 , b = -843.925451494751 , d = -184.482153986057 [20250519_003651.]: # CpG-site: CpG#9 Cubic: Using a = 7.72300426487093e-05 , b = -0.00542281173641174 , c = 0.67899229597563 , d = 2.72413468013467 [20250519_003653.]: Creating plot No. 10 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_rowmeans.png [20250519_003653.]: # CpG-site: row_means Hyperbolic: Using bias_weight = -1525.75758254501 , a = -127.67501720702 , b = -1525.75758254501 , d = -263.003030149428 [20250519_003653.]: # CpG-site: row_means Cubic: Using a = 2.88923726150392e-05 , b = -0.0006299592752926 , c = 0.600117857944524 , d = 5.17789562289563 [20250519_003654.]: Entered 'solving_equations'-Function [20250519_003654.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: -2.23222990163966 [20250519_003654.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.232 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.1698489850618 [20250519_003654.]: Samplename: 12.5 Root: 12.17 --> Root in between the borders! Added to results. Hyperbolic solved: 24.4781920312644 [20250519_003654.]: Samplename: 25 Root: 24.478 --> Root in between the borders! Added to results. Hyperbolic solved: 38.173044740918 [20250519_003654.]: Samplename: 37.5 Root: 38.173 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3349371964438 [20250519_003654.]: Samplename: 50 Root: 52.335 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4582773627666 [20250519_003654.]: Samplename: 62.5 Root: 65.458 --> Root in between the borders! Added to results. Hyperbolic solved: 75.0090795260796 [20250519_003654.]: Samplename: 75 Root: 75.009 --> Root in between the borders! Added to results. Hyperbolic solved: 81.5271920968417 [20250519_003654.]: Samplename: 87.5 Root: 81.527 --> Root in between the borders! Added to results. Hyperbolic solved: 102.400893095062 [20250519_003654.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.401 --> '100 < root < 110' --> substitute 100 [20250519_003654.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 1.13660501904968 [20250519_003654.]: Samplename: 0 Root: 1.137 --> Root in between the borders! Added to results. Hyperbolic solved: 11.4129696733689 [20250519_003654.]: Samplename: 12.5 Root: 11.413 --> Root in between the borders! Added to results. Hyperbolic solved: 26.174000526428 [20250519_003654.]: Samplename: 25 Root: 26.174 --> Root in between the borders! Added to results. Hyperbolic solved: 35.1050449117028 [20250519_003655.]: Samplename: 37.5 Root: 35.105 --> Root in between the borders! Added to results. Hyperbolic solved: 47.685500330611 [20250519_003655.]: Samplename: 50 Root: 47.686 --> Root in between the borders! Added to results. Hyperbolic solved: 67.1440494417104 [20250519_003655.]: Samplename: 62.5 Root: 67.144 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7644668894086 [20250519_003655.]: Samplename: 75 Root: 75.764 --> Root in between the borders! Added to results. Hyperbolic solved: 84.4054158616395 [20250519_003655.]: Samplename: 87.5 Root: 84.405 --> Root in between the borders! Added to results. Hyperbolic solved: 100.94827248399 [20250519_003655.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.948 --> '100 < root < 110' --> substitute 100 [20250519_003655.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 0.51235653688495 [20250519_003655.]: Samplename: 0 Root: 0.512 --> Root in between the borders! Added to results. Hyperbolic solved: 10.7523884294604 [20250519_003655.]: Samplename: 12.5 Root: 10.752 --> Root in between the borders! Added to results. Hyperbolic solved: 25.5218907947761 [20250519_003655.]: Samplename: 25 Root: 25.522 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5270462675211 [20250519_003655.]: Samplename: 37.5 Root: 36.527 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7909245028224 [20250519_003655.]: Samplename: 50 Root: 50.791 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8686317550184 [20250519_003655.]: Samplename: 62.5 Root: 64.869 --> Root in between the borders! Added to results. Hyperbolic solved: 77.5524188495235 [20250519_003655.]: Samplename: 75 Root: 77.552 --> Root in between the borders! Added to results. Hyperbolic solved: 80.4374617358174 [20250519_003655.]: Samplename: 87.5 Root: 80.437 --> Root in between the borders! Added to results. Hyperbolic solved: 102.704024900825 [20250519_003655.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.704 --> '100 < root < 110' --> substitute 100 [20250519_003655.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: -0.519503092357606 [20250519_003655.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.52 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.4934147844872 [20250519_003655.]: Samplename: 12.5 Root: 12.493 --> Root in between the borders! Added to results. Hyperbolic solved: 24.2685420024115 [20250519_003655.]: Samplename: 25 Root: 24.269 --> Root in between the borders! Added to results. Hyperbolic solved: 38.0817128465023 [20250519_003655.]: Samplename: 37.5 Root: 38.082 --> Root in between the borders! Added to results. Hyperbolic solved: 48.5843181174811 [20250519_003655.]: Samplename: 50 Root: 48.584 --> Root in between the borders! Added to results. Hyperbolic solved: 67.6722399183037 [20250519_003655.]: Samplename: 62.5 Root: 67.672 --> Root in between the borders! Added to results. Hyperbolic solved: 74.1549277799119 [20250519_003655.]: Samplename: 75 Root: 74.155 --> Root in between the borders! Added to results. Hyperbolic solved: 82.8821797890026 [20250519_003655.]: Samplename: 87.5 Root: 82.882 --> Root in between the borders! Added to results. Hyperbolic solved: 102.0791269023 [20250519_003655.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.079 --> '100 < root < 110' --> substitute 100 [20250519_003655.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 2.41558626275183 [20250519_003655.]: Samplename: 0 Root: 2.416 --> Root in between the borders! Added to results. Hyperbolic solved: 10.1649674907454 [20250519_003655.]: Samplename: 12.5 Root: 10.165 --> Root in between the borders! Added to results. Hyperbolic solved: 23.9830820412762 [20250519_003655.]: Samplename: 25 Root: 23.983 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2773619900429 [20250519_003655.]: Samplename: 37.5 Root: 37.277 --> Root in between the borders! Added to results. Hyperbolic solved: 50.8659386543864 [20250519_003655.]: Samplename: 50 Root: 50.866 --> Root in between the borders! Added to results. Hyperbolic solved: 62.4342273571069 [20250519_003655.]: Samplename: 62.5 Root: 62.434 --> Root in between the borders! Added to results. Hyperbolic solved: 76.3915260534323 [20250519_003655.]: Samplename: 75 Root: 76.392 --> Root in between the borders! Added to results. Hyperbolic solved: 86.159788778566 [20250519_003655.]: Samplename: 87.5 Root: 86.16 --> Root in between the borders! Added to results. Hyperbolic solved: 100.267759893323 [20250519_003655.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.268 --> '100 < root < 110' --> substitute 100 [20250519_003655.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 0.138163748613034 [20250519_003655.]: Samplename: 0 Root: 0.138 --> Root in between the borders! Added to results. Hyperbolic solved: 11.8635558881981 [20250519_003655.]: Samplename: 12.5 Root: 11.864 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5107449550797 [20250519_003655.]: Samplename: 25 Root: 26.511 --> Root in between the borders! Added to results. Hyperbolic solved: 35.3205073050661 [20250519_003655.]: Samplename: 37.5 Root: 35.321 --> Root in between the borders! Added to results. Hyperbolic solved: 50.0570767570666 [20250519_003655.]: Samplename: 50 Root: 50.057 --> Root in between the borders! Added to results. Hyperbolic solved: 64.9602944381018 [20250519_003655.]: Samplename: 62.5 Root: 64.96 --> Root in between the borders! Added to results. Hyperbolic solved: 73.66890571617 [20250519_003655.]: Samplename: 75 Root: 73.669 --> Root in between the borders! Added to results. Hyperbolic solved: 87.1266086585036 [20250519_003655.]: Samplename: 87.5 Root: 87.127 --> Root in between the borders! Added to results. Hyperbolic solved: 100.261637014212 [20250519_003655.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.262 --> '100 < root < 110' --> substitute 100 [20250519_003655.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: -1.37238087287012 [20250519_003655.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -1.372 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.1993162352498 [20250519_003655.]: Samplename: 12.5 Root: 10.199 --> Root in between the borders! Added to results. Hyperbolic solved: 24.595178967123 [20250519_003655.]: Samplename: 25 Root: 24.595 --> Root in between the borders! Added to results. Hyperbolic solved: 37.8310421041787 [20250519_003655.]: Samplename: 37.5 Root: 37.831 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5588739724067 [20250519_003655.]: Samplename: 50 Root: 53.559 --> Root in between the borders! Added to results. Hyperbolic solved: 65.9364947980258 [20250519_003655.]: Samplename: 62.5 Root: 65.936 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7361094434913 [20250519_003655.]: Samplename: 75 Root: 75.736 --> Root in between the borders! Added to results. Hyperbolic solved: 79.432823759854 [20250519_003655.]: Samplename: 87.5 Root: 79.433 --> Root in between the borders! Added to results. Hyperbolic solved: 103.004237013737 [20250519_003655.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 103.004 --> '100 < root < 110' --> substitute 100 [20250519_003655.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 2.80068218205093 [20250519_003655.]: Samplename: 0 Root: 2.801 --> Root in between the borders! Added to results. Hyperbolic solved: 9.27535134596596 [20250519_003655.]: Samplename: 12.5 Root: 9.275 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4762621928197 [20250519_003655.]: Samplename: 25 Root: 25.476 --> Root in between the borders! Added to results. Hyperbolic solved: 34.0122075735416 [20250519_003655.]: Samplename: 37.5 Root: 34.012 --> Root in between the borders! Added to results. Hyperbolic solved: 51.7842655662325 [20250519_003655.]: Samplename: 50 Root: 51.784 --> Root in between the borders! Added to results. Hyperbolic solved: 64.6732311906145 [20250519_003655.]: Samplename: 62.5 Root: 64.673 --> Root in between the borders! Added to results. Hyperbolic solved: 78.4326978859189 [20250519_003655.]: Samplename: 75 Root: 78.433 --> Root in between the borders! Added to results. Hyperbolic solved: 81.3427232852719 [20250519_003655.]: Samplename: 87.5 Root: 81.343 --> Root in between the borders! Added to results. Hyperbolic solved: 101.964406640583 [20250519_003655.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.964 --> '100 < root < 110' --> substitute 100 [20250519_003655.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: -2.13403721845678 [20250519_003655.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.134 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.5082192457956 [20250519_003655.]: Samplename: 12.5 Root: 10.508 --> Root in between the borders! Added to results. Hyperbolic solved: 26.9164567253388 [20250519_003655.]: Samplename: 25 Root: 26.916 --> Root in between the borders! Added to results. Hyperbolic solved: 36.8334779159501 [20250519_003655.]: Samplename: 37.5 Root: 36.833 --> Root in between the borders! Added to results. Hyperbolic solved: 52.0097895977263 [20250519_003655.]: Samplename: 50 Root: 52.01 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8930527921581 [20250519_003655.]: Samplename: 62.5 Root: 64.893 --> Root in between the borders! Added to results. Hyperbolic solved: 74.5671055499357 [20250519_003655.]: Samplename: 75 Root: 74.567 --> Root in between the borders! Added to results. Hyperbolic solved: 84.5294954832669 [20250519_003655.]: Samplename: 87.5 Root: 84.529 --> Root in between the borders! Added to results. Hyperbolic solved: 101.047146466811 [20250519_003655.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.047 --> '100 < root < 110' --> substitute 100 [20250519_003655.]: Solving hyperbolic regression for row_means Hyperbolic solved: 0.290940467580009 [20250519_003655.]: Samplename: 0 Root: 0.291 --> Root in between the borders! Added to results. Hyperbolic solved: 11.041240609297 [20250519_003655.]: Samplename: 12.5 Root: 11.041 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4081503210647 [20250519_003655.]: Samplename: 25 Root: 25.408 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5243723188613 [20250519_003655.]: Samplename: 37.5 Root: 36.524 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7348829528732 [20250519_003655.]: Samplename: 50 Root: 50.735 --> Root in between the borders! Added to results. Hyperbolic solved: 65.3135214250794 [20250519_003655.]: Samplename: 62.5 Root: 65.314 --> Root in between the borders! Added to results. Hyperbolic solved: 75.5342711952113 [20250519_003655.]: Samplename: 75 Root: 75.534 --> Root in between the borders! Added to results. Hyperbolic solved: 83.2411229564887 [20250519_003655.]: Samplename: 87.5 Root: 83.241 --> Root in between the borders! Added to results. Hyperbolic solved: 101.666942268285 [20250519_003655.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.667 --> '100 < root < 110' --> substitute 100 [20250519_003655.]: ### Starting with regression calculations ### [20250519_003655.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_003655.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.1698489850618, 24.4781920312644, 38.173044740918, 52.3349371964438, 65.4582773627666, 75.0090795260796, 81.5271920968417, 100)c(0, 0.330151014938201, 0.521807968735601, 0.673044740918002, 2.3349371964438, 2.95827736276659, 0.00907952607960283, 5.9728079031583, 0)c(NA, 2.64120811950561, 2.0872318749424, 1.79478597578134, 4.66987439288761, 4.73324378042655, 0.0121060347728038, 6.82606617503806, 0) [20250519_003655.]: Logging df_agg: CpG#1 [20250519_003655.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003655.]: c(0, 12.1698489850618, 24.4781920312644, 38.173044740918, 52.3349371964438, 65.4582773627666, 75.0090795260796, 81.5271920968417, 100)[20250519_003655.]: c(0, 0.330151014938201, 0.521807968735601, 0.673044740918002, 2.3349371964438, 2.95827736276659, 0.00907952607960283, 5.9728079031583, 0)[20250519_003655.]: c(NA, 2.64120811950561, 2.0872318749424, 1.79478597578134, 4.66987439288761, 4.73324378042655, 0.0121060347728038, 6.82606617503806, 0) [20250519_003655.]: Entered 'hyperbolic_regression'-Function [20250519_003655.]: 'hyperbolic_regression': minmax = FALSE [20250519_003655.]: Entered 'cubic_regression'-Function [20250519_003655.]: 'cubic_regression': minmax = FALSE [20250519_003655.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.13660501904968, 11.4129696733689, 26.174000526428, 35.1050449117028, 47.685500330611, 67.1440494417104, 75.7644668894086, 84.4054158616395, 100)c(1.13660501904968, 1.0870303266311, 1.174000526428, 2.3949550882972, 2.314499669389, 4.64404944171039, 0.764466889408595, 3.0945841383605, 0)c(NA, 8.6962426130488, 4.696002105712, 6.38654690212587, 4.628999338778, 7.43047910673663, 1.01928918587813, 3.53666758669772, 0) [20250519_003655.]: Logging df_agg: CpG#2 [20250519_003655.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003655.]: c(1.13660501904968, 11.4129696733689, 26.174000526428, 35.1050449117028, 47.685500330611, 67.1440494417104, 75.7644668894086, 84.4054158616395, 100)[20250519_003655.]: c(1.13660501904968, 1.0870303266311, 1.174000526428, 2.3949550882972, 2.314499669389, 4.64404944171039, 0.764466889408595, 3.0945841383605, 0)[20250519_003655.]: c(NA, 8.6962426130488, 4.696002105712, 6.38654690212587, 4.628999338778, 7.43047910673663, 1.01928918587813, 3.53666758669772, 0) [20250519_003655.]: Entered 'hyperbolic_regression'-Function [20250519_003655.]: 'hyperbolic_regression': minmax = FALSE [20250519_003656.]: Entered 'cubic_regression'-Function [20250519_003656.]: 'cubic_regression': minmax = FALSE [20250519_003656.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.51235653688495, 10.7523884294604, 25.5218907947761, 36.5270462675211, 50.7909245028224, 64.8686317550184, 77.5524188495235, 80.4374617358174, 100)c(0.51235653688495, 1.7476115705396, 0.521890794776098, 0.972953732478899, 0.790924502822399, 2.3686317550184, 2.55241884952351, 7.06253826418261, 0)c(NA, 13.9808925643168, 2.08756317910439, 2.5945432866104, 1.5818490056448, 3.78981080802944, 3.40322513269801, 8.07147230192298, 0) [20250519_003656.]: Logging df_agg: CpG#3 [20250519_003656.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003656.]: c(0.51235653688495, 10.7523884294604, 25.5218907947761, 36.5270462675211, 50.7909245028224, 64.8686317550184, 77.5524188495235, 80.4374617358174, 100)[20250519_003656.]: c(0.51235653688495, 1.7476115705396, 0.521890794776098, 0.972953732478899, 0.790924502822399, 2.3686317550184, 2.55241884952351, 7.06253826418261, 0)[20250519_003656.]: c(NA, 13.9808925643168, 2.08756317910439, 2.5945432866104, 1.5818490056448, 3.78981080802944, 3.40322513269801, 8.07147230192298, 0) [20250519_003656.]: Entered 'hyperbolic_regression'-Function [20250519_003656.]: 'hyperbolic_regression': minmax = FALSE [20250519_003656.]: Entered 'cubic_regression'-Function [20250519_003656.]: 'cubic_regression': minmax = FALSE [20250519_003656.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.4934147844872, 24.2685420024115, 38.0817128465023, 48.5843181174811, 67.6722399183037, 74.1549277799119, 82.8821797890026, 100)c(0, 0.00658521551279989, 0.7314579975885, 0.581712846502299, 1.4156818825189, 5.1722399183037, 0.845072220088099, 4.61782021099739, 0)c(NA, 0.0526817241023991, 2.925831990354, 1.55123425733946, 2.83136376503781, 8.27558386928592, 1.12676296011747, 5.27750881256845, 0) [20250519_003656.]: Logging df_agg: CpG#4 [20250519_003656.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003656.]: c(0, 12.4934147844872, 24.2685420024115, 38.0817128465023, 48.5843181174811, 67.6722399183037, 74.1549277799119, 82.8821797890026, 100)[20250519_003656.]: c(0, 0.00658521551279989, 0.7314579975885, 0.581712846502299, 1.4156818825189, 5.1722399183037, 0.845072220088099, 4.61782021099739, 0)[20250519_003656.]: c(NA, 0.0526817241023991, 2.925831990354, 1.55123425733946, 2.83136376503781, 8.27558386928592, 1.12676296011747, 5.27750881256845, 0) [20250519_003656.]: Entered 'hyperbolic_regression'-Function [20250519_003656.]: 'hyperbolic_regression': minmax = FALSE [20250519_003656.]: Entered 'cubic_regression'-Function [20250519_003656.]: 'cubic_regression': minmax = FALSE [20250519_003656.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.41558626275183, 10.1649674907454, 23.9830820412762, 37.2773619900429, 50.8659386543864, 62.4342273571069, 76.3915260534323, 86.159788778566, 100)c(2.41558626275183, 2.3350325092546, 1.0169179587238, 0.222638009957102, 0.865938654386397, 0.0657726428931014, 1.3915260534323, 1.340211221434, 0)c(NA, 18.6802600740368, 4.06767183489519, 0.593701359885604, 1.73187730877279, 0.105236228628962, 1.85536807124307, 1.53166996735315, 0) [20250519_003656.]: Logging df_agg: CpG#5 [20250519_003656.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003656.]: c(2.41558626275183, 10.1649674907454, 23.9830820412762, 37.2773619900429, 50.8659386543864, 62.4342273571069, 76.3915260534323, 86.159788778566, 100)[20250519_003656.]: c(2.41558626275183, 2.3350325092546, 1.0169179587238, 0.222638009957102, 0.865938654386397, 0.0657726428931014, 1.3915260534323, 1.340211221434, 0)[20250519_003656.]: c(NA, 18.6802600740368, 4.06767183489519, 0.593701359885604, 1.73187730877279, 0.105236228628962, 1.85536807124307, 1.53166996735315, 0) [20250519_003656.]: Entered 'hyperbolic_regression'-Function [20250519_003656.]: 'hyperbolic_regression': minmax = FALSE [20250519_003656.]: Entered 'cubic_regression'-Function [20250519_003656.]: 'cubic_regression': minmax = FALSE [20250519_003656.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.138163748613034, 11.8635558881981, 26.5107449550797, 35.3205073050661, 50.0570767570666, 64.9602944381018, 73.66890571617, 87.1266086585036, 100)c(0.138163748613034, 0.6364441118019, 1.5107449550797, 2.1794926949339, 0.0570767570666035, 2.4602944381018, 1.33109428383, 0.373391341496401, 0)c(NA, 5.0915528944152, 6.04297982031881, 5.81198051982373, 0.114153514133207, 3.93647110096288, 1.77479237844, 0.426732961710172, 0) [20250519_003656.]: Logging df_agg: CpG#6 [20250519_003656.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003656.]: c(0.138163748613034, 11.8635558881981, 26.5107449550797, 35.3205073050661, 50.0570767570666, 64.9602944381018, 73.66890571617, 87.1266086585036, 100)[20250519_003656.]: c(0.138163748613034, 0.6364441118019, 1.5107449550797, 2.1794926949339, 0.0570767570666035, 2.4602944381018, 1.33109428383, 0.373391341496401, 0)[20250519_003656.]: c(NA, 5.0915528944152, 6.04297982031881, 5.81198051982373, 0.114153514133207, 3.93647110096288, 1.77479237844, 0.426732961710172, 0) [20250519_003656.]: Entered 'hyperbolic_regression'-Function [20250519_003656.]: 'hyperbolic_regression': minmax = FALSE [20250519_003657.]: Entered 'cubic_regression'-Function [20250519_003657.]: 'cubic_regression': minmax = FALSE [20250519_003657.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.1993162352498, 24.595178967123, 37.8310421041787, 53.5588739724067, 65.9364947980258, 75.7361094434913, 79.432823759854, 100)c(0, 2.3006837647502, 0.404821032876999, 0.3310421041787, 3.5588739724067, 3.43649479802581, 0.736109443491301, 8.067176240146, 0)c(NA, 18.4054701180016, 1.619284131508, 0.882778944476532, 7.1177479448134, 5.49839167684129, 0.981479257988402, 9.21962998873828, 0) [20250519_003657.]: Logging df_agg: CpG#7 [20250519_003657.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003657.]: c(0, 10.1993162352498, 24.595178967123, 37.8310421041787, 53.5588739724067, 65.9364947980258, 75.7361094434913, 79.432823759854, 100)[20250519_003657.]: c(0, 2.3006837647502, 0.404821032876999, 0.3310421041787, 3.5588739724067, 3.43649479802581, 0.736109443491301, 8.067176240146, 0)[20250519_003657.]: c(NA, 18.4054701180016, 1.619284131508, 0.882778944476532, 7.1177479448134, 5.49839167684129, 0.981479257988402, 9.21962998873828, 0) [20250519_003657.]: Entered 'hyperbolic_regression'-Function [20250519_003657.]: 'hyperbolic_regression': minmax = FALSE [20250519_003657.]: Entered 'cubic_regression'-Function [20250519_003657.]: 'cubic_regression': minmax = FALSE [20250519_003657.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.80068218205093, 9.27535134596596, 25.4762621928197, 34.0122075735416, 51.7842655662325, 64.6732311906145, 78.4326978859189, 81.3427232852719, 100)c(2.80068218205093, 3.22464865403404, 0.4762621928197, 3.4877924264584, 1.7842655662325, 2.1732311906145, 3.4326978859189, 6.1572767147281, 0)c(NA, 25.7971892322723, 1.9050487712788, 9.30077980388906, 3.568531132465, 3.4771699049832, 4.57693051455853, 7.03688767397497, 0) [20250519_003657.]: Logging df_agg: CpG#8 [20250519_003657.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003657.]: c(2.80068218205093, 9.27535134596596, 25.4762621928197, 34.0122075735416, 51.7842655662325, 64.6732311906145, 78.4326978859189, 81.3427232852719, 100)[20250519_003657.]: c(2.80068218205093, 3.22464865403404, 0.4762621928197, 3.4877924264584, 1.7842655662325, 2.1732311906145, 3.4326978859189, 6.1572767147281, 0)[20250519_003657.]: c(NA, 25.7971892322723, 1.9050487712788, 9.30077980388906, 3.568531132465, 3.4771699049832, 4.57693051455853, 7.03688767397497, 0) [20250519_003657.]: Entered 'hyperbolic_regression'-Function [20250519_003657.]: 'hyperbolic_regression': minmax = FALSE [20250519_003657.]: Entered 'cubic_regression'-Function [20250519_003657.]: 'cubic_regression': minmax = FALSE [20250519_003657.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.5082192457956, 26.9164567253388, 36.8334779159501, 52.0097895977263, 64.8930527921581, 74.5671055499357, 84.5294954832669, 100)c(0, 1.9917807542044, 1.9164567253388, 0.666522084049902, 2.0097895977263, 2.3930527921581, 0.432894450064296, 2.9705045167331, 0)c(NA, 15.9342460336352, 7.6658269013552, 1.77739222413307, 4.01957919545259, 3.82888446745296, 0.577192600085728, 3.39486230483783, 0) [20250519_003657.]: Logging df_agg: CpG#9 [20250519_003657.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003657.]: c(0, 10.5082192457956, 26.9164567253388, 36.8334779159501, 52.0097895977263, 64.8930527921581, 74.5671055499357, 84.5294954832669, 100)[20250519_003657.]: c(0, 1.9917807542044, 1.9164567253388, 0.666522084049902, 2.0097895977263, 2.3930527921581, 0.432894450064296, 2.9705045167331, 0)[20250519_003657.]: c(NA, 15.9342460336352, 7.6658269013552, 1.77739222413307, 4.01957919545259, 3.82888446745296, 0.577192600085728, 3.39486230483783, 0) [20250519_003657.]: Entered 'hyperbolic_regression'-Function [20250519_003657.]: 'hyperbolic_regression': minmax = FALSE [20250519_003657.]: Entered 'cubic_regression'-Function [20250519_003657.]: 'cubic_regression': minmax = FALSE [20250519_003657.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.290940467580009, 11.041240609297, 25.4081503210647, 36.5243723188613, 50.7348829528732, 65.3135214250794, 75.5342711952113, 83.2411229564887, 100)c(0.290940467580009, 1.458759390703, 0.408150321064699, 0.975627681138697, 0.7348829528732, 2.8135214250794, 0.534271195211304, 4.2588770435113, 0)c(NA, 11.670075125624, 1.6326012842588, 2.60167381636986, 1.4697659057464, 4.50163428012704, 0.712361593615071, 4.8672880497272, 0) [20250519_003657.]: Logging df_agg: row_means [20250519_003657.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003657.]: c(0.290940467580009, 11.041240609297, 25.4081503210647, 36.5243723188613, 50.7348829528732, 65.3135214250794, 75.5342711952113, 83.2411229564887, 100)[20250519_003657.]: c(0.290940467580009, 1.458759390703, 0.408150321064699, 0.975627681138697, 0.7348829528732, 2.8135214250794, 0.534271195211304, 4.2588770435113, 0)[20250519_003657.]: c(NA, 11.670075125624, 1.6326012842588, 2.60167381636986, 1.4697659057464, 4.50163428012704, 0.712361593615071, 4.8672880497272, 0) [20250519_003657.]: Entered 'hyperbolic_regression'-Function [20250519_003657.]: 'hyperbolic_regression': minmax = FALSE [20250519_003658.]: Entered 'cubic_regression'-Function [20250519_003658.]: 'cubic_regression': minmax = FALSE [20250519_003658.]: ### Starting with plotting ### [20250519_003658.]: Creating BiasCorrected (hyperbolic) plot No. 1 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG1_corrected_h.png [20250519_003658.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -657.853491622191 , a = 985.267531706584 , b = -657.853491622191 , d = 903.45414536045 [20250519_003658.]: # CpG-site: CpG#1 Cubic: Using a = 5.18463232357963e-06 , b = -0.00180592068110451 , c = 1.11404020909943 , d = -0.858600112691325 [20250519_003700.]: Creating BiasCorrected (hyperbolic) plot No. 2 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG2_corrected_h.png [20250519_003700.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = 3399.55230393578 , a = 30276.0421015763 , b = 3399.55230393578 , d = 30352.4984502595 [20250519_003700.]: # CpG-site: CpG#2 Cubic: Using a = -2.58097549676629e-05 , b = 0.00383753278164476 , c = 0.8516077608074 , d = 0.957127237506517 [20250519_003701.]: Creating BiasCorrected (hyperbolic) plot No. 3 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG3_corrected_h.png [20250519_003701.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = -945.757057025882 , a = 1333.61975246108 , b = -945.757057025882 , d = 1252.95856205348 [20250519_003701.]: # CpG-site: CpG#3 Cubic: Using a = -1.13948611491254e-05 , b = 0.000937180276062016 , c = 0.998630620080636 , d = -0.383337686690576 [20250519_003703.]: Creating BiasCorrected (hyperbolic) plot No. 4 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG4_corrected_h.png [20250519_003703.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = -754.050736416394 , a = 1637.26786961468 , b = -754.050736416394 , d = 1558.27361713715 [20250519_003703.]: # CpG-site: CpG#4 Cubic: Using a = -3.25779988054422e-06 , b = -0.000129596030700936 , c = 1.03077246222439 , d = -0.370343704072989 [20250519_003704.]: Creating BiasCorrected (hyperbolic) plot No. 5 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG5_corrected_h.png [20250519_003704.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = -1917.44420974597 , a = -5621.4534701009 , b = -1917.44420974597 , d = -5734.47300613195 [20250519_003704.]: # CpG-site: CpG#5 Cubic: Using a = -3.16782834876228e-05 , b = 0.00493527189107616 , c = 0.800194601834967 , d = 1.38653015028503 [20250519_003706.]: Creating BiasCorrected (hyperbolic) plot No. 6 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG6_corrected_h.png [20250519_003706.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = -1255.67712347228 , a = 13775.5934089325 , b = -1255.67712347228 , d = 13690.4825730065 [20250519_003706.]: # CpG-site: CpG#6 Cubic: Using a = -3.70201684653088e-06 , b = 0.000482210694203131 , c = 0.985293133118367 , d = 0.0293568698433451 [20250519_003707.]: Creating BiasCorrected (hyperbolic) plot No. 7 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG7_corrected_h.png [20250519_003707.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -1065.05415478934 , a = 722.083975601617 , b = -1065.05415478934 , d = 634.402060442185 [20250519_003707.]: # CpG-site: CpG#7 Cubic: Using a = -1.04111972402046e-06 , b = -0.00130150084904922 , c = 1.12399516228677 , d = -1.5985899870939 [20250519_003709.]: Creating BiasCorrected (hyperbolic) plot No. 8 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG8_corrected_h.png [20250519_003709.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = 68.5784871630731 , a = 4784.90969361316 , b = 68.5784871630731 , d = 4739.12296431021 [20250519_003709.]: # CpG-site: CpG#8 Cubic: Using a = -4.65730502627556e-05 , b = 0.00676525570440644 , c = 0.747405744075959 , d = 1.52337158754213 [20250519_003710.]: Creating BiasCorrected (hyperbolic) plot No. 9 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG9_corrected_h.png [20250519_003710.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -1022.70169872734 , a = 1273.73039431947 , b = -1022.70169872734 , d = 1181.86496720377 [20250519_003710.]: # CpG-site: CpG#9 Cubic: Using a = 4.24790097543708e-06 , b = -0.00144169637811555 , c = 1.09837791472274 , d = -0.97898795630014 [20250519_003712.]: Creating BiasCorrected (hyperbolic) plot No. 10 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_rowmeans_corrected_h.png [20250519_003712.]: # CpG-site: row_means Hyperbolic: Using bias_weight = -1166.73858297355 , a = 1740.63784318208 , b = -1166.73858297355 , d = 1653.86071043116 [20250519_003712.]: # CpG-site: row_means Cubic: Using a = -8.71186803343537e-06 , b = 0.000715553560638339 , c = 1.00248252890793 , d = -0.420554394203132 [20250519_003713.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG1_corrected_h.png [20250519_003715.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG2_corrected_h.png [20250519_003716.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG3_corrected_h.png [20250519_003717.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG4_corrected_h.png [20250519_003719.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG5_corrected_h.png [20250519_003720.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG6_corrected_h.png [20250519_003721.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG7_corrected_h.png [20250519_003723.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG8_corrected_h.png [20250519_003724.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG9_corrected_h.png [20250519_003725.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_rowmeans_corrected_h.png [20250519_003727.]: Entered 'solving_equations'-Function [20250519_003727.]: Solving cubic regression for CpG#1 Coefficients: -1.03617340067344Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_003727.]: Samplename: 0 Root: 1.334 --> Root in between the borders! Added to results. Coefficients: -8.34150673400678Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_003727.]: Samplename: 12.5 Root: 11.446 --> Root in between the borders! Added to results. Coefficients: -15.3881734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_003727.]: Samplename: 25 Root: 22.228 --> Root in between the borders! Added to results. Coefficients: -24.2801734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_003727.]: Samplename: 37.5 Root: 36.374 --> Root in between the borders! Added to results. Coefficients: -34.9006734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_003727.]: Samplename: 50 Root: 52.044 --> Root in between the borders! Added to results. Coefficients: -46.3541734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_003727.]: Samplename: 62.5 Root: 66.144 --> Root in between the borders! Added to results. Coefficients: -55.8931734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_003727.]: Samplename: 75 Root: 75.864 --> Root in between the borders! Added to results. Coefficients: -63.0981734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_003727.]: Samplename: 87.5 Root: 82.254 --> Root in between the borders! Added to results. Coefficients: -91.0461734006735Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_003727.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.877 --> '100 < root < 110' --> substitute 100 [20250519_003727.]: Solving cubic regression for CpG#2 Coefficients: -0.283329966329966Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_003727.]: Samplename: 0 Root: 0.549 --> Root in between the borders! Added to results. Coefficients: -6.33999663299663Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_003727.]: Samplename: 12.5 Root: 11.533 --> Root in between the borders! Added to results. Coefficients: -15.93932996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_003727.]: Samplename: 25 Root: 26.628 --> Root in between the borders! Added to results. Coefficients: -22.33732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_003727.]: Samplename: 37.5 Root: 35.509 --> Root in between the borders! Added to results. Coefficients: -32.22832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_003727.]: Samplename: 50 Root: 47.851 --> Root in between the borders! Added to results. Coefficients: -49.96332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_003727.]: Samplename: 62.5 Root: 66.893 --> Root in between the borders! Added to results. Coefficients: -58.96582996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_003727.]: Samplename: 75 Root: 75.431 --> Root in between the borders! Added to results. Coefficients: -68.8366632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_003727.]: Samplename: 87.5 Root: 84.118 --> Root in between the borders! Added to results. Coefficients: -90.57732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_003727.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.287 --> '100 < root < 110' --> substitute 100 [20250519_003727.]: Solving cubic regression for CpG#3 Coefficients: -0.90294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_003727.]: Samplename: 0 Root: 1.441 --> Root in between the borders! Added to results. Coefficients: -6.57294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_003727.]: Samplename: 12.5 Root: 10.568 --> Root in between the borders! Added to results. Coefficients: -15.4289478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_003727.]: Samplename: 25 Root: 24.796 --> Root in between the borders! Added to results. Coefficients: -22.6129478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_003727.]: Samplename: 37.5 Root: 35.952 --> Root in between the borders! Added to results. Coefficients: -32.7754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_003727.]: Samplename: 50 Root: 50.684 --> Root in between the borders! Added to results. Coefficients: -43.8889478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_003727.]: Samplename: 62.5 Root: 65.142 --> Root in between the borders! Added to results. Coefficients: -54.9754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_003727.]: Samplename: 75 Root: 77.905 --> Root in between the borders! Added to results. Coefficients: -57.6562811447812Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_003727.]: Samplename: 87.5 Root: 80.767 --> Root in between the borders! Added to results. Coefficients: -80.6649478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_003727.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.38 --> '100 < root < 110' --> substitute 100 [20250519_003727.]: Solving cubic regression for CpG#4 Coefficients: -0.597449494949524Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_003727.]: Samplename: 0 Root: 0.858 --> Root in between the borders! Added to results. Coefficients: -8.25278282828286Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_003727.]: Samplename: 12.5 Root: 12.086 --> Root in between the borders! Added to results. Coefficients: -15.8034494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_003727.]: Samplename: 25 Root: 23.316 --> Root in between the borders! Added to results. Coefficients: -25.5274494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_003727.]: Samplename: 37.5 Root: 37.383 --> Root in between the borders! Added to results. Coefficients: -33.6369494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_003727.]: Samplename: 50 Root: 48.353 --> Root in between the borders! Added to results. Coefficients: -50.2554494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_003727.]: Samplename: 62.5 Root: 68.082 --> Root in between the borders! Added to results. Coefficients: -56.5394494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_003727.]: Samplename: 75 Root: 74.615 --> Root in between the borders! Added to results. Coefficients: -65.5927828282829Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_003727.]: Samplename: 87.5 Root: 83.254 --> Root in between the borders! Added to results. Coefficients: -88.3214494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_003727.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.715 --> '100 < root < 110' --> substitute 100 [20250519_003727.]: Solving cubic regression for CpG#5 Coefficients: -0.623961279461278Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_003727.]: Samplename: 0 Root: 1.458 --> Root in between the borders! Added to results. Coefficients: -4.76796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_003727.]: Samplename: 12.5 Root: 10.347 --> Root in between the borders! Added to results. Coefficients: -12.7259612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_003727.]: Samplename: 25 Root: 24.815 --> Root in between the borders! Added to results. Coefficients: -21.1599612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_003727.]: Samplename: 37.5 Root: 37.902 --> Root in between the borders! Added to results. Coefficients: -30.6954612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_003727.]: Samplename: 50 Root: 50.977 --> Root in between the borders! Added to results. Coefficients: -39.6579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_003727.]: Samplename: 62.5 Root: 62.126 --> Root in between the borders! Added to results. Coefficients: -51.6829612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_003727.]: Samplename: 75 Root: 75.852 --> Root in between the borders! Added to results. Coefficients: -61.0146279461279Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_003727.]: Samplename: 87.5 Root: 85.767 --> Root in between the borders! Added to results. Coefficients: -76.0699612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_003727.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.743 --> '100 < root < 110' --> substitute 100 [20250519_003727.]: Solving cubic regression for CpG#6 Coefficients: -0.196072390572403Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_003727.]: Samplename: 0 Root: 0.349 --> Root in between the borders! Added to results. Coefficients: -6.73873905723907Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_003727.]: Samplename: 12.5 Root: 11.718 --> Root in between the borders! Added to results. Coefficients: -15.8880723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_003727.]: Samplename: 25 Root: 26.396 --> Root in between the borders! Added to results. Coefficients: -22.0000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_003727.]: Samplename: 37.5 Root: 35.301 --> Root in between the borders! Added to results. Coefficients: -33.4445723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_003727.]: Samplename: 50 Root: 50.134 --> Root in between the borders! Added to results. Coefficients: -46.9000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_003727.]: Samplename: 62.5 Root: 64.993 --> Root in between the borders! Added to results. Coefficients: -55.8320723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_003727.]: Samplename: 75 Root: 73.639 --> Root in between the borders! Added to results. Coefficients: -71.5454057239057Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_003727.]: Samplename: 87.5 Root: 87.043 --> Root in between the borders! Added to results. Coefficients: -89.6560723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_003727.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.329 --> '100 < root < 110' --> substitute 100 [20250519_003727.]: Solving cubic regression for CpG#7 Coefficients: -1.21495454545456Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_003727.]: Samplename: 0 Root: 2.13 --> Root in between the borders! Added to results. Coefficients: -5.39562121212123Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_003727.]: Samplename: 12.5 Root: 9.973 --> Root in between the borders! Added to results. Coefficients: -11.2649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_003727.]: Samplename: 25 Root: 22.206 --> Root in between the borders! Added to results. Coefficients: -17.4509545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_003727.]: Samplename: 37.5 Root: 35.814 --> Root in between the borders! Added to results. Coefficients: -26.0314545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_003727.]: Samplename: 50 Root: 53.28 --> Root in between the borders! Added to results. Coefficients: -33.9649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_003727.]: Samplename: 62.5 Root: 66.598 --> Root in between the borders! Added to results. Coefficients: -41.1689545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_003727.]: Samplename: 75 Root: 76.575 --> Root in between the borders! Added to results. Coefficients: -44.1356212121212Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_003727.]: Samplename: 87.5 Root: 80.219 --> Root in between the borders! Added to results. Coefficients: -67.2229545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_003727.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.506 --> '100 < root < 110' --> substitute 100 [20250519_003727.]: Solving cubic regression for CpG#8 Coefficients: -1.09618518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_003727.]: Samplename: 0 Root: 2.016 --> Root in between the borders! Added to results. Coefficients: -5.44685185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_003727.]: Samplename: 12.5 Root: 9.458 --> Root in between the borders! Added to results. Coefficients: -16.9301851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_003727.]: Samplename: 25 Root: 26.35 --> Root in between the borders! Added to results. Coefficients: -23.3501851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_003727.]: Samplename: 37.5 Root: 34.728 --> Root in between the borders! Added to results. Coefficients: -37.6251851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_003727.]: Samplename: 50 Root: 51.781 --> Root in between the borders! Added to results. Coefficients: -48.8261851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_003727.]: Samplename: 62.5 Root: 64.192 --> Root in between the borders! Added to results. Coefficients: -61.6676851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_003727.]: Samplename: 75 Root: 77.804 --> Root in between the borders! Added to results. Coefficients: -64.5101851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_003727.]: Samplename: 87.5 Root: 80.758 --> Root in between the borders! Added to results. Coefficients: -86.0601851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_003727.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.834 --> '100 < root < 110' --> substitute 100 [20250519_003727.]: Solving cubic regression for CpG#9 Coefficients: -0.989865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_003727.]: Samplename: 0 Root: 1.475 --> Root in between the borders! Added to results. Coefficients: -6.39586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_003727.]: Samplename: 12.5 Root: 10.12 --> Root in between the borders! Added to results. Coefficients: -14.7058653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_003727.]: Samplename: 25 Root: 24.844 --> Root in between the borders! Added to results. Coefficients: -20.6238653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_003727.]: Samplename: 37.5 Root: 35.327 --> Root in between the borders! Added to results. Coefficients: -31.3958653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_003727.]: Samplename: 50 Root: 51.855 --> Root in between the borders! Added to results. Coefficients: -42.6858653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_003727.]: Samplename: 62.5 Root: 65.265 --> Root in between the borders! Added to results. Coefficients: -52.9033653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_003727.]: Samplename: 75 Root: 74.915 --> Root in between the borders! Added to results. Coefficients: -65.492531986532Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_003727.]: Samplename: 87.5 Root: 84.67 --> Root in between the borders! Added to results. Coefficients: -92.9898653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_003727.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.082 --> '100 < root < 110' --> substitute 100 [20250519_003727.]: Solving cubic regression for row_means Coefficients: -0.771215488215478Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_003727.]: Samplename: 0 Root: 1.287 --> Root in between the borders! Added to results. Coefficients: -6.47247474747474Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_003727.]: Samplename: 12.5 Root: 10.847 --> Root in between the borders! Added to results. Coefficients: -14.8972154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_003727.]: Samplename: 25 Root: 24.737 --> Root in between the borders! Added to results. Coefficients: -22.1492154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_003727.]: Samplename: 37.5 Root: 36.02 --> Root in between the borders! Added to results. Coefficients: -32.5259932659933Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_003727.]: Samplename: 50 Root: 50.639 --> Root in between the borders! Added to results. Coefficients: -44.7218821548821Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_003727.]: Samplename: 62.5 Root: 65.497 --> Root in between the borders! Added to results. Coefficients: -54.4032154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_003727.]: Samplename: 75 Root: 75.751 --> Root in between the borders! Added to results. Coefficients: -62.4313636363636Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_003727.]: Samplename: 87.5 Root: 83.403 --> Root in between the borders! Added to results. Coefficients: -84.7343265993266Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_003727.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.573 --> '100 < root < 110' --> substitute 100 [20250519_003727.]: ### Starting with regression calculations ### [20250519_003727.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_003727.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) [20250519_003727.]: Logging df_agg: CpG#1 [20250519_003727.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003727.]: c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)[20250519_003727.]: c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)[20250519_003727.]: c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) [20250519_003727.]: Entered 'hyperbolic_regression'-Function [20250519_003727.]: 'hyperbolic_regression': minmax = FALSE [20250519_003728.]: Entered 'cubic_regression'-Function [20250519_003728.]: 'cubic_regression': minmax = FALSE [20250519_003728.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) [20250519_003728.]: Logging df_agg: CpG#2 [20250519_003728.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003728.]: c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)[20250519_003728.]: c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)[20250519_003728.]: c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) [20250519_003728.]: Entered 'hyperbolic_regression'-Function [20250519_003728.]: 'hyperbolic_regression': minmax = FALSE [20250519_003728.]: Entered 'cubic_regression'-Function [20250519_003728.]: 'cubic_regression': minmax = FALSE [20250519_003728.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) [20250519_003728.]: Logging df_agg: CpG#3 [20250519_003728.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003728.]: c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)[20250519_003728.]: c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)[20250519_003728.]: c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) [20250519_003728.]: Entered 'hyperbolic_regression'-Function [20250519_003728.]: 'hyperbolic_regression': minmax = FALSE [20250519_003728.]: Entered 'cubic_regression'-Function [20250519_003728.]: 'cubic_regression': minmax = FALSE [20250519_003728.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) [20250519_003728.]: Logging df_agg: CpG#4 [20250519_003728.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003728.]: c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)[20250519_003728.]: c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)[20250519_003728.]: c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) [20250519_003728.]: Entered 'hyperbolic_regression'-Function [20250519_003728.]: 'hyperbolic_regression': minmax = FALSE [20250519_003728.]: Entered 'cubic_regression'-Function [20250519_003728.]: 'cubic_regression': minmax = FALSE [20250519_003728.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) [20250519_003728.]: Logging df_agg: CpG#5 [20250519_003728.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003728.]: c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)[20250519_003728.]: c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)[20250519_003728.]: c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) [20250519_003728.]: Entered 'hyperbolic_regression'-Function [20250519_003728.]: 'hyperbolic_regression': minmax = FALSE [20250519_003729.]: Entered 'cubic_regression'-Function [20250519_003729.]: 'cubic_regression': minmax = FALSE [20250519_003729.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) [20250519_003729.]: Logging df_agg: CpG#6 [20250519_003729.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003729.]: c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)[20250519_003729.]: c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)[20250519_003729.]: c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) [20250519_003729.]: Entered 'hyperbolic_regression'-Function [20250519_003729.]: 'hyperbolic_regression': minmax = FALSE [20250519_003729.]: Entered 'cubic_regression'-Function [20250519_003729.]: 'cubic_regression': minmax = FALSE [20250519_003729.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) [20250519_003729.]: Logging df_agg: CpG#7 [20250519_003729.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003729.]: c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)[20250519_003729.]: c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)[20250519_003729.]: c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) [20250519_003729.]: Entered 'hyperbolic_regression'-Function [20250519_003729.]: 'hyperbolic_regression': minmax = FALSE [20250519_003729.]: Entered 'cubic_regression'-Function [20250519_003729.]: 'cubic_regression': minmax = FALSE [20250519_003729.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) [20250519_003729.]: Logging df_agg: CpG#8 [20250519_003729.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003729.]: c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)[20250519_003729.]: c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)[20250519_003729.]: c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) [20250519_003729.]: Entered 'hyperbolic_regression'-Function [20250519_003729.]: 'hyperbolic_regression': minmax = FALSE [20250519_003729.]: Entered 'cubic_regression'-Function [20250519_003729.]: 'cubic_regression': minmax = FALSE [20250519_003729.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) [20250519_003729.]: Logging df_agg: CpG#9 [20250519_003729.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003729.]: c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)[20250519_003729.]: c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)[20250519_003729.]: c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) [20250519_003729.]: Entered 'hyperbolic_regression'-Function [20250519_003729.]: 'hyperbolic_regression': minmax = FALSE [20250519_003730.]: Entered 'cubic_regression'-Function [20250519_003730.]: 'cubic_regression': minmax = FALSE [20250519_003730.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) [20250519_003730.]: Logging df_agg: row_means [20250519_003730.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003730.]: c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)[20250519_003730.]: c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)[20250519_003730.]: c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) [20250519_003730.]: Entered 'hyperbolic_regression'-Function [20250519_003730.]: 'hyperbolic_regression': minmax = FALSE [20250519_003730.]: Entered 'cubic_regression'-Function [20250519_003730.]: 'cubic_regression': minmax = FALSE [20250519_003730.]: ### Starting with plotting ### [20250519_003731.]: Creating BiasCorrected (cubic) plot No. 1 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG1_corrected_c.png [20250519_003731.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -1164.77580684101 , a = 2039.49844669959 , b = -1164.77580684101 , d = 1955.83553683067 [20250519_003731.]: # CpG-site: CpG#1 Cubic: Using a = -3.42821196240366e-05 , b = 0.00462173155013798 , c = 0.850326258865826 , d = 0.500073874211125 [20250519_003732.]: Creating BiasCorrected (cubic) plot No. 2 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG2_corrected_c.png [20250519_003732.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = -464.128853555559 , a = 4988.85258428155 , b = -464.128853555559 , d = 4925.32559808582 [20250519_003732.]: # CpG-site: CpG#2 Cubic: Using a = -1.40811796906314e-05 , b = 0.00190849431754485 , c = 0.933455870941473 , d = 0.363096210099674 [20250519_003733.]: Creating BiasCorrected (cubic) plot No. 3 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG3_corrected_c.png [20250519_003733.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = -745.611435752166 , a = 2026.53033232932 , b = -745.611435752166 , d = 1953.28002286922 [20250519_003733.]: # CpG-site: CpG#3 Cubic: Using a = -2.78835051857186e-05 , b = 0.00366863707889089 , c = 0.880665171220893 , d = 0.510779308833653 [20250519_003735.]: Creating BiasCorrected (cubic) plot No. 4 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG4_corrected_c.png [20250519_003735.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = -833.073059054674 , a = 2871.1407505286 , b = -833.073059054674 , d = 2796.8155099796 [20250519_003735.]: # CpG-site: CpG#4 Cubic: Using a = -2.30028865643e-05 , b = 0.00309017851940692 , c = 0.897073107777645 , d = 0.443455761841866 [20250519_003736.]: Creating BiasCorrected (cubic) plot No. 5 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG5_corrected_c.png [20250519_003736.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = 222.127659665624 , a = 12477.5957294815 , b = 222.127659665624 , d = 12432.2636419037 [20250519_003736.]: # CpG-site: CpG#5 Cubic: Using a = -1.28632696989134e-05 , b = 0.00184822285299014 , c = 0.931001046631176 , d = 0.437963095185599 [20250519_003738.]: Creating BiasCorrected (cubic) plot No. 6 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG6_corrected_c.png [20250519_003738.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = -700.510534379779 , a = 21299.1825568909 , b = -700.510534379779 , d = 21234.5878606004 [20250519_003738.]: # CpG-site: CpG#6 Cubic: Using a = -5.838335151375e-06 , b = 0.000828447163218526 , c = 0.970047764475166 , d = 0.158074701412251 [20250519_003739.]: Creating BiasCorrected (cubic) plot No. 7 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG7_corrected_c.png [20250519_003739.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -1144.90822837929 , a = 1477.91227480711 , b = -1144.90822837929 , d = 1394.70075909546 [20250519_003739.]: # CpG-site: CpG#7 Cubic: Using a = -4.54220535614047e-05 , b = 0.00607993130565131 , c = 0.805411493452389 , d = 0.615125382790254 [20250519_003741.]: Creating BiasCorrected (cubic) plot No. 8 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG8_corrected_c.png [20250519_003741.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = -349.378022452652 , a = 2377.37834502072 , b = -349.378022452652 , d = 2315.2055766781 [20250519_003741.]: # CpG-site: CpG#8 Cubic: Using a = -2.64622306669639e-05 , b = 0.00353621593528015 , c = 0.877916844709074 , d = 0.698684606493051 [20250519_003742.]: Creating BiasCorrected (cubic) plot No. 9 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG9_corrected_c.png [20250519_003742.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -1536.91601393605 , a = 3745.17687624221 , b = -1536.91601393605 , d = 3656.2687244524 [20250519_003742.]: # CpG-site: CpG#9 Cubic: Using a = -2.70976130611725e-05 , b = 0.00378478247182342 , c = 0.871825263051793 , d = 0.455475740974113 [20250519_003743.]: Creating BiasCorrected (cubic) plot No. 10 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_rowmeans_corrected_c.png [20250519_003743.]: # CpG-site: row_means Hyperbolic: Using bias_weight = -820.650348722241 , a = 3198.46441212347 , b = -820.650348722241 , d = 3124.20895615508 [20250519_003743.]: # CpG-site: row_means Cubic: Using a = -2.34328362100009e-05 , b = 0.00319139073176266 , c = 0.891842168032129 , d = 0.493517689787135 [20250519_003745.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG1_corrected_c.png [20250519_003746.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG2_corrected_c.png [20250519_003748.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG3_corrected_c.png [20250519_003749.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG4_corrected_c.png [20250519_003750.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG5_corrected_c.png [20250519_003752.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG6_corrected_c.png [20250519_003753.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG7_corrected_c.png [20250519_003754.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG8_corrected_c.png [20250519_003755.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG9_corrected_c.png [20250519_003757.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_rowmeans_corrected_c.png [20250519_003758.]: Entered 'solving_equations'-Function [20250519_003758.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 79.8673456895745 [20250519_003758.]: Samplename: Sample#1 Root: 79.867 --> Root in between the borders! Added to results. Hyperbolic solved: 29.7900184340805 [20250519_003758.]: Samplename: Sample#10 Root: 29.79 --> Root in between the borders! Added to results. Hyperbolic solved: 41.6525415639691 [20250519_003758.]: Samplename: Sample#2 Root: 41.653 --> Root in between the borders! Added to results. Hyperbolic solved: 57.4652090254513 [20250519_003758.]: Samplename: Sample#3 Root: 57.465 --> Root in between the borders! Added to results. Hyperbolic solved: 9.2007130627765 [20250519_003758.]: Samplename: Sample#4 Root: 9.201 --> Root in between the borders! Added to results. Hyperbolic solved: 21.8059600538131 [20250519_003758.]: Samplename: Sample#5 Root: 21.806 --> Root in between the borders! Added to results. Hyperbolic solved: 23.083796735881 [20250519_003758.]: Samplename: Sample#6 Root: 23.084 --> Root in between the borders! Added to results. Hyperbolic solved: 45.5034245569385 [20250519_003758.]: Samplename: Sample#7 Root: 45.503 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6987904075704 [20250519_003758.]: Samplename: Sample#8 Root: 85.699 --> Root in between the borders! Added to results. Hyperbolic solved: -3.66512807265101 [20250519_003758.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -3.665 --> '-10 < root < 0' --> substitute 0 [20250519_003758.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 76.7227931065881 [20250519_003758.]: Samplename: Sample#1 Root: 76.723 --> Root in between the borders! Added to results. Hyperbolic solved: 30.992427511473 [20250519_003758.]: Samplename: Sample#10 Root: 30.992 --> Root in between the borders! Added to results. Hyperbolic solved: 42.6810882780827 [20250519_003758.]: Samplename: Sample#2 Root: 42.681 --> Root in between the borders! Added to results. Hyperbolic solved: 58.9337908379889 [20250519_003758.]: Samplename: Sample#3 Root: 58.934 --> Root in between the borders! Added to results. Hyperbolic solved: 4.98020457313721 [20250519_003758.]: Samplename: Sample#4 Root: 4.98 --> Root in between the borders! Added to results. Hyperbolic solved: 20.2480263675669 [20250519_003758.]: Samplename: Sample#5 Root: 20.248 --> Root in between the borders! Added to results. Hyperbolic solved: 17.8133085643845 [20250519_003758.]: Samplename: Sample#6 Root: 17.813 --> Root in between the borders! Added to results. Hyperbolic solved: 40.9176745340012 [20250519_003758.]: Samplename: Sample#7 Root: 40.918 --> Root in between the borders! Added to results. Hyperbolic solved: 86.0450715732916 [20250519_003758.]: Samplename: Sample#8 Root: 86.045 --> Root in between the borders! Added to results. Hyperbolic solved: 3.11579770508969 [20250519_003758.]: Samplename: Sample#9 Root: 3.116 --> Root in between the borders! Added to results. [20250519_003758.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 74.9349254100163 [20250519_003758.]: Samplename: Sample#1 Root: 74.935 --> Root in between the borders! Added to results. Hyperbolic solved: 27.6844381581493 [20250519_003758.]: Samplename: Sample#10 Root: 27.684 --> Root in between the borders! Added to results. Hyperbolic solved: 41.852019114379 [20250519_003758.]: Samplename: Sample#2 Root: 41.852 --> Root in between the borders! Added to results. Hyperbolic solved: 55.8325180209418 [20250519_003758.]: Samplename: Sample#3 Root: 55.833 --> Root in between the borders! Added to results. Hyperbolic solved: 8.03519251633153 [20250519_003758.]: Samplename: Sample#4 Root: 8.035 --> Root in between the borders! Added to results. Hyperbolic solved: 24.1066315721853 [20250519_003758.]: Samplename: Sample#5 Root: 24.107 --> Root in between the borders! Added to results. Hyperbolic solved: 26.2419820027673 [20250519_003758.]: Samplename: Sample#6 Root: 26.242 --> Root in between the borders! Added to results. Hyperbolic solved: 44.0944922703422 [20250519_003758.]: Samplename: Sample#7 Root: 44.094 --> Root in between the borders! Added to results. Hyperbolic solved: 85.8279382585787 [20250519_003758.]: Samplename: Sample#8 Root: 85.828 --> Root in between the borders! Added to results. Hyperbolic solved: -0.666482392725758 [20250519_003758.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.666 --> '-10 < root < 0' --> substitute 0 [20250519_003758.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 76.3495278640236 [20250519_003758.]: Samplename: Sample#1 Root: 76.35 --> Root in between the borders! Added to results. Hyperbolic solved: 28.2568553570941 [20250519_003758.]: Samplename: Sample#10 Root: 28.257 --> Root in between the borders! Added to results. Hyperbolic solved: 43.4089839390807 [20250519_003758.]: Samplename: Sample#2 Root: 43.409 --> Root in between the borders! Added to results. Hyperbolic solved: 58.5435236860146 [20250519_003758.]: Samplename: Sample#3 Root: 58.544 --> Root in between the borders! Added to results. Hyperbolic solved: 10.3087045690571 [20250519_003758.]: Samplename: Sample#4 Root: 10.309 --> Root in between the borders! Added to results. Hyperbolic solved: 22.183045165659 [20250519_003758.]: Samplename: Sample#5 Root: 22.183 --> Root in between the borders! Added to results. Hyperbolic solved: 27.1337769553499 [20250519_003758.]: Samplename: Sample#6 Root: 27.134 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8321096080155 [20250519_003758.]: Samplename: Sample#7 Root: 41.832 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6890189074743 [20250519_003758.]: Samplename: Sample#8 Root: 85.689 --> Root in between the borders! Added to results. Hyperbolic solved: 2.42232098177269 [20250519_003758.]: Samplename: Sample#9 Root: 2.422 --> Root in between the borders! Added to results. [20250519_003758.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 72.8126914355011 [20250519_003758.]: Samplename: Sample#1 Root: 72.813 --> Root in between the borders! Added to results. Hyperbolic solved: 26.4206935815673 [20250519_003758.]: Samplename: Sample#10 Root: 26.421 --> Root in between the borders! Added to results. Hyperbolic solved: 44.2738765057261 [20250519_003758.]: Samplename: Sample#2 Root: 44.274 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5324386232341 [20250519_003758.]: Samplename: Sample#3 Root: 53.532 --> Root in between the borders! Added to results. Hyperbolic solved: 10.0373717962154 [20250519_003758.]: Samplename: Sample#4 Root: 10.037 --> Root in between the borders! Added to results. Hyperbolic solved: 22.8733115052607 [20250519_003758.]: Samplename: Sample#5 Root: 22.873 --> Root in between the borders! Added to results. Hyperbolic solved: 25.9417915784748 [20250519_003758.]: Samplename: Sample#6 Root: 25.942 --> Root in between the borders! Added to results. Hyperbolic solved: 42.7945074918604 [20250519_003758.]: Samplename: Sample#7 Root: 42.795 --> Root in between the borders! Added to results. Hyperbolic solved: 88.8741214630346 [20250519_003758.]: Samplename: Sample#8 Root: 88.874 --> Root in between the borders! Added to results. Hyperbolic solved: 2.34647529966966 [20250519_003758.]: Samplename: Sample#9 Root: 2.346 --> Root in between the borders! Added to results. [20250519_003758.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 79.2780593622711 [20250519_003758.]: Samplename: Sample#1 Root: 79.278 --> Root in between the borders! Added to results. Hyperbolic solved: 30.2012458984074 [20250519_003758.]: Samplename: Sample#10 Root: 30.201 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8474393624107 [20250519_003758.]: Samplename: Sample#2 Root: 41.847 --> Root in between the borders! Added to results. Hyperbolic solved: 56.8423517321508 [20250519_003758.]: Samplename: Sample#3 Root: 56.842 --> Root in between the borders! Added to results. Hyperbolic solved: 8.87856046118588 [20250519_003758.]: Samplename: Sample#4 Root: 8.879 --> Root in between the borders! Added to results. Hyperbolic solved: 18.69015950004 [20250519_003758.]: Samplename: Sample#5 Root: 18.69 --> Root in between the borders! Added to results. Hyperbolic solved: 29.9309263534749 [20250519_003758.]: Samplename: Sample#6 Root: 29.931 --> Root in between the borders! Added to results. Hyperbolic solved: 42.8148560027697 [20250519_003758.]: Samplename: Sample#7 Root: 42.815 --> Root in between the borders! Added to results. Hyperbolic solved: 86.7501831416152 [20250519_003758.]: Samplename: Sample#8 Root: 86.75 --> Root in between the borders! Added to results. Hyperbolic solved: 1.51516194985267 [20250519_003758.]: Samplename: Sample#9 Root: 1.515 --> Root in between the borders! Added to results. [20250519_003758.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 78.2565592569279 [20250519_003758.]: Samplename: Sample#1 Root: 78.257 --> Root in between the borders! Added to results. Hyperbolic solved: 25.488739349283 [20250519_003758.]: Samplename: Sample#10 Root: 25.489 --> Root in between the borders! Added to results. Hyperbolic solved: 47.3712258915285 [20250519_003758.]: Samplename: Sample#2 Root: 47.371 --> Root in between the borders! Added to results. Hyperbolic solved: 58.3142673189298 [20250519_003758.]: Samplename: Sample#3 Root: 58.314 --> Root in between the borders! Added to results. Hyperbolic solved: 11.7212231360573 [20250519_003758.]: Samplename: Sample#4 Root: 11.721 --> Root in between the borders! Added to results. Hyperbolic solved: 25.3797485992238 [20250519_003758.]: Samplename: Sample#5 Root: 25.38 --> Root in between the borders! Added to results. Hyperbolic solved: 29.4095133062523 [20250519_003758.]: Samplename: Sample#6 Root: 29.41 --> Root in between the borders! Added to results. Hyperbolic solved: 44.5755071469546 [20250519_003758.]: Samplename: Sample#7 Root: 44.576 --> Root in between the borders! Added to results. Hyperbolic solved: 85.9628731021447 [20250519_003758.]: Samplename: Sample#8 Root: 85.963 --> Root in between the borders! Added to results. Hyperbolic solved: -4.1645647175353 [20250519_003758.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -4.165 --> '-10 < root < 0' --> substitute 0 [20250519_003758.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 72.9696605050898 [20250519_003758.]: Samplename: Sample#1 Root: 72.97 --> Root in between the borders! Added to results. Hyperbolic solved: 27.8250956361743 [20250519_003758.]: Samplename: Sample#10 Root: 27.825 --> Root in between the borders! Added to results. Hyperbolic solved: 34.9068388175484 [20250519_003758.]: Samplename: Sample#2 Root: 34.907 --> Root in between the borders! Added to results. Hyperbolic solved: 59.163743824213 [20250519_003758.]: Samplename: Sample#3 Root: 59.164 --> Root in between the borders! Added to results. Hyperbolic solved: 9.64644413255063 [20250519_003758.]: Samplename: Sample#4 Root: 9.646 --> Root in between the borders! Added to results. Hyperbolic solved: 19.3522607837916 [20250519_003758.]: Samplename: Sample#5 Root: 19.352 --> Root in between the borders! Added to results. Hyperbolic solved: 38.5571436812761 [20250519_003758.]: Samplename: Sample#6 Root: 38.557 --> Root in between the borders! Added to results. Hyperbolic solved: 44.7759625743335 [20250519_003758.]: Samplename: Sample#7 Root: 44.776 --> Root in between the borders! Added to results. Hyperbolic solved: 84.8599671624974 [20250519_003758.]: Samplename: Sample#8 Root: 84.86 --> Root in between the borders! Added to results. Hyperbolic solved: -2.05044894070214 [20250519_003758.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.05 --> '-10 < root < 0' --> substitute 0 [20250519_003758.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 81.041667802328 [20250519_003758.]: Samplename: Sample#1 Root: 81.042 --> Root in between the borders! Added to results. Hyperbolic solved: 26.6441666289306 [20250519_003758.]: Samplename: Sample#10 Root: 26.644 --> Root in between the borders! Added to results. Hyperbolic solved: 45.7046199658262 [20250519_003758.]: Samplename: Sample#2 Root: 45.705 --> Root in between the borders! Added to results. Hyperbolic solved: 56.9767986854544 [20250519_003758.]: Samplename: Sample#3 Root: 56.977 --> Root in between the borders! Added to results. Hyperbolic solved: 6.81231222322667 [20250519_003758.]: Samplename: Sample#4 Root: 6.812 --> Root in between the borders! Added to results. Hyperbolic solved: 22.9284780361346 [20250519_003758.]: Samplename: Sample#5 Root: 22.928 --> Root in between the borders! Added to results. Hyperbolic solved: 38.8653203361426 [20250519_003758.]: Samplename: Sample#6 Root: 38.865 --> Root in between the borders! Added to results. Hyperbolic solved: 43.6855631975613 [20250519_003758.]: Samplename: Sample#7 Root: 43.686 --> Root in between the borders! Added to results. Hyperbolic solved: 88.0155364495973 [20250519_003758.]: Samplename: Sample#8 Root: 88.016 --> Root in between the borders! Added to results. Hyperbolic solved: -3.35610072407525 [20250519_003758.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -3.356 --> '-10 < root < 0' --> substitute 0 [20250519_003758.]: Solving hyperbolic regression for row_means Hyperbolic solved: 77.0692799954538 [20250519_003758.]: Samplename: Sample#1 Root: 77.069 --> Root in between the borders! Added to results. Hyperbolic solved: 28.3620043244584 [20250519_003758.]: Samplename: Sample#10 Root: 28.362 --> Root in between the borders! Added to results. Hyperbolic solved: 42.5026175468089 [20250519_003758.]: Samplename: Sample#2 Root: 42.503 --> Root in between the borders! Added to results. Hyperbolic solved: 57.2972050444248 [20250519_003758.]: Samplename: Sample#3 Root: 57.297 --> Root in between the borders! Added to results. Hyperbolic solved: 8.82704012618393 [20250519_003758.]: Samplename: Sample#4 Root: 8.827 --> Root in between the borders! Added to results. Hyperbolic solved: 21.8102592524881 [20250519_003758.]: Samplename: Sample#5 Root: 21.81 --> Root in between the borders! Added to results. Hyperbolic solved: 28.7228660045975 [20250519_003758.]: Samplename: Sample#6 Root: 28.723 --> Root in between the borders! Added to results. Hyperbolic solved: 43.410510290696 [20250519_003758.]: Samplename: Sample#7 Root: 43.411 --> Root in between the borders! Added to results. Hyperbolic solved: 86.4143551963301 [20250519_003758.]: Samplename: Sample#8 Root: 86.414 --> Root in between the borders! Added to results. Hyperbolic solved: -0.237020571201611 [20250519_003758.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.237 --> '-10 < root < 0' --> substitute 0 [20250519_003758.]: on_start: using future::plan("sequential") [20250519_003758.]: Entered 'clean_dt'-Function [20250519_003758.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_003758.]: got experimental data [20250519_003758.]: Entered 'clean_dt'-Function [20250519_003758.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_003758.]: got calibration data [20250519_003759.]: ### Starting with regression calculations ### [20250519_003759.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_003759.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_003759.]: Logging df_agg: CpG#1 [20250519_003759.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003759.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_003759.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_003759.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_003759.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_003759.]: Entered 'hyperbolic_regression'-Function [20250519_003759.]: 'hyperbolic_regression': minmax = FALSE [20250519_003759.]: Entered 'cubic_regression'-Function [20250519_003759.]: 'cubic_regression': minmax = FALSE [20250519_003759.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_003759.]: Logging df_agg: CpG#2 [20250519_003759.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003759.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_003759.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_003759.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_003759.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_003759.]: Entered 'hyperbolic_regression'-Function [20250519_003759.]: 'hyperbolic_regression': minmax = FALSE [20250519_003759.]: Entered 'cubic_regression'-Function [20250519_003759.]: 'cubic_regression': minmax = FALSE [20250519_003759.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_003759.]: Logging df_agg: CpG#3 [20250519_003759.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003759.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_003759.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_003759.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_003759.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_003759.]: Entered 'hyperbolic_regression'-Function [20250519_003759.]: 'hyperbolic_regression': minmax = FALSE [20250519_003759.]: Entered 'cubic_regression'-Function [20250519_003759.]: 'cubic_regression': minmax = FALSE [20250519_003759.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_003759.]: Logging df_agg: CpG#4 [20250519_003759.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003759.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_003759.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_003759.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_003759.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_003759.]: Entered 'hyperbolic_regression'-Function [20250519_003759.]: 'hyperbolic_regression': minmax = FALSE [20250519_003759.]: Entered 'cubic_regression'-Function [20250519_003759.]: 'cubic_regression': minmax = FALSE [20250519_003800.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_003800.]: Logging df_agg: CpG#5 [20250519_003800.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003800.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_003800.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_003800.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_003800.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_003800.]: Entered 'hyperbolic_regression'-Function [20250519_003800.]: 'hyperbolic_regression': minmax = FALSE [20250519_003800.]: Entered 'cubic_regression'-Function [20250519_003800.]: 'cubic_regression': minmax = FALSE [20250519_003800.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_003800.]: Logging df_agg: CpG#6 [20250519_003800.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003800.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_003800.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_003800.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_003800.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_003800.]: Entered 'hyperbolic_regression'-Function [20250519_003800.]: 'hyperbolic_regression': minmax = FALSE [20250519_003800.]: Entered 'cubic_regression'-Function [20250519_003800.]: 'cubic_regression': minmax = FALSE [20250519_003800.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_003800.]: Logging df_agg: CpG#7 [20250519_003800.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003800.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_003800.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_003800.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_003800.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_003800.]: Entered 'hyperbolic_regression'-Function [20250519_003800.]: 'hyperbolic_regression': minmax = FALSE [20250519_003800.]: Entered 'cubic_regression'-Function [20250519_003800.]: 'cubic_regression': minmax = FALSE [20250519_003800.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_003800.]: Logging df_agg: CpG#8 [20250519_003800.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003800.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_003800.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_003800.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_003800.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_003800.]: Entered 'hyperbolic_regression'-Function [20250519_003800.]: 'hyperbolic_regression': minmax = FALSE [20250519_003800.]: Entered 'cubic_regression'-Function [20250519_003800.]: 'cubic_regression': minmax = FALSE [20250519_003800.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_003800.]: Logging df_agg: CpG#9 [20250519_003800.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003800.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_003800.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_003800.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_003800.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_003800.]: Entered 'hyperbolic_regression'-Function [20250519_003800.]: 'hyperbolic_regression': minmax = FALSE [20250519_003801.]: Entered 'cubic_regression'-Function [20250519_003801.]: 'cubic_regression': minmax = FALSE [20250519_003801.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723122, 2.47760998435345)c(5.94911111111111, 0.84962962962963, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703704, 19.6995555555555, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_003801.]: Logging df_agg: row_means [20250519_003801.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003801.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_003801.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723122, 2.47760998435345)[20250519_003801.]: c(5.94911111111111, 0.84962962962963, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_003801.]: c(NA, 6.79703703703704, 19.6995555555555, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_003801.]: Entered 'hyperbolic_regression'-Function [20250519_003801.]: 'hyperbolic_regression': minmax = FALSE [20250519_003801.]: Entered 'cubic_regression'-Function [20250519_003801.]: 'cubic_regression': minmax = FALSE [20250519_003801.]: ### Starting with plotting ### [20250519_003802.]: Creating plot No. 1 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG1.png [20250519_003802.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -937.71833725671 , a = -108.568188547925 , b = -937.71833725671 , d = -232.05735859911 [20250519_003802.]: # CpG-site: CpG#1 Cubic: Using a = 6.53413423120091e-05 , b = -0.0055806968734969 , c = 0.784061853455188 , d = 1.93182659932656 [20250519_003803.]: Creating plot No. 2 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG2.png [20250519_003803.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = -2393.14264996499 , a = -141.862267639951 , b = -2393.14264996499 , d = -268.331587255735 [20250519_003803.]: # CpG-site: CpG#2 Cubic: Using a = 8.04237934904601e-06 , b = 0.00293158941798942 , c = 0.514821742825076 , d = 9.27667003367003 [20250519_003805.]: Creating plot No. 3 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG3.png [20250519_003805.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = -1477.46867711742 , a = -170.424279984248 , b = -1477.46867711742 , d = -327.189866698889 [20250519_003805.]: # CpG-site: CpG#3 Cubic: Using a = 2.30555869809204e-05 , b = -0.000797009331409346 , c = 0.62783129228796 , d = 3.88705218855218 [20250519_003806.]: Creating plot No. 4 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG4.png [20250519_003806.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = -2115.87814484424 , a = -162.169929539519 , b = -2115.87814484424 , d = -299.662454768746 [20250519_003806.]: # CpG-site: CpG#4 Cubic: Using a = 3.2166356902357e-05 , b = -0.0015913142857143 , c = 0.697398364598366 , d = 6.17055050505048 [20250519_003808.]: Creating plot No. 5 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG5.png [20250519_003808.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = -1383.32734884945 , a = -151.440088502132 , b = -1383.32734884945 , d = -304.200280997675 [20250519_003808.]: # CpG-site: CpG#5 Cubic: Using a = -4.48459708193036e-06 , b = 0.00375371236171235 , c = 0.422446384479718 , d = 5.17203872053872 [20250519_003809.]: Creating plot No. 6 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG6.png [20250519_003809.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = -1550.59304894212 , a = -125.016150436792 , b = -1550.59304894212 , d = -247.279683783527 [20250519_003809.]: # CpG-site: CpG#6 Cubic: Using a = 2.38852884399552e-05 , b = 0.000918637037037021 , c = 0.561022132435467 , d = 6.1479276094276 [20250519_003811.]: Creating plot No. 7 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG7.png [20250519_003811.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -772.843265948977 , a = -75.2726697123378 , b = -772.843265948977 , d = -226.67343046903 [20250519_003811.]: # CpG-site: CpG#7 Cubic: Using a = 4.98010505050505e-05 , b = -0.00436152150072151 , c = 0.579588917748918 , d = 1.72104545454544 [20250519_003812.]: Creating plot No. 8 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG8.png [20250519_003812.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = -3872.73534838154 , a = -278.017155483789 , b = -3872.73534838154 , d = -438.894126934244 [20250519_003812.]: # CpG-site: CpG#8 Cubic: Using a = -1.50060965207632e-05 , b = 0.00447978143338143 , c = 0.534897737694404 , d = 9.56981481481482 [20250519_003814.]: Creating plot No. 9 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG9.png [20250519_003814.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -843.925271611492 , a = -70.6796326247736 , b = -843.925271611492 , d = -184.482178799669 [20250519_003814.]: # CpG-site: CpG#9 Cubic: Using a = 7.72300426487093e-05 , b = -0.00542281173641174 , c = 0.67899229597563 , d = 2.72413468013467 [20250519_003815.]: Creating plot No. 10 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_rowmeans.png [20250519_003815.]: # CpG-site: row_means Hyperbolic: Using bias_weight = -1525.75751056682 , a = -127.675093193972 , b = -1525.75751056682 , d = -263.003121358972 [20250519_003815.]: # CpG-site: row_means Cubic: Using a = 2.88923726150392e-05 , b = -0.0006299592752926 , c = 0.600117857944524 , d = 5.17789562289563 [20250519_003817.]: Entered 'solving_equations'-Function [20250519_003817.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: -2.23220911684257 [20250519_003817.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.232 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.1698564372971 [20250519_003817.]: Samplename: 12.5 Root: 12.17 --> Root in between the borders! Added to results. Hyperbolic solved: 24.478190918656 [20250519_003817.]: Samplename: 25 Root: 24.478 --> Root in between the borders! Added to results. Hyperbolic solved: 38.1730371634976 [20250519_003817.]: Samplename: 37.5 Root: 38.173 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3349263290239 [20250519_003817.]: Samplename: 50 Root: 52.335 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4582665283612 [20250519_003817.]: Samplename: 62.5 Root: 65.458 --> Root in between the borders! Added to results. Hyperbolic solved: 75.0090705794847 [20250519_003817.]: Samplename: 75 Root: 75.009 --> Root in between the borders! Added to results. Hyperbolic solved: 81.5271853400487 [20250519_003817.]: Samplename: 87.5 Root: 81.527 --> Root in between the borders! Added to results. Hyperbolic solved: 102.400898271831 [20250519_003817.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.401 --> '100 < root < 110' --> substitute 100 [20250519_003817.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 1.13660511681856 [20250519_003817.]: Samplename: 0 Root: 1.137 --> Root in between the borders! Added to results. Hyperbolic solved: 11.4129696918445 [20250519_003817.]: Samplename: 12.5 Root: 11.413 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1740004612661 [20250519_003817.]: Samplename: 25 Root: 26.174 --> Root in between the borders! Added to results. Hyperbolic solved: 35.1050448132614 [20250519_003817.]: Samplename: 37.5 Root: 35.105 --> Root in between the borders! Added to results. Hyperbolic solved: 47.6855002074492 [20250519_003817.]: Samplename: 50 Root: 47.686 --> Root in between the borders! Added to results. Hyperbolic solved: 67.1440493313574 [20250519_003817.]: Samplename: 62.5 Root: 67.144 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7644668045486 [20250519_003817.]: Samplename: 75 Root: 75.764 --> Root in between the borders! Added to results. Hyperbolic solved: 84.4054158145455 [20250519_003817.]: Samplename: 87.5 Root: 84.405 --> Root in between the borders! Added to results. Hyperbolic solved: 100.948272543309 [20250519_003817.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.948 --> '100 < root < 110' --> substitute 100 [20250519_003817.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 0.512348561877098 [20250519_003817.]: Samplename: 0 Root: 0.512 --> Root in between the borders! Added to results. Hyperbolic solved: 10.7523845008661 [20250519_003817.]: Samplename: 12.5 Root: 10.752 --> Root in between the borders! Added to results. Hyperbolic solved: 25.5218912317424 [20250519_003817.]: Samplename: 25 Root: 25.522 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5270488279988 [20250519_003817.]: Samplename: 37.5 Root: 36.527 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7909283805111 [20250519_003817.]: Samplename: 50 Root: 50.791 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8686353441458 [20250519_003817.]: Samplename: 62.5 Root: 64.869 --> Root in between the borders! Added to results. Hyperbolic solved: 77.5524208271734 [20250519_003817.]: Samplename: 75 Root: 77.552 --> Root in between the borders! Added to results. Hyperbolic solved: 80.4374631680835 [20250519_003817.]: Samplename: 87.5 Root: 80.437 --> Root in between the borders! Added to results. Hyperbolic solved: 102.704019894026 [20250519_003817.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.704 --> '100 < root < 110' --> substitute 100 [20250519_003817.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: -0.519496428117613 [20250519_003817.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.519 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.4934173132007 [20250519_003817.]: Samplename: 12.5 Root: 12.493 --> Root in between the borders! Added to results. Hyperbolic solved: 24.2685417185317 [20250519_003817.]: Samplename: 25 Root: 24.269 --> Root in between the borders! Added to results. Hyperbolic solved: 38.0817103888785 [20250519_003817.]: Samplename: 37.5 Root: 38.082 --> Root in between the borders! Added to results. Hyperbolic solved: 48.5843148203973 [20250519_003817.]: Samplename: 50 Root: 48.584 --> Root in between the borders! Added to results. Hyperbolic solved: 67.672236894341 [20250519_003817.]: Samplename: 62.5 Root: 67.672 --> Root in between the borders! Added to results. Hyperbolic solved: 74.1549253766253 [20250519_003817.]: Samplename: 75 Root: 74.155 --> Root in between the borders! Added to results. Hyperbolic solved: 82.8821786440368 [20250519_003817.]: Samplename: 87.5 Root: 82.882 --> Root in between the borders! Added to results. Hyperbolic solved: 102.079130232402 [20250519_003817.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.079 --> '100 < root < 110' --> substitute 100 [20250519_003817.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 2.41558718123367 [20250519_003817.]: Samplename: 0 Root: 2.416 --> Root in between the borders! Added to results. Hyperbolic solved: 10.1649680545674 [20250519_003817.]: Samplename: 12.5 Root: 10.165 --> Root in between the borders! Added to results. Hyperbolic solved: 23.9830821073418 [20250519_003817.]: Samplename: 25 Root: 23.983 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2773617400679 [20250519_003817.]: Samplename: 37.5 Root: 37.277 --> Root in between the borders! Added to results. Hyperbolic solved: 50.8659382464167 [20250519_003817.]: Samplename: 50 Root: 50.866 --> Root in between the borders! Added to results. Hyperbolic solved: 62.4342269461158 [20250519_003817.]: Samplename: 62.5 Root: 62.434 --> Root in between the borders! Added to results. Hyperbolic solved: 76.3915257997575 [20250519_003817.]: Samplename: 75 Root: 76.392 --> Root in between the borders! Added to results. Hyperbolic solved: 86.1597887396996 [20250519_003817.]: Samplename: 87.5 Root: 86.16 --> Root in between the borders! Added to results. Hyperbolic solved: 100.267760316884 [20250519_003817.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.268 --> '100 < root < 110' --> substitute 100 [20250519_003817.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 0.138164161050506 [20250519_003817.]: Samplename: 0 Root: 0.138 --> Root in between the borders! Added to results. Hyperbolic solved: 11.8635561171135 [20250519_003817.]: Samplename: 12.5 Root: 11.864 --> Root in between the borders! Added to results. Hyperbolic solved: 26.510745006666 [20250519_003817.]: Samplename: 25 Root: 26.511 --> Root in between the borders! Added to results. Hyperbolic solved: 35.3205072777727 [20250519_003817.]: Samplename: 37.5 Root: 35.321 --> Root in between the borders! Added to results. Hyperbolic solved: 50.0570766444694 [20250519_003817.]: Samplename: 50 Root: 50.057 --> Root in between the borders! Added to results. Hyperbolic solved: 64.9602942986125 [20250519_003817.]: Samplename: 62.5 Root: 64.96 --> Root in between the borders! Added to results. Hyperbolic solved: 73.6689055886064 [20250519_003817.]: Samplename: 75 Root: 73.669 --> Root in between the borders! Added to results. Hyperbolic solved: 87.1266085894673 [20250519_003817.]: Samplename: 87.5 Root: 87.127 --> Root in between the borders! Added to results. Hyperbolic solved: 100.261637049252 [20250519_003817.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.262 --> '100 < root < 110' --> substitute 100 [20250519_003817.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: -1.37235519395329 [20250519_003817.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -1.372 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.1993294013604 [20250519_003817.]: Samplename: 12.5 Root: 10.199 --> Root in between the borders! Added to results. Hyperbolic solved: 24.5951803850766 [20250519_003817.]: Samplename: 25 Root: 24.595 --> Root in between the borders! Added to results. Hyperbolic solved: 37.8310364564335 [20250519_003817.]: Samplename: 37.5 Root: 37.831 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5588645818264 [20250519_003817.]: Samplename: 50 Root: 53.559 --> Root in between the borders! Added to results. Hyperbolic solved: 65.9364860153449 [20250519_003817.]: Samplename: 62.5 Root: 65.936 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7361033620359 [20250519_003817.]: Samplename: 75 Root: 75.736 --> Root in between the borders! Added to results. Hyperbolic solved: 79.4328192070151 [20250519_003817.]: Samplename: 87.5 Root: 79.433 --> Root in between the borders! Added to results. Hyperbolic solved: 103.004248773309 [20250519_003817.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 103.004 --> '100 < root < 110' --> substitute 100 [20250519_003817.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 2.80068093389159 [20250519_003817.]: Samplename: 0 Root: 2.801 --> Root in between the borders! Added to results. Hyperbolic solved: 9.27535066703723 [20250519_003817.]: Samplename: 12.5 Root: 9.275 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4762625870809 [20250519_003817.]: Samplename: 25 Root: 25.476 --> Root in between the borders! Added to results. Hyperbolic solved: 34.0122083314201 [20250519_003817.]: Samplename: 37.5 Root: 34.012 --> Root in between the borders! Added to results. Hyperbolic solved: 51.7842666342759 [20250519_003817.]: Samplename: 50 Root: 51.784 --> Root in between the borders! Added to results. Hyperbolic solved: 64.6732321058682 [20250519_003817.]: Samplename: 62.5 Root: 64.673 --> Root in between the borders! Added to results. Hyperbolic solved: 78.4326982875914 [20250519_003817.]: Samplename: 75 Root: 78.433 --> Root in between the borders! Added to results. Hyperbolic solved: 81.3427235319597 [20250519_003817.]: Samplename: 87.5 Root: 81.343 --> Root in between the borders! Added to results. Hyperbolic solved: 101.964405325155 [20250519_003817.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.964 --> '100 < root < 110' --> substitute 100 [20250519_003817.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: -2.1340328997008 [20250519_003817.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.134 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.5082212970134 [20250519_003817.]: Samplename: 12.5 Root: 10.508 --> Root in between the borders! Added to results. Hyperbolic solved: 26.9164566258777 [20250519_003817.]: Samplename: 25 Root: 26.916 --> Root in between the borders! Added to results. Hyperbolic solved: 36.8334769505904 [20250519_003817.]: Samplename: 37.5 Root: 36.833 --> Root in between the borders! Added to results. Hyperbolic solved: 52.0097879402751 [20250519_003817.]: Samplename: 50 Root: 52.01 --> Root in between the borders! Added to results. Hyperbolic solved: 64.893051148082 [20250519_003817.]: Samplename: 62.5 Root: 64.893 --> Root in between the borders! Added to results. Hyperbolic solved: 74.5671042786367 [20250519_003817.]: Samplename: 75 Root: 74.567 --> Root in between the borders! Added to results. Hyperbolic solved: 84.5294949210319 [20250519_003817.]: Samplename: 87.5 Root: 84.529 --> Root in between the borders! Added to results. Hyperbolic solved: 101.047147807246 [20250519_003817.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.047 --> '100 < root < 110' --> substitute 100 [20250519_003817.]: Solving hyperbolic regression for row_means Hyperbolic solved: 0.290944901553815 [20250519_003817.]: Samplename: 0 Root: 0.291 --> Root in between the borders! Added to results. Hyperbolic solved: 11.0412427310408 [20250519_003817.]: Samplename: 12.5 Root: 11.041 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4081501338029 [20250519_003817.]: Samplename: 25 Root: 25.408 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5243709582085 [20250519_003817.]: Samplename: 37.5 Root: 36.524 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7348808712519 [20250519_003817.]: Samplename: 50 Root: 50.735 --> Root in between the borders! Added to results. Hyperbolic solved: 65.3135195123017 [20250519_003817.]: Samplename: 62.5 Root: 65.314 --> Root in between the borders! Added to results. Hyperbolic solved: 75.5342699494589 [20250519_003817.]: Samplename: 75 Root: 75.534 --> Root in between the borders! Added to results. Hyperbolic solved: 83.2411225127701 [20250519_003817.]: Samplename: 87.5 Root: 83.241 --> Root in between the borders! Added to results. Hyperbolic solved: 101.666944784378 [20250519_003817.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.667 --> '100 < root < 110' --> substitute 100 [20250519_003817.]: ### Starting with regression calculations ### [20250519_003817.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_003817.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.1698564372971, 24.478190918656, 38.1730371634976, 52.3349263290239, 65.4582665283612, 75.0090705794847, 81.5271853400487, 100)c(0, 0.330143562702901, 0.521809081343999, 0.673037163497597, 2.3349263290239, 2.95826652836119, 0.00907057948469969, 5.9728146599513, 0)c(NA, 2.64114850162321, 2.087236325376, 1.79476576932692, 4.6698526580478, 4.73322644537791, 0.0120941059795996, 6.8260738970872, 0) [20250519_003817.]: Logging df_agg: CpG#1 [20250519_003817.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003817.]: c(0, 12.1698564372971, 24.478190918656, 38.1730371634976, 52.3349263290239, 65.4582665283612, 75.0090705794847, 81.5271853400487, 100)[20250519_003817.]: c(0, 0.330143562702901, 0.521809081343999, 0.673037163497597, 2.3349263290239, 2.95826652836119, 0.00907057948469969, 5.9728146599513, 0)[20250519_003817.]: c(NA, 2.64114850162321, 2.087236325376, 1.79476576932692, 4.6698526580478, 4.73322644537791, 0.0120941059795996, 6.8260738970872, 0) [20250519_003817.]: Entered 'hyperbolic_regression'-Function [20250519_003817.]: 'hyperbolic_regression': minmax = FALSE [20250519_003818.]: Entered 'cubic_regression'-Function [20250519_003818.]: 'cubic_regression': minmax = FALSE [20250519_003818.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.13660511681856, 11.4129696918445, 26.1740004612661, 35.1050448132614, 47.6855002074492, 67.1440493313574, 75.7644668045486, 84.4054158145455, 100)c(1.13660511681856, 1.0870303081555, 1.1740004612661, 2.3949551867386, 2.3144997925508, 4.64404933135739, 0.764466804548604, 3.09458418545449, 0)c(NA, 8.696242465244, 4.6960018450644, 6.38654716463626, 4.6289995851016, 7.43047893017183, 1.01928907273147, 3.53666764051942, 0) [20250519_003818.]: Logging df_agg: CpG#2 [20250519_003818.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003818.]: c(1.13660511681856, 11.4129696918445, 26.1740004612661, 35.1050448132614, 47.6855002074492, 67.1440493313574, 75.7644668045486, 84.4054158145455, 100)[20250519_003818.]: c(1.13660511681856, 1.0870303081555, 1.1740004612661, 2.3949551867386, 2.3144997925508, 4.64404933135739, 0.764466804548604, 3.09458418545449, 0)[20250519_003818.]: c(NA, 8.696242465244, 4.6960018450644, 6.38654716463626, 4.6289995851016, 7.43047893017183, 1.01928907273147, 3.53666764051942, 0) [20250519_003818.]: Entered 'hyperbolic_regression'-Function [20250519_003818.]: 'hyperbolic_regression': minmax = FALSE [20250519_003818.]: Entered 'cubic_regression'-Function [20250519_003818.]: 'cubic_regression': minmax = FALSE [20250519_003818.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.512348561877098, 10.7523845008661, 25.5218912317424, 36.5270488279988, 50.7909283805111, 64.8686353441458, 77.5524208271734, 80.4374631680835, 100)c(0.512348561877098, 1.7476154991339, 0.521891231742401, 0.972951172001203, 0.790928380511097, 2.3686353441458, 2.55242082717341, 7.0625368319165, 0)c(NA, 13.9809239930712, 2.08756492696961, 2.59453645866987, 1.58185676102219, 3.78981655063328, 3.40322776956454, 8.07147066504743, 0) [20250519_003818.]: Logging df_agg: CpG#3 [20250519_003818.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003818.]: c(0.512348561877098, 10.7523845008661, 25.5218912317424, 36.5270488279988, 50.7909283805111, 64.8686353441458, 77.5524208271734, 80.4374631680835, 100)[20250519_003818.]: c(0.512348561877098, 1.7476154991339, 0.521891231742401, 0.972951172001203, 0.790928380511097, 2.3686353441458, 2.55242082717341, 7.0625368319165, 0)[20250519_003818.]: c(NA, 13.9809239930712, 2.08756492696961, 2.59453645866987, 1.58185676102219, 3.78981655063328, 3.40322776956454, 8.07147066504743, 0) [20250519_003818.]: Entered 'hyperbolic_regression'-Function [20250519_003818.]: 'hyperbolic_regression': minmax = FALSE [20250519_003818.]: Entered 'cubic_regression'-Function [20250519_003818.]: 'cubic_regression': minmax = FALSE [20250519_003818.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.4934173132007, 24.2685417185317, 38.0817103888785, 48.5843148203973, 67.672236894341, 74.1549253766253, 82.8821786440368, 100)c(0, 0.00658268679929996, 0.731458281468299, 0.581710388878498, 1.4156851796027, 5.172236894341, 0.8450746233747, 4.6178213559632, 0)c(NA, 0.0526614943943997, 2.9258331258732, 1.551227703676, 2.83137035920539, 8.27557903094559, 1.1267661644996, 5.2775101211008, 0) [20250519_003818.]: Logging df_agg: CpG#4 [20250519_003818.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003818.]: c(0, 12.4934173132007, 24.2685417185317, 38.0817103888785, 48.5843148203973, 67.672236894341, 74.1549253766253, 82.8821786440368, 100)[20250519_003818.]: c(0, 0.00658268679929996, 0.731458281468299, 0.581710388878498, 1.4156851796027, 5.172236894341, 0.8450746233747, 4.6178213559632, 0)[20250519_003818.]: c(NA, 0.0526614943943997, 2.9258331258732, 1.551227703676, 2.83137035920539, 8.27557903094559, 1.1267661644996, 5.2775101211008, 0) [20250519_003818.]: Entered 'hyperbolic_regression'-Function [20250519_003818.]: 'hyperbolic_regression': minmax = FALSE [20250519_003818.]: Entered 'cubic_regression'-Function [20250519_003818.]: 'cubic_regression': minmax = FALSE [20250519_003818.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.41558718123367, 10.1649680545674, 23.9830821073418, 37.2773617400679, 50.8659382464167, 62.4342269461158, 76.3915257997575, 86.1597887396996, 100)c(2.41558718123367, 2.3350319454326, 1.0169178926582, 0.222638259932097, 0.865938246416697, 0.0657730538842003, 1.3915257997575, 1.34021126030041, 0)c(NA, 18.6802555634608, 4.0676715706328, 0.593702026485592, 1.73187649283339, 0.10523688621472, 1.85536773301, 1.53167001177189, 0) [20250519_003818.]: Logging df_agg: CpG#5 [20250519_003818.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003818.]: c(2.41558718123367, 10.1649680545674, 23.9830821073418, 37.2773617400679, 50.8659382464167, 62.4342269461158, 76.3915257997575, 86.1597887396996, 100)[20250519_003818.]: c(2.41558718123367, 2.3350319454326, 1.0169178926582, 0.222638259932097, 0.865938246416697, 0.0657730538842003, 1.3915257997575, 1.34021126030041, 0)[20250519_003818.]: c(NA, 18.6802555634608, 4.0676715706328, 0.593702026485592, 1.73187649283339, 0.10523688621472, 1.85536773301, 1.53167001177189, 0) [20250519_003818.]: Entered 'hyperbolic_regression'-Function [20250519_003818.]: 'hyperbolic_regression': minmax = FALSE [20250519_003819.]: Entered 'cubic_regression'-Function [20250519_003819.]: 'cubic_regression': minmax = FALSE [20250519_003819.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.138164161050506, 11.8635561171135, 26.510745006666, 35.3205072777727, 50.0570766444694, 64.9602942986125, 73.6689055886064, 87.1266085894673, 100)c(0.138164161050506, 0.6364438828865, 1.510745006666, 2.1794927222273, 0.0570766444693973, 2.4602942986125, 1.33109441139359, 0.373391410532705, 0)c(NA, 5.091551063092, 6.042980026664, 5.81198059260614, 0.114153288938795, 3.93647087777999, 1.77479254852479, 0.426733040608805, 0) [20250519_003819.]: Logging df_agg: CpG#6 [20250519_003819.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003819.]: c(0.138164161050506, 11.8635561171135, 26.510745006666, 35.3205072777727, 50.0570766444694, 64.9602942986125, 73.6689055886064, 87.1266085894673, 100)[20250519_003819.]: c(0.138164161050506, 0.6364438828865, 1.510745006666, 2.1794927222273, 0.0570766444693973, 2.4602942986125, 1.33109441139359, 0.373391410532705, 0)[20250519_003819.]: c(NA, 5.091551063092, 6.042980026664, 5.81198059260614, 0.114153288938795, 3.93647087777999, 1.77479254852479, 0.426733040608805, 0) [20250519_003819.]: Entered 'hyperbolic_regression'-Function [20250519_003819.]: 'hyperbolic_regression': minmax = FALSE [20250519_003819.]: Entered 'cubic_regression'-Function [20250519_003819.]: 'cubic_regression': minmax = FALSE [20250519_003819.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.1993294013604, 24.5951803850766, 37.8310364564335, 53.5588645818264, 65.9364860153449, 75.7361033620359, 79.4328192070151, 100)c(0, 2.3006705986396, 0.404819614923401, 0.331036456433502, 3.5588645818264, 3.4364860153449, 0.736103362035905, 8.0671807929849, 0)c(NA, 18.4053647891168, 1.6192784596936, 0.882763883822671, 7.1177291636528, 5.49837762455184, 0.981471149381207, 9.21963519198274, 0) [20250519_003819.]: Logging df_agg: CpG#7 [20250519_003819.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003819.]: c(0, 10.1993294013604, 24.5951803850766, 37.8310364564335, 53.5588645818264, 65.9364860153449, 75.7361033620359, 79.4328192070151, 100)[20250519_003819.]: c(0, 2.3006705986396, 0.404819614923401, 0.331036456433502, 3.5588645818264, 3.4364860153449, 0.736103362035905, 8.0671807929849, 0)[20250519_003819.]: c(NA, 18.4053647891168, 1.6192784596936, 0.882763883822671, 7.1177291636528, 5.49837762455184, 0.981471149381207, 9.21963519198274, 0) [20250519_003819.]: Entered 'hyperbolic_regression'-Function [20250519_003819.]: 'hyperbolic_regression': minmax = FALSE [20250519_003819.]: Entered 'cubic_regression'-Function [20250519_003819.]: 'cubic_regression': minmax = FALSE [20250519_003819.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.80068093389159, 9.27535066703723, 25.4762625870809, 34.0122083314201, 51.7842666342759, 64.6732321058682, 78.4326982875914, 81.3427235319597, 100)c(2.80068093389159, 3.22464933296277, 0.4762625870809, 3.4877916685799, 1.7842666342759, 2.17323210586819, 3.43269828759141, 6.15727646804029, 0)c(NA, 25.7971946637022, 1.9050503483236, 9.30077778287974, 3.5685332685518, 3.47717136938911, 4.57693105012188, 7.03688739204605, 0) [20250519_003819.]: Logging df_agg: CpG#8 [20250519_003819.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003819.]: c(2.80068093389159, 9.27535066703723, 25.4762625870809, 34.0122083314201, 51.7842666342759, 64.6732321058682, 78.4326982875914, 81.3427235319597, 100)[20250519_003819.]: c(2.80068093389159, 3.22464933296277, 0.4762625870809, 3.4877916685799, 1.7842666342759, 2.17323210586819, 3.43269828759141, 6.15727646804029, 0)[20250519_003819.]: c(NA, 25.7971946637022, 1.9050503483236, 9.30077778287974, 3.5685332685518, 3.47717136938911, 4.57693105012188, 7.03688739204605, 0) [20250519_003819.]: Entered 'hyperbolic_regression'-Function [20250519_003819.]: 'hyperbolic_regression': minmax = FALSE [20250519_003819.]: Entered 'cubic_regression'-Function [20250519_003819.]: 'cubic_regression': minmax = FALSE [20250519_003819.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.5082212970134, 26.9164566258777, 36.8334769505904, 52.0097879402751, 64.893051148082, 74.5671042786367, 84.5294949210319, 100)c(0, 1.9917787029866, 1.9164566258777, 0.666523049409598, 2.0097879402751, 2.39305114808199, 0.432895721363295, 2.9705050789681, 0)c(NA, 15.9342296238928, 7.6658265035108, 1.77739479842559, 4.01957588055021, 3.82888183693119, 0.57719429515106, 3.39486294739212, 0) [20250519_003819.]: Logging df_agg: CpG#9 [20250519_003819.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003819.]: c(0, 10.5082212970134, 26.9164566258777, 36.8334769505904, 52.0097879402751, 64.893051148082, 74.5671042786367, 84.5294949210319, 100)[20250519_003819.]: c(0, 1.9917787029866, 1.9164566258777, 0.666523049409598, 2.0097879402751, 2.39305114808199, 0.432895721363295, 2.9705050789681, 0)[20250519_003819.]: c(NA, 15.9342296238928, 7.6658265035108, 1.77739479842559, 4.01957588055021, 3.82888183693119, 0.57719429515106, 3.39486294739212, 0) [20250519_003819.]: Entered 'hyperbolic_regression'-Function [20250519_003819.]: 'hyperbolic_regression': minmax = FALSE [20250519_003820.]: Entered 'cubic_regression'-Function [20250519_003820.]: 'cubic_regression': minmax = FALSE [20250519_003820.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.290944901553815, 11.0412427310408, 25.4081501338029, 36.5243709582085, 50.7348808712519, 65.3135195123017, 75.5342699494589, 83.2411225127701, 100)c(0.290944901553815, 1.4587572689592, 0.408150133802899, 0.9756290417915, 0.734880871251903, 2.8135195123017, 0.534269949458903, 4.2588774872299, 0)c(NA, 11.6700581516736, 1.63260053521159, 2.60167744477733, 1.46976174250381, 4.50163121968271, 0.71235993261187, 4.86728855683417, 0) [20250519_003820.]: Logging df_agg: row_means [20250519_003820.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003820.]: c(0.290944901553815, 11.0412427310408, 25.4081501338029, 36.5243709582085, 50.7348808712519, 65.3135195123017, 75.5342699494589, 83.2411225127701, 100)[20250519_003820.]: c(0.290944901553815, 1.4587572689592, 0.408150133802899, 0.9756290417915, 0.734880871251903, 2.8135195123017, 0.534269949458903, 4.2588774872299, 0)[20250519_003820.]: c(NA, 11.6700581516736, 1.63260053521159, 2.60167744477733, 1.46976174250381, 4.50163121968271, 0.71235993261187, 4.86728855683417, 0) [20250519_003820.]: Entered 'hyperbolic_regression'-Function [20250519_003820.]: 'hyperbolic_regression': minmax = FALSE [20250519_003820.]: Entered 'cubic_regression'-Function [20250519_003820.]: 'cubic_regression': minmax = FALSE [20250519_003820.]: ### Starting with plotting ### [20250519_003821.]: Creating BiasCorrected (hyperbolic) plot No. 1 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG1_corrected_h.png [20250519_003821.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -657.85070009309 , a = 985.271206705952 , b = -657.85070009309 , d = 903.457967400565 [20250519_003821.]: # CpG-site: CpG#1 Cubic: Using a = 5.1847317144537e-06 , b = -0.00180593132520811 , c = 1.11404026275692 , d = -0.858597345049636 [20250519_003822.]: Creating BiasCorrected (hyperbolic) plot No. 2 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG2_corrected_h.png [20250519_003822.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = 3399.63077807263 , a = 30276.8974003894 , b = 3399.63077807263 , d = 30353.3579426379 [20250519_003822.]: # CpG-site: CpG#2 Cubic: Using a = -2.58097552031906e-05 , b = 0.00383753288450718 , c = 0.851607751869747 , d = 0.95712734045721 [20250519_003823.]: Creating BiasCorrected (hyperbolic) plot No. 3 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG3_corrected_h.png [20250519_003823.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = -945.763900328712 , a = 1333.61328542459 , b = -945.763900328712 , d = 1252.95190710086 [20250519_003823.]: # CpG-site: CpG#3 Cubic: Using a = -1.13948418252831e-05 , b = 0.000937174314708123 , c = 0.998631105115713 , d = -0.383346041194064 [20250519_003825.]: Creating BiasCorrected (hyperbolic) plot No. 4 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG4_corrected_h.png [20250519_003825.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = -754.050808709341 , a = 1637.26986382636 , b = -754.050808709341 , d = 1558.27566152171 [20250519_003825.]: # CpG-site: CpG#4 Cubic: Using a = -3.25777472656452e-06 , b = -0.000129598485618069 , c = 1.03077245158202 , d = -0.370342672245111 [20250519_003826.]: Creating BiasCorrected (hyperbolic) plot No. 5 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG5_corrected_h.png [20250519_003826.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = -1917.44470782279 , a = -5621.43051220384 , b = -1917.44470782279 , d = -5734.45004174505 [20250519_003826.]: # CpG-site: CpG#5 Cubic: Using a = -3.16782847952728e-05 , b = 0.00493527243554485 , c = 0.800194551229801 , d = 1.38653114075915 [20250519_003828.]: Creating BiasCorrected (hyperbolic) plot No. 6 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG6_corrected_h.png [20250519_003828.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = -1255.66539296107 , a = 13775.5132875201 , b = -1255.66539296107 , d = 13690.4025642689 [20250519_003828.]: # CpG-site: CpG#6 Cubic: Using a = -3.70201699726522e-06 , b = 0.000482210843999232 , c = 0.98529311552838 , d = 0.0293572853154747 [20250519_003829.]: Creating BiasCorrected (hyperbolic) plot No. 7 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG7_corrected_h.png [20250519_003829.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -1065.05219601462 , a = 722.085443879134 , b = -1065.05219601462 , d = 634.403611801521 [20250519_003829.]: # CpG-site: CpG#7 Cubic: Using a = -1.04101209223167e-06 , b = -0.00130151331776721 , c = 1.1239953084199 , d = -1.59858561306358 [20250519_003831.]: Creating BiasCorrected (hyperbolic) plot No. 8 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG8_corrected_h.png [20250519_003831.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = 68.5259802694693 , a = 4784.81913306904 , b = 68.5259802694693 , d = 4739.03101269703 [20250519_003831.]: # CpG-site: CpG#8 Cubic: Using a = -4.65730461761547e-05 , b = 0.00676525443932464 , c = 0.747405842988874 , d = 1.5233701793389 [20250519_003832.]: Creating BiasCorrected (hyperbolic) plot No. 9 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG9_corrected_h.png [20250519_003832.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -1022.70071430348 , a = 1273.73160369723 , b = -1022.70071430348 , d = 1181.86620562781 [20250519_003832.]: # CpG-site: CpG#9 Cubic: Using a = 4.24791805783875e-06 , b = -0.00144169825546529 , c = 1.09837792836035 , d = -0.978987254622136 [20250519_003834.]: Creating BiasCorrected (hyperbolic) plot No. 10 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_rowmeans_corrected_h.png [20250519_003834.]: # CpG-site: row_means Hyperbolic: Using bias_weight = -1166.73385465972 , a = 1740.64188131784 , b = -1166.73385465972 , d = 1653.86486365297 [20250519_003834.]: # CpG-site: row_means Cubic: Using a = -8.71187897501959e-06 , b = 0.000715556896368103 , c = 1.00248226010187 , d = -0.420549765740683 [20250519_003835.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG1_corrected_h.png [20250519_003837.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG2_corrected_h.png [20250519_003838.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG3_corrected_h.png [20250519_003839.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG4_corrected_h.png [20250519_003841.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG5_corrected_h.png [20250519_003842.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG6_corrected_h.png [20250519_003843.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG7_corrected_h.png [20250519_003845.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG8_corrected_h.png [20250519_003846.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG9_corrected_h.png [20250519_003847.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_rowmeans_corrected_h.png [20250519_003848.]: Entered 'solving_equations'-Function [20250519_003848.]: Solving cubic regression for CpG#1 Coefficients: -1.03617340067344Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_003848.]: Samplename: 0 Root: 1.334 --> Root in between the borders! Added to results. Coefficients: -8.34150673400678Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_003848.]: Samplename: 12.5 Root: 11.446 --> Root in between the borders! Added to results. Coefficients: -15.3881734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_003848.]: Samplename: 25 Root: 22.228 --> Root in between the borders! Added to results. Coefficients: -24.2801734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_003848.]: Samplename: 37.5 Root: 36.374 --> Root in between the borders! Added to results. Coefficients: -34.9006734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_003848.]: Samplename: 50 Root: 52.044 --> Root in between the borders! Added to results. Coefficients: -46.3541734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_003848.]: Samplename: 62.5 Root: 66.144 --> Root in between the borders! Added to results. Coefficients: -55.8931734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_003848.]: Samplename: 75 Root: 75.864 --> Root in between the borders! Added to results. Coefficients: -63.0981734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_003848.]: Samplename: 87.5 Root: 82.254 --> Root in between the borders! Added to results. Coefficients: -91.0461734006735Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_003848.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.877 --> '100 < root < 110' --> substitute 100 [20250519_003848.]: Solving cubic regression for CpG#2 Coefficients: -0.283329966329966Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_003848.]: Samplename: 0 Root: 0.549 --> Root in between the borders! Added to results. Coefficients: -6.33999663299663Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_003848.]: Samplename: 12.5 Root: 11.533 --> Root in between the borders! Added to results. Coefficients: -15.93932996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_003848.]: Samplename: 25 Root: 26.628 --> Root in between the borders! Added to results. Coefficients: -22.33732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_003848.]: Samplename: 37.5 Root: 35.509 --> Root in between the borders! Added to results. Coefficients: -32.22832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_003848.]: Samplename: 50 Root: 47.851 --> Root in between the borders! Added to results. Coefficients: -49.96332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_003848.]: Samplename: 62.5 Root: 66.893 --> Root in between the borders! Added to results. Coefficients: -58.96582996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_003848.]: Samplename: 75 Root: 75.431 --> Root in between the borders! Added to results. Coefficients: -68.8366632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_003848.]: Samplename: 87.5 Root: 84.118 --> Root in between the borders! Added to results. Coefficients: -90.57732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_003849.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.287 --> '100 < root < 110' --> substitute 100 [20250519_003849.]: Solving cubic regression for CpG#3 Coefficients: -0.90294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_003849.]: Samplename: 0 Root: 1.441 --> Root in between the borders! Added to results. Coefficients: -6.57294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_003849.]: Samplename: 12.5 Root: 10.568 --> Root in between the borders! Added to results. Coefficients: -15.4289478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_003849.]: Samplename: 25 Root: 24.796 --> Root in between the borders! Added to results. Coefficients: -22.6129478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_003849.]: Samplename: 37.5 Root: 35.952 --> Root in between the borders! Added to results. Coefficients: -32.7754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_003849.]: Samplename: 50 Root: 50.684 --> Root in between the borders! Added to results. Coefficients: -43.8889478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_003849.]: Samplename: 62.5 Root: 65.142 --> Root in between the borders! Added to results. Coefficients: -54.9754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_003849.]: Samplename: 75 Root: 77.905 --> Root in between the borders! Added to results. Coefficients: -57.6562811447812Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_003849.]: Samplename: 87.5 Root: 80.767 --> Root in between the borders! Added to results. Coefficients: -80.6649478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_003849.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.38 --> '100 < root < 110' --> substitute 100 [20250519_003849.]: Solving cubic regression for CpG#4 Coefficients: -0.597449494949524Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_003849.]: Samplename: 0 Root: 0.858 --> Root in between the borders! Added to results. Coefficients: -8.25278282828286Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_003849.]: Samplename: 12.5 Root: 12.086 --> Root in between the borders! Added to results. Coefficients: -15.8034494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_003849.]: Samplename: 25 Root: 23.316 --> Root in between the borders! Added to results. Coefficients: -25.5274494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_003849.]: Samplename: 37.5 Root: 37.383 --> Root in between the borders! Added to results. Coefficients: -33.6369494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_003849.]: Samplename: 50 Root: 48.353 --> Root in between the borders! Added to results. Coefficients: -50.2554494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_003849.]: Samplename: 62.5 Root: 68.082 --> Root in between the borders! Added to results. Coefficients: -56.5394494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_003849.]: Samplename: 75 Root: 74.615 --> Root in between the borders! Added to results. Coefficients: -65.5927828282829Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_003849.]: Samplename: 87.5 Root: 83.254 --> Root in between the borders! Added to results. Coefficients: -88.3214494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_003849.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.715 --> '100 < root < 110' --> substitute 100 [20250519_003849.]: Solving cubic regression for CpG#5 Coefficients: -0.623961279461278Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_003849.]: Samplename: 0 Root: 1.458 --> Root in between the borders! Added to results. Coefficients: -4.76796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_003849.]: Samplename: 12.5 Root: 10.347 --> Root in between the borders! Added to results. Coefficients: -12.7259612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_003849.]: Samplename: 25 Root: 24.815 --> Root in between the borders! Added to results. Coefficients: -21.1599612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_003849.]: Samplename: 37.5 Root: 37.902 --> Root in between the borders! Added to results. Coefficients: -30.6954612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_003849.]: Samplename: 50 Root: 50.977 --> Root in between the borders! Added to results. Coefficients: -39.6579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_003849.]: Samplename: 62.5 Root: 62.126 --> Root in between the borders! Added to results. Coefficients: -51.6829612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_003849.]: Samplename: 75 Root: 75.852 --> Root in between the borders! Added to results. Coefficients: -61.0146279461279Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_003849.]: Samplename: 87.5 Root: 85.767 --> Root in between the borders! Added to results. Coefficients: -76.0699612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_003849.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.743 --> '100 < root < 110' --> substitute 100 [20250519_003849.]: Solving cubic regression for CpG#6 Coefficients: -0.196072390572403Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_003849.]: Samplename: 0 Root: 0.349 --> Root in between the borders! Added to results. Coefficients: -6.73873905723907Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_003849.]: Samplename: 12.5 Root: 11.718 --> Root in between the borders! Added to results. Coefficients: -15.8880723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_003849.]: Samplename: 25 Root: 26.396 --> Root in between the borders! Added to results. Coefficients: -22.0000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_003849.]: Samplename: 37.5 Root: 35.301 --> Root in between the borders! Added to results. Coefficients: -33.4445723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_003849.]: Samplename: 50 Root: 50.134 --> Root in between the borders! Added to results. Coefficients: -46.9000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_003849.]: Samplename: 62.5 Root: 64.993 --> Root in between the borders! Added to results. Coefficients: -55.8320723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_003849.]: Samplename: 75 Root: 73.639 --> Root in between the borders! Added to results. Coefficients: -71.5454057239057Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_003849.]: Samplename: 87.5 Root: 87.043 --> Root in between the borders! Added to results. Coefficients: -89.6560723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_003849.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.329 --> '100 < root < 110' --> substitute 100 [20250519_003849.]: Solving cubic regression for CpG#7 Coefficients: -1.21495454545456Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_003849.]: Samplename: 0 Root: 2.13 --> Root in between the borders! Added to results. Coefficients: -5.39562121212123Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_003849.]: Samplename: 12.5 Root: 9.973 --> Root in between the borders! Added to results. Coefficients: -11.2649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_003849.]: Samplename: 25 Root: 22.206 --> Root in between the borders! Added to results. Coefficients: -17.4509545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_003849.]: Samplename: 37.5 Root: 35.814 --> Root in between the borders! Added to results. Coefficients: -26.0314545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_003849.]: Samplename: 50 Root: 53.28 --> Root in between the borders! Added to results. Coefficients: -33.9649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_003849.]: Samplename: 62.5 Root: 66.598 --> Root in between the borders! Added to results. Coefficients: -41.1689545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_003849.]: Samplename: 75 Root: 76.575 --> Root in between the borders! Added to results. Coefficients: -44.1356212121212Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_003849.]: Samplename: 87.5 Root: 80.219 --> Root in between the borders! Added to results. Coefficients: -67.2229545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_003849.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.506 --> '100 < root < 110' --> substitute 100 [20250519_003849.]: Solving cubic regression for CpG#8 Coefficients: -1.09618518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_003849.]: Samplename: 0 Root: 2.016 --> Root in between the borders! Added to results. Coefficients: -5.44685185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_003849.]: Samplename: 12.5 Root: 9.458 --> Root in between the borders! Added to results. Coefficients: -16.9301851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_003849.]: Samplename: 25 Root: 26.35 --> Root in between the borders! Added to results. Coefficients: -23.3501851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_003849.]: Samplename: 37.5 Root: 34.728 --> Root in between the borders! Added to results. Coefficients: -37.6251851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_003849.]: Samplename: 50 Root: 51.781 --> Root in between the borders! Added to results. Coefficients: -48.8261851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_003849.]: Samplename: 62.5 Root: 64.192 --> Root in between the borders! Added to results. Coefficients: -61.6676851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_003849.]: Samplename: 75 Root: 77.804 --> Root in between the borders! Added to results. Coefficients: -64.5101851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_003849.]: Samplename: 87.5 Root: 80.758 --> Root in between the borders! Added to results. Coefficients: -86.0601851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_003849.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.834 --> '100 < root < 110' --> substitute 100 [20250519_003849.]: Solving cubic regression for CpG#9 Coefficients: -0.989865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_003849.]: Samplename: 0 Root: 1.475 --> Root in between the borders! Added to results. Coefficients: -6.39586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_003849.]: Samplename: 12.5 Root: 10.12 --> Root in between the borders! Added to results. Coefficients: -14.7058653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_003849.]: Samplename: 25 Root: 24.844 --> Root in between the borders! Added to results. Coefficients: -20.6238653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_003849.]: Samplename: 37.5 Root: 35.327 --> Root in between the borders! Added to results. Coefficients: -31.3958653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_003849.]: Samplename: 50 Root: 51.855 --> Root in between the borders! Added to results. Coefficients: -42.6858653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_003849.]: Samplename: 62.5 Root: 65.265 --> Root in between the borders! Added to results. Coefficients: -52.9033653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_003849.]: Samplename: 75 Root: 74.915 --> Root in between the borders! Added to results. Coefficients: -65.492531986532Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_003849.]: Samplename: 87.5 Root: 84.67 --> Root in between the borders! Added to results. Coefficients: -92.9898653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_003849.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.082 --> '100 < root < 110' --> substitute 100 [20250519_003849.]: Solving cubic regression for row_means Coefficients: -0.771215488215478Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_003849.]: Samplename: 0 Root: 1.287 --> Root in between the borders! Added to results. Coefficients: -6.47247474747474Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_003849.]: Samplename: 12.5 Root: 10.847 --> Root in between the borders! Added to results. Coefficients: -14.8972154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_003849.]: Samplename: 25 Root: 24.737 --> Root in between the borders! Added to results. Coefficients: -22.1492154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_003849.]: Samplename: 37.5 Root: 36.02 --> Root in between the borders! Added to results. Coefficients: -32.5259932659933Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_003849.]: Samplename: 50 Root: 50.639 --> Root in between the borders! Added to results. Coefficients: -44.7218821548821Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_003849.]: Samplename: 62.5 Root: 65.497 --> Root in between the borders! Added to results. Coefficients: -54.4032154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_003849.]: Samplename: 75 Root: 75.751 --> Root in between the borders! Added to results. Coefficients: -62.4313636363636Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_003849.]: Samplename: 87.5 Root: 83.403 --> Root in between the borders! Added to results. Coefficients: -84.7343265993266Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_003849.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.573 --> '100 < root < 110' --> substitute 100 [20250519_003849.]: ### Starting with regression calculations ### [20250519_003849.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_003849.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) [20250519_003849.]: Logging df_agg: CpG#1 [20250519_003849.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003849.]: c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)[20250519_003849.]: c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)[20250519_003849.]: c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) [20250519_003849.]: Entered 'hyperbolic_regression'-Function [20250519_003849.]: 'hyperbolic_regression': minmax = FALSE [20250519_003849.]: Entered 'cubic_regression'-Function [20250519_003849.]: 'cubic_regression': minmax = FALSE [20250519_003849.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) [20250519_003849.]: Logging df_agg: CpG#2 [20250519_003849.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003849.]: c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)[20250519_003849.]: c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)[20250519_003849.]: c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) [20250519_003849.]: Entered 'hyperbolic_regression'-Function [20250519_003849.]: 'hyperbolic_regression': minmax = FALSE [20250519_003850.]: Entered 'cubic_regression'-Function [20250519_003850.]: 'cubic_regression': minmax = FALSE [20250519_003850.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) [20250519_003850.]: Logging df_agg: CpG#3 [20250519_003850.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003850.]: c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)[20250519_003850.]: c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)[20250519_003850.]: c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) [20250519_003850.]: Entered 'hyperbolic_regression'-Function [20250519_003850.]: 'hyperbolic_regression': minmax = FALSE [20250519_003850.]: Entered 'cubic_regression'-Function [20250519_003850.]: 'cubic_regression': minmax = FALSE [20250519_003850.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) [20250519_003850.]: Logging df_agg: CpG#4 [20250519_003850.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003850.]: c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)[20250519_003850.]: c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)[20250519_003850.]: c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) [20250519_003850.]: Entered 'hyperbolic_regression'-Function [20250519_003850.]: 'hyperbolic_regression': minmax = FALSE [20250519_003850.]: Entered 'cubic_regression'-Function [20250519_003850.]: 'cubic_regression': minmax = FALSE [20250519_003850.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) [20250519_003850.]: Logging df_agg: CpG#5 [20250519_003850.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003850.]: c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)[20250519_003850.]: c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)[20250519_003850.]: c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) [20250519_003850.]: Entered 'hyperbolic_regression'-Function [20250519_003850.]: 'hyperbolic_regression': minmax = FALSE [20250519_003850.]: Entered 'cubic_regression'-Function [20250519_003850.]: 'cubic_regression': minmax = FALSE [20250519_003850.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) [20250519_003850.]: Logging df_agg: CpG#6 [20250519_003850.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003850.]: c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)[20250519_003850.]: c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)[20250519_003850.]: c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) [20250519_003850.]: Entered 'hyperbolic_regression'-Function [20250519_003850.]: 'hyperbolic_regression': minmax = FALSE [20250519_003851.]: Entered 'cubic_regression'-Function [20250519_003851.]: 'cubic_regression': minmax = FALSE [20250519_003851.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) [20250519_003851.]: Logging df_agg: CpG#7 [20250519_003851.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003851.]: c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)[20250519_003851.]: c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)[20250519_003851.]: c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) [20250519_003851.]: Entered 'hyperbolic_regression'-Function [20250519_003851.]: 'hyperbolic_regression': minmax = FALSE [20250519_003851.]: Entered 'cubic_regression'-Function [20250519_003851.]: 'cubic_regression': minmax = FALSE [20250519_003851.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) [20250519_003851.]: Logging df_agg: CpG#8 [20250519_003851.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003851.]: c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)[20250519_003851.]: c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)[20250519_003851.]: c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) [20250519_003851.]: Entered 'hyperbolic_regression'-Function [20250519_003851.]: 'hyperbolic_regression': minmax = FALSE [20250519_003851.]: Entered 'cubic_regression'-Function [20250519_003851.]: 'cubic_regression': minmax = FALSE [20250519_003851.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) [20250519_003851.]: Logging df_agg: CpG#9 [20250519_003851.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003851.]: c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)[20250519_003851.]: c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)[20250519_003851.]: c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) [20250519_003851.]: Entered 'hyperbolic_regression'-Function [20250519_003851.]: 'hyperbolic_regression': minmax = FALSE [20250519_003851.]: Entered 'cubic_regression'-Function [20250519_003851.]: 'cubic_regression': minmax = FALSE [20250519_003851.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) [20250519_003851.]: Logging df_agg: row_means [20250519_003851.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003851.]: c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)[20250519_003851.]: c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)[20250519_003851.]: c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) [20250519_003851.]: Entered 'hyperbolic_regression'-Function [20250519_003851.]: 'hyperbolic_regression': minmax = FALSE [20250519_003852.]: Entered 'cubic_regression'-Function [20250519_003852.]: 'cubic_regression': minmax = FALSE [20250519_003852.]: ### Starting with plotting ### [20250519_003852.]: Creating BiasCorrected (cubic) plot No. 1 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG1_corrected_c.png [20250519_003852.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -1164.78037112998 , a = 2039.49073397227 , b = -1164.78037112998 , d = 1955.827713817 [20250519_003852.]: # CpG-site: CpG#1 Cubic: Using a = -3.42821196240366e-05 , b = 0.00462173155013798 , c = 0.850326258865826 , d = 0.500073874211125 [20250519_003854.]: Creating BiasCorrected (cubic) plot No. 2 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG2_corrected_c.png [20250519_003854.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = -464.14479041434 , a = 4988.85430873436 , b = -464.14479041434 , d = 4925.32709283236 [20250519_003854.]: # CpG-site: CpG#2 Cubic: Using a = -1.40811796906314e-05 , b = 0.00190849431754485 , c = 0.933455870941473 , d = 0.363096210099674 [20250519_003855.]: Creating BiasCorrected (cubic) plot No. 3 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG3_corrected_c.png [20250519_003855.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = -745.616753604673 , a = 2026.5285182947 , b = -745.616753604673 , d = 1953.27811759735 [20250519_003855.]: # CpG-site: CpG#3 Cubic: Using a = -2.78835051857186e-05 , b = 0.00366863707889089 , c = 0.880665171220893 , d = 0.510779308833653 [20250519_003857.]: Creating BiasCorrected (cubic) plot No. 4 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG4_corrected_c.png [20250519_003857.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = -833.077868829112 , a = 2871.13688289992 , b = -833.077868829112 , d = 2796.8115417971 [20250519_003857.]: # CpG-site: CpG#4 Cubic: Using a = -2.30028865643e-05 , b = 0.00309017851940692 , c = 0.897073107777645 , d = 0.443455761841866 [20250519_003858.]: Creating BiasCorrected (cubic) plot No. 5 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG5_corrected_c.png [20250519_003858.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = 222.093251203934 , a = 12477.4109611091 , b = 222.093251203934 , d = 12432.0779293841 [20250519_003858.]: # CpG-site: CpG#5 Cubic: Using a = -1.28632696989134e-05 , b = 0.00184822285299014 , c = 0.931001046631176 , d = 0.437963095185599 [20250519_003900.]: Creating BiasCorrected (cubic) plot No. 6 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG6_corrected_c.png [20250519_003900.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = -700.460688613084 , a = 21299.059407053 , b = -700.460688613084 , d = 21234.4652853188 [20250519_003900.]: # CpG-site: CpG#6 Cubic: Using a = -5.838335151375e-06 , b = 0.000828447163218526 , c = 0.970047764475166 , d = 0.158074701412251 [20250519_003901.]: Creating BiasCorrected (cubic) plot No. 7 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG7_corrected_c.png [20250519_003901.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -1144.91191228206 , a = 1477.91098401837 , b = -1144.91191228206 , d = 1394.69940178972 [20250519_003901.]: # CpG-site: CpG#7 Cubic: Using a = -4.54220535614047e-05 , b = 0.00607993130565131 , c = 0.805411493452389 , d = 0.615125382790254 [20250519_003903.]: Creating BiasCorrected (cubic) plot No. 8 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG8_corrected_c.png [20250519_003903.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = -349.37367288786 , a = 2377.38756952122 , b = -349.37367288786 , d = 2315.21494778767 [20250519_003903.]: # CpG-site: CpG#8 Cubic: Using a = -2.64622306669639e-05 , b = 0.00353621593528015 , c = 0.877916844709074 , d = 0.698684606493051 [20250519_003904.]: Creating BiasCorrected (cubic) plot No. 9 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG9_corrected_c.png [20250519_003904.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -1536.91699417279 , a = 3745.17619112231 , b = -1536.91699417279 , d = 3656.26801968932 [20250519_003904.]: # CpG-site: CpG#9 Cubic: Using a = -2.70976130611725e-05 , b = 0.00378478247182342 , c = 0.871825263051793 , d = 0.455475740974113 [20250519_003906.]: Creating BiasCorrected (cubic) plot No. 10 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_rowmeans_corrected_c.png [20250519_003906.]: # CpG-site: row_means Hyperbolic: Using bias_weight = -820.643805549713 , a = 3198.46619445775 , b = -820.643805549713 , d = 3124.21083989374 [20250519_003906.]: # CpG-site: row_means Cubic: Using a = -2.34328362100009e-05 , b = 0.00319139073176266 , c = 0.891842168032129 , d = 0.493517689787135 [20250519_003907.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG1_corrected_c.png [20250519_003909.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG2_corrected_c.png [20250519_003910.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG3_corrected_c.png [20250519_003911.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG4_corrected_c.png [20250519_003912.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG5_corrected_c.png [20250519_003914.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG6_corrected_c.png [20250519_003915.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG7_corrected_c.png [20250519_003916.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG8_corrected_c.png [20250519_003918.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG9_corrected_c.png [20250519_003919.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_rowmeans_corrected_c.png [20250519_003920.]: Entered 'solving_equations'-Function [20250519_003920.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 79.867338305743 [20250519_003920.]: Samplename: Sample#1 Root: 79.867 --> Root in between the borders! Added to results. Hyperbolic solved: 29.7900144307253 [20250519_003920.]: Samplename: Sample#10 Root: 29.79 --> Root in between the borders! Added to results. Hyperbolic solved: 41.6525328583179 [20250519_003920.]: Samplename: Sample#2 Root: 41.653 --> Root in between the borders! Added to results. Hyperbolic solved: 57.4651978180159 [20250519_003920.]: Samplename: Sample#3 Root: 57.465 --> Root in between the borders! Added to results. Hyperbolic solved: 9.20072297150156 [20250519_003920.]: Samplename: Sample#4 Root: 9.201 --> Root in between the borders! Added to results. Hyperbolic solved: 21.8059605790798 [20250519_003920.]: Samplename: Sample#5 Root: 21.806 --> Root in between the borders! Added to results. Hyperbolic solved: 23.0837964625959 [20250519_003920.]: Samplename: Sample#6 Root: 23.084 --> Root in between the borders! Added to results. Hyperbolic solved: 45.5034148455809 [20250519_003920.]: Samplename: Sample#7 Root: 45.503 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6987854360424 [20250519_003920.]: Samplename: Sample#8 Root: 85.699 --> Root in between the borders! Added to results. Hyperbolic solved: -3.66510576608508 [20250519_003920.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -3.665 --> '-10 < root < 0' --> substitute 0 [20250519_003921.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 76.7227930253137 [20250519_003921.]: Samplename: Sample#1 Root: 76.723 --> Root in between the borders! Added to results. Hyperbolic solved: 30.9924274267338 [20250519_003921.]: Samplename: Sample#10 Root: 30.992 --> Root in between the borders! Added to results. Hyperbolic solved: 42.6810881616502 [20250519_003921.]: Samplename: Sample#2 Root: 42.681 --> Root in between the borders! Added to results. Hyperbolic solved: 58.93379071467 [20250519_003921.]: Samplename: Sample#3 Root: 58.934 --> Root in between the borders! Added to results. Hyperbolic solved: 4.98020463922415 [20250519_003921.]: Samplename: Sample#4 Root: 4.98 --> Root in between the borders! Added to results. Hyperbolic solved: 20.2480263316954 [20250519_003921.]: Samplename: Sample#5 Root: 20.248 --> Root in between the borders! Added to results. Hyperbolic solved: 17.8133085422138 [20250519_003921.]: Samplename: Sample#6 Root: 17.813 --> Root in between the borders! Added to results. Hyperbolic solved: 40.917674420917 [20250519_003921.]: Samplename: Sample#7 Root: 40.918 --> Root in between the borders! Added to results. Hyperbolic solved: 86.045071534744 [20250519_003921.]: Samplename: Sample#8 Root: 86.045 --> Root in between the borders! Added to results. Hyperbolic solved: 3.1157977862424 [20250519_003921.]: Samplename: Sample#9 Root: 3.116 --> Root in between the borders! Added to results. [20250519_003921.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 74.9349278251272 [20250519_003921.]: Samplename: Sample#1 Root: 74.935 --> Root in between the borders! Added to results. Hyperbolic solved: 27.6844390885298 [20250519_003921.]: Samplename: Sample#10 Root: 27.684 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8520223561161 [20250519_003921.]: Samplename: Sample#2 Root: 41.852 --> Root in between the borders! Added to results. Hyperbolic solved: 55.8325219766746 [20250519_003921.]: Samplename: Sample#3 Root: 55.833 --> Root in between the borders! Added to results. Hyperbolic solved: 8.03518759540723 [20250519_003921.]: Samplename: Sample#4 Root: 8.035 --> Root in between the borders! Added to results. Hyperbolic solved: 24.1066316660805 [20250519_003921.]: Samplename: Sample#5 Root: 24.107 --> Root in between the borders! Added to results. Hyperbolic solved: 26.2419826081677 [20250519_003921.]: Samplename: Sample#6 Root: 26.242 --> Root in between the borders! Added to results. Hyperbolic solved: 44.0944957314077 [20250519_003921.]: Samplename: Sample#7 Root: 44.094 --> Root in between the borders! Added to results. Hyperbolic solved: 85.8279384942248 [20250519_003921.]: Samplename: Sample#8 Root: 85.828 --> Root in between the borders! Added to results. Hyperbolic solved: -0.66649088715512 [20250519_003921.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.666 --> '-10 < root < 0' --> substitute 0 [20250519_003921.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 76.349525731503 [20250519_003921.]: Samplename: Sample#1 Root: 76.35 --> Root in between the borders! Added to results. Hyperbolic solved: 28.2568543207918 [20250519_003921.]: Samplename: Sample#10 Root: 28.257 --> Root in between the borders! Added to results. Hyperbolic solved: 43.408980967851 [20250519_003921.]: Samplename: Sample#2 Root: 43.409 --> Root in between the borders! Added to results. Hyperbolic solved: 58.5435202418955 [20250519_003921.]: Samplename: Sample#3 Root: 58.544 --> Root in between the borders! Added to results. Hyperbolic solved: 10.308707716736 [20250519_003921.]: Samplename: Sample#4 Root: 10.309 --> Root in between the borders! Added to results. Hyperbolic solved: 22.1830453155629 [20250519_003921.]: Samplename: Sample#5 Root: 22.183 --> Root in between the borders! Added to results. Hyperbolic solved: 27.133776120676 [20250519_003921.]: Samplename: Sample#6 Root: 27.134 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8321067699793 [20250519_003921.]: Samplename: Sample#7 Root: 41.832 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6890182703099 [20250519_003921.]: Samplename: Sample#8 Root: 85.689 --> Root in between the borders! Added to results. Hyperbolic solved: 2.42232661674155 [20250519_003921.]: Samplename: Sample#9 Root: 2.422 --> Root in between the borders! Added to results. [20250519_003921.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 72.8126911247073 [20250519_003921.]: Samplename: Sample#1 Root: 72.813 --> Root in between the borders! Added to results. Hyperbolic solved: 26.4206935777278 [20250519_003921.]: Samplename: Sample#10 Root: 26.421 --> Root in between the borders! Added to results. Hyperbolic solved: 44.2738761535648 [20250519_003921.]: Samplename: Sample#2 Root: 44.274 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5324382038438 [20250519_003921.]: Samplename: Sample#3 Root: 53.532 --> Root in between the borders! Added to results. Hyperbolic solved: 10.0373723654377 [20250519_003921.]: Samplename: Sample#4 Root: 10.037 --> Root in between the borders! Added to results. Hyperbolic solved: 22.8733116049306 [20250519_003921.]: Samplename: Sample#5 Root: 22.873 --> Root in between the borders! Added to results. Hyperbolic solved: 25.9417915879453 [20250519_003921.]: Samplename: Sample#6 Root: 25.942 --> Root in between the borders! Added to results. Hyperbolic solved: 42.7945071576182 [20250519_003921.]: Samplename: Sample#7 Root: 42.795 --> Root in between the borders! Added to results. Hyperbolic solved: 88.8741214991654 [20250519_003921.]: Samplename: Sample#8 Root: 88.874 --> Root in between the borders! Added to results. Hyperbolic solved: 2.34647622155857 [20250519_003921.]: Samplename: Sample#9 Root: 2.346 --> Root in between the borders! Added to results. [20250519_003921.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 79.278059253184 [20250519_003921.]: Samplename: Sample#1 Root: 79.278 --> Root in between the borders! Added to results. Hyperbolic solved: 30.2012459144106 [20250519_003921.]: Samplename: Sample#10 Root: 30.201 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8474392901329 [20250519_003921.]: Samplename: Sample#2 Root: 41.847 --> Root in between the borders! Added to results. Hyperbolic solved: 56.8423515999057 [20250519_003921.]: Samplename: Sample#3 Root: 56.842 --> Root in between the borders! Added to results. Hyperbolic solved: 8.87856073331453 [20250519_003921.]: Samplename: Sample#4 Root: 8.879 --> Root in between the borders! Added to results. Hyperbolic solved: 18.6901596391315 [20250519_003921.]: Samplename: Sample#5 Root: 18.69 --> Root in between the borders! Added to results. Hyperbolic solved: 29.9309263719602 [20250519_003921.]: Samplename: Sample#6 Root: 29.931 --> Root in between the borders! Added to results. Hyperbolic solved: 42.814855924799 [20250519_003921.]: Samplename: Sample#7 Root: 42.815 --> Root in between the borders! Added to results. Hyperbolic solved: 86.75018307028 [20250519_003921.]: Samplename: Sample#8 Root: 86.75 --> Root in between the borders! Added to results. Hyperbolic solved: 1.51516233882239 [20250519_003921.]: Samplename: Sample#9 Root: 1.515 --> Root in between the borders! Added to results. [20250519_003921.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 78.2565541874121 [20250519_003921.]: Samplename: Sample#1 Root: 78.257 --> Root in between the borders! Added to results. Hyperbolic solved: 25.488740177574 [20250519_003921.]: Samplename: Sample#10 Root: 25.489 --> Root in between the borders! Added to results. Hyperbolic solved: 47.3712173704701 [20250519_003921.]: Samplename: Sample#2 Root: 47.371 --> Root in between the borders! Added to results. Hyperbolic solved: 58.3142577916519 [20250519_003921.]: Samplename: Sample#3 Root: 58.314 --> Root in between the borders! Added to results. Hyperbolic solved: 11.7212348600366 [20250519_003921.]: Samplename: Sample#4 Root: 11.721 --> Root in between the borders! Added to results. Hyperbolic solved: 25.3797494985646 [20250519_003921.]: Samplename: Sample#5 Root: 25.38 --> Root in between the borders! Added to results. Hyperbolic solved: 29.4095117400265 [20250519_003921.]: Samplename: Sample#6 Root: 29.41 --> Root in between the borders! Added to results. Hyperbolic solved: 44.5754992753069 [20250519_003921.]: Samplename: Sample#7 Root: 44.575 --> Root in between the borders! Added to results. Hyperbolic solved: 85.9628719316752 [20250519_003921.]: Samplename: Sample#8 Root: 85.963 --> Root in between the borders! Added to results. Hyperbolic solved: -4.16453560967523 [20250519_003921.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -4.165 --> '-10 < root < 0' --> substitute 0 [20250519_003921.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 72.9696611539955 [20250519_003921.]: Samplename: Sample#1 Root: 72.97 --> Root in between the borders! Added to results. Hyperbolic solved: 27.8250961443825 [20250519_003921.]: Samplename: Sample#10 Root: 27.825 --> Root in between the borders! Added to results. Hyperbolic solved: 34.9068396054724 [20250519_003921.]: Samplename: Sample#2 Root: 34.907 --> Root in between the borders! Added to results. Hyperbolic solved: 59.163744843639 [20250519_003921.]: Samplename: Sample#3 Root: 59.164 --> Root in between the borders! Added to results. Hyperbolic solved: 9.64644348381889 [20250519_003921.]: Samplename: Sample#4 Root: 9.646 --> Root in between the borders! Added to results. Hyperbolic solved: 19.3522608313684 [20250519_003921.]: Samplename: Sample#5 Root: 19.352 --> Root in between the borders! Added to results. Hyperbolic solved: 38.5571445759356 [20250519_003921.]: Samplename: Sample#6 Root: 38.557 --> Root in between the borders! Added to results. Hyperbolic solved: 44.7759635921689 [20250519_003921.]: Samplename: Sample#7 Root: 44.776 --> Root in between the borders! Added to results. Hyperbolic solved: 84.8599672002532 [20250519_003921.]: Samplename: Sample#8 Root: 84.86 --> Root in between the borders! Added to results. Hyperbolic solved: -2.05045066787228 [20250519_003921.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.05 --> '-10 < root < 0' --> substitute 0 [20250519_003921.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 81.0416669543132 [20250519_003921.]: Samplename: Sample#1 Root: 81.042 --> Root in between the borders! Added to results. Hyperbolic solved: 26.6441665578561 [20250519_003921.]: Samplename: Sample#10 Root: 26.644 --> Root in between the borders! Added to results. Hyperbolic solved: 45.7046185029368 [20250519_003921.]: Samplename: Sample#2 Root: 45.705 --> Root in between the borders! Added to results. Hyperbolic solved: 56.9767969678006 [20250519_003921.]: Samplename: Sample#3 Root: 56.977 --> Root in between the borders! Added to results. Hyperbolic solved: 6.81231488238752 [20250519_003921.]: Samplename: Sample#4 Root: 6.812 --> Root in between the borders! Added to results. Hyperbolic solved: 22.928478377057 [20250519_003921.]: Samplename: Sample#5 Root: 22.928 --> Root in between the borders! Added to results. Hyperbolic solved: 38.8653192337298 [20250519_003921.]: Samplename: Sample#6 Root: 38.865 --> Root in between the borders! Added to results. Hyperbolic solved: 43.6855618249121 [20250519_003921.]: Samplename: Sample#7 Root: 43.686 --> Root in between the borders! Added to results. Hyperbolic solved: 88.0155362134065 [20250519_003921.]: Samplename: Sample#8 Root: 88.016 --> Root in between the borders! Added to results. Hyperbolic solved: -3.35609615796437 [20250519_003921.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -3.356 --> '-10 < root < 0' --> substitute 0 [20250519_003921.]: Solving hyperbolic regression for row_means Hyperbolic solved: 77.0692788889392 [20250519_003921.]: Samplename: Sample#1 Root: 77.069 --> Root in between the borders! Added to results. Hyperbolic solved: 28.3620037732087 [20250519_003921.]: Samplename: Sample#10 Root: 28.362 --> Root in between the borders! Added to results. Hyperbolic solved: 42.5026157763218 [20250519_003921.]: Samplename: Sample#2 Root: 42.503 --> Root in between the borders! Added to results. Hyperbolic solved: 57.2972029249363 [20250519_003921.]: Samplename: Sample#3 Root: 57.297 --> Root in between the borders! Added to results. Hyperbolic solved: 8.82704268325745 [20250519_003921.]: Samplename: Sample#4 Root: 8.827 --> Root in between the borders! Added to results. Hyperbolic solved: 21.8102595595999 [20250519_003921.]: Samplename: Sample#5 Root: 21.81 --> Root in between the borders! Added to results. Hyperbolic solved: 28.7228654114699 [20250519_003921.]: Samplename: Sample#6 Root: 28.723 --> Root in between the borders! Added to results. Hyperbolic solved: 43.4105084715017 [20250519_003921.]: Samplename: Sample#7 Root: 43.411 --> Root in between the borders! Added to results. Hyperbolic solved: 86.4143551575745 [20250519_003921.]: Samplename: Sample#8 Root: 86.414 --> Root in between the borders! Added to results. Hyperbolic solved: -0.237016010782264 [20250519_003921.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.237 --> '-10 < root < 0' --> substitute 0 [20250519_003921.]: on_start: using future::plan("sequential") [20250519_003921.]: Entered 'clean_dt'-Function [20250519_003921.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_003921.]: got experimental data [20250519_003921.]: Entered 'clean_dt'-Function [20250519_003921.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_003921.]: got calibration data [20250519_003921.]: ### Starting with regression calculations ### [20250519_003921.]: Entered 'regression_type1'-Function [20250519_003921.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_003921.]: Logging df_agg: CpG#1 [20250519_003921.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003921.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_003921.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_003921.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_003921.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_003921.]: Entered 'hyperbolic_regression'-Function [20250519_003921.]: 'hyperbolic_regression': minmax = FALSE [20250519_003921.]: Entered 'cubic_regression'-Function [20250519_003921.]: 'cubic_regression': minmax = FALSE [20250519_003921.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_003921.]: Logging df_agg: CpG#2 [20250519_003921.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003921.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_003921.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_003921.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_003921.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_003921.]: Entered 'hyperbolic_regression'-Function [20250519_003921.]: 'hyperbolic_regression': minmax = FALSE [20250519_003921.]: Entered 'cubic_regression'-Function [20250519_003921.]: 'cubic_regression': minmax = FALSE [20250519_003921.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_003921.]: Logging df_agg: CpG#3 [20250519_003921.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003921.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_003921.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_003921.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_003921.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_003921.]: Entered 'hyperbolic_regression'-Function [20250519_003921.]: 'hyperbolic_regression': minmax = FALSE [20250519_003921.]: Entered 'cubic_regression'-Function [20250519_003921.]: 'cubic_regression': minmax = FALSE [20250519_003921.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_003921.]: Logging df_agg: CpG#4 [20250519_003921.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003921.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_003921.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_003921.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_003921.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_003921.]: Entered 'hyperbolic_regression'-Function [20250519_003921.]: 'hyperbolic_regression': minmax = FALSE [20250519_003921.]: Entered 'cubic_regression'-Function [20250519_003921.]: 'cubic_regression': minmax = FALSE [20250519_003921.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_003921.]: Logging df_agg: CpG#5 [20250519_003921.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003921.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_003921.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_003921.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_003921.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_003921.]: Entered 'hyperbolic_regression'-Function [20250519_003921.]: 'hyperbolic_regression': minmax = FALSE [20250519_003921.]: Entered 'cubic_regression'-Function [20250519_003921.]: 'cubic_regression': minmax = FALSE [20250519_003921.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_003921.]: Logging df_agg: CpG#6 [20250519_003921.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003921.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_003921.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_003921.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_003921.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_003921.]: Entered 'hyperbolic_regression'-Function [20250519_003921.]: 'hyperbolic_regression': minmax = FALSE [20250519_003921.]: Entered 'cubic_regression'-Function [20250519_003921.]: 'cubic_regression': minmax = FALSE [20250519_003921.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_003921.]: Logging df_agg: CpG#7 [20250519_003921.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003921.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_003921.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_003921.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_003921.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_003921.]: Entered 'hyperbolic_regression'-Function [20250519_003921.]: 'hyperbolic_regression': minmax = FALSE [20250519_003921.]: Entered 'cubic_regression'-Function [20250519_003921.]: 'cubic_regression': minmax = FALSE [20250519_003921.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_003921.]: Logging df_agg: CpG#8 [20250519_003921.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003921.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_003921.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_003921.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_003921.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_003921.]: Entered 'hyperbolic_regression'-Function [20250519_003921.]: 'hyperbolic_regression': minmax = FALSE [20250519_003921.]: Entered 'cubic_regression'-Function [20250519_003921.]: 'cubic_regression': minmax = FALSE [20250519_003921.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_003921.]: Logging df_agg: CpG#9 [20250519_003921.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003921.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_003921.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_003921.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_003921.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_003921.]: Entered 'hyperbolic_regression'-Function [20250519_003921.]: 'hyperbolic_regression': minmax = FALSE [20250519_003921.]: Entered 'cubic_regression'-Function [20250519_003921.]: 'cubic_regression': minmax = FALSE [20250519_003921.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723122, 2.47760998435345)c(5.94911111111111, 0.84962962962963, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703704, 19.6995555555555, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_003921.]: Logging df_agg: row_means [20250519_003921.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003921.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_003921.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723122, 2.47760998435345)[20250519_003921.]: c(5.94911111111111, 0.84962962962963, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_003921.]: c(NA, 6.79703703703704, 19.6995555555555, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_003921.]: Entered 'hyperbolic_regression'-Function [20250519_003921.]: 'hyperbolic_regression': minmax = FALSE [20250519_003922.]: Entered 'cubic_regression'-Function [20250519_003922.]: 'cubic_regression': minmax = FALSE [20250519_003922.]: ### Starting with plotting ### [20250519_003922.]: Creating plot No. 1 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG1.png [20250519_003922.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -123249135.40483 , a = 49589952.0989949 , b = -123249135.40483 , d = 59280789.0121927 [20250519_003922.]: # CpG-site: CpG#1 Cubic: Using a = 6.53413423120091e-05 , b = -0.0055806968734969 , c = 0.784061853455188 , d = 1.93182659932656 [20250519_003924.]: Creating plot No. 2 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG2.png [20250519_003924.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = 167929575.171327 , a = 42299444.0962795 , b = 167929575.171327 , d = 47897274.2220611 [20250519_003924.]: # CpG-site: CpG#2 Cubic: Using a = 8.04237934904601e-06 , b = 0.00293158941798942 , c = 0.514821742825076 , d = 9.27667003367003 [20250519_003925.]: Creating plot No. 3 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG3.png [20250519_003925.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = 31004745.3756238 , a = 31050253.3779659 , b = 31004745.3756238 , d = 40665231.758814 [20250519_003925.]: # CpG-site: CpG#3 Cubic: Using a = 2.30555869809204e-05 , b = -0.000797009331409346 , c = 0.62783129228796 , d = 3.88705218855218 [20250519_003927.]: Creating plot No. 4 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG4.png [20250519_003927.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = 128022258.276155 , a = 42779497.5538086 , b = 128022258.276155 , d = 50999439.9227065 [20250519_003927.]: # CpG-site: CpG#4 Cubic: Using a = 3.2166356902357e-05 , b = -0.0015913142857143 , c = 0.697398364598366 , d = 6.17055050505048 [20250519_003928.]: Creating plot No. 5 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG5.png [20250519_003928.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = 29447513.6184781 , a = 41864399.8673718 , b = 29447513.6184781 , d = 55382346.8470806 [20250519_003928.]: # CpG-site: CpG#5 Cubic: Using a = -4.48459708193036e-06 , b = 0.00375371236171235 , c = 0.422446384479718 , d = 5.17203872053872 [20250519_003930.]: Creating plot No. 6 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG6.png [20250519_003930.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = 17150080.5960938 , a = 40932834.1078748 , b = 17150080.5960938 , d = 46723471.6686748 [20250519_003930.]: # CpG-site: CpG#6 Cubic: Using a = 2.38852884399552e-05 , b = 0.000918637037037021 , c = 0.561022132435467 , d = 6.1479276094276 [20250519_003931.]: Creating plot No. 7 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG7.png [20250519_003931.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -57204506.2862076 , a = 29554652.6579632 , b = -57204506.2862076 , d = 48548425.6483363 [20250519_003931.]: # CpG-site: CpG#7 Cubic: Using a = 4.98010505050505e-05 , b = -0.00436152150072151 , c = 0.579588917748918 , d = 1.72104545454544 [20250519_003933.]: Creating plot No. 8 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG8.png [20250519_003933.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = 263871870.982016 , a = 38159683.1948542 , b = 263871870.982016 , d = 45284547.6704654 [20250519_003933.]: # CpG-site: CpG#8 Cubic: Using a = -1.50060965207632e-05 , b = 0.00447978143338143 , c = 0.534897737694404 , d = 9.56981481481482 [20250519_003934.]: Creating plot No. 9 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG9.png [20250519_003934.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -224404013.660658 , a = 51672526.9851193 , b = -224404013.660658 , d = 60201205.3472543 [20250519_003934.]: # CpG-site: CpG#9 Cubic: Using a = 7.72300426487093e-05 , b = -0.00542281173641174 , c = 0.67899229597563 , d = 2.72413468013467 [20250519_003936.]: Creating plot No. 10 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_rowmeans.png [20250519_003936.]: # CpG-site: row_means Hyperbolic: Using bias_weight = 40333655.1288316 , a = 44937623.5310126 , b = 40333655.1288316 , d = 55678831.3245349 [20250519_003936.]: # CpG-site: row_means Cubic: Using a = 2.88923726150392e-05 , b = -0.0006299592752926 , c = 0.600117857944524 , d = 5.17789562289563 [20250519_003937.]: Entered 'solving_equations'-Function [20250519_003937.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 6.03337011705089 [20250519_003937.]: Samplename: 0 Root: 6.033 --> Root in between the borders! Added to results. Hyperbolic solved: 14.7663097441095 [20250519_003937.]: Samplename: 12.5 Root: 14.766 --> Root in between the borders! Added to results. Hyperbolic solved: 23.1900365713551 [20250519_003937.]: Samplename: 25 Root: 23.19 --> Root in between the borders! Added to results. Hyperbolic solved: 33.8197153352119 [20250519_003937.]: Samplename: 37.5 Root: 33.82 --> Root in between the borders! Added to results. Hyperbolic solved: 46.5156833662696 [20250519_003937.]: Samplename: 50 Root: 46.516 --> Root in between the borders! Added to results. Hyperbolic solved: 60.2074431300908 [20250519_003937.]: Samplename: 62.5 Root: 60.207 --> Root in between the borders! Added to results. Hyperbolic solved: 71.6105733239506 [20250519_003937.]: Samplename: 75 Root: 71.611 --> Root in between the borders! Added to results. Hyperbolic solved: 80.2235915426204 [20250519_003937.]: Samplename: 87.5 Root: 80.224 --> Root in between the borders! Added to results. Hyperbolic solved: 113.633279788745 [20250519_003937.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20250519_003937.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 6.85513576175294 [20250519_003937.]: Samplename: 0 Root: 6.855 --> Root in between the borders! Added to results. Hyperbolic solved: 13.7133339963707 [20250519_003937.]: Samplename: 12.5 Root: 13.713 --> Root in between the borders! Added to results. Hyperbolic solved: 24.5830349243156 [20250519_003937.]: Samplename: 25 Root: 24.583 --> Root in between the borders! Added to results. Hyperbolic solved: 31.8277435330554 [20250519_003937.]: Samplename: 37.5 Root: 31.828 --> Root in between the borders! Added to results. Hyperbolic solved: 43.0277185945539 [20250519_003937.]: Samplename: 50 Root: 43.028 --> Root in between the borders! Added to results. Hyperbolic solved: 63.1097818281222 [20250519_003937.]: Samplename: 62.5 Root: 63.11 --> Root in between the borders! Added to results. Hyperbolic solved: 73.3036858899805 [20250519_003937.]: Samplename: 75 Root: 73.304 --> Root in between the borders! Added to results. Hyperbolic solved: 84.4808447799272 [20250519_003937.]: Samplename: 87.5 Root: 84.481 --> Root in between the borders! Added to results. Hyperbolic solved: 109.0987325611 [20250519_003937.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 109.099 --> '100 < root < 110' --> substitute 100 [20250519_003937.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 5.27473119209009 [20250519_003937.]: Samplename: 0 Root: 5.275 --> Root in between the borders! Added to results. Hyperbolic solved: 12.7004989900866 [20250519_003937.]: Samplename: 12.5 Root: 12.7 --> Root in between the borders! Added to results. Hyperbolic solved: 24.2988465005425 [20250519_003937.]: Samplename: 25 Root: 24.299 --> Root in between the borders! Added to results. Hyperbolic solved: 33.7074476249853 [20250519_003937.]: Samplename: 37.5 Root: 33.707 --> Root in between the borders! Added to results. Hyperbolic solved: 47.0168800224423 [20250519_003937.]: Samplename: 50 Root: 47.017 --> Root in between the borders! Added to results. Hyperbolic solved: 61.5718102366584 [20250519_003937.]: Samplename: 62.5 Root: 61.572 --> Root in between the borders! Added to results. Hyperbolic solved: 76.091389954119 [20250519_003937.]: Samplename: 75 Root: 76.091 --> Root in between the borders! Added to results. Hyperbolic solved: 79.6023799465703 [20250519_003937.]: Samplename: 87.5 Root: 79.602 --> Root in between the borders! Added to results. Hyperbolic solved: 109.736019452694 [20250519_003937.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 109.736 --> '100 < root < 110' --> substitute 100 [20250519_003937.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 5.07584235183922 [20250519_003937.]: Samplename: 0 Root: 5.076 --> Root in between the borders! Added to results. Hyperbolic solved: 14.2021272601476 [20250519_003937.]: Samplename: 12.5 Root: 14.202 --> Root in between the borders! Added to results. Hyperbolic solved: 23.203637284743 [20250519_003937.]: Samplename: 25 Root: 23.204 --> Root in between the borders! Added to results. Hyperbolic solved: 34.7960863609446 [20250519_003937.]: Samplename: 37.5 Root: 34.796 --> Root in between the borders! Added to results. Hyperbolic solved: 44.4638162018573 [20250519_003937.]: Samplename: 50 Root: 44.464 --> Root in between the borders! Added to results. Hyperbolic solved: 64.2755507391393 [20250519_003937.]: Samplename: 62.5 Root: 64.276 --> Root in between the borders! Added to results. Hyperbolic solved: 71.7670214785718 [20250519_003937.]: Samplename: 75 Root: 71.767 --> Root in between the borders! Added to results. Hyperbolic solved: 82.5599568822802 [20250519_003937.]: Samplename: 87.5 Root: 82.56 --> Root in between the borders! Added to results. Hyperbolic solved: 109.655967198106 [20250519_003937.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 109.656 --> '100 < root < 110' --> substitute 100 [20250519_003938.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 6.96411772281142 [20250519_003938.]: Samplename: 0 Root: 6.964 --> Root in between the borders! Added to results. Hyperbolic solved: 12.4462106087166 [20250519_003938.]: Samplename: 12.5 Root: 12.446 --> Root in between the borders! Added to results. Hyperbolic solved: 22.9738427992134 [20250519_003938.]: Samplename: 25 Root: 22.974 --> Root in between the borders! Added to results. Hyperbolic solved: 34.1311793989123 [20250519_003938.]: Samplename: 37.5 Root: 34.131 --> Root in between the borders! Added to results. Hyperbolic solved: 46.7456955019114 [20250519_003938.]: Samplename: 50 Root: 46.746 --> Root in between the borders! Added to results. Hyperbolic solved: 58.6021949543013 [20250519_003938.]: Samplename: 62.5 Root: 58.602 --> Root in between the borders! Added to results. Hyperbolic solved: 74.5100864344263 [20250519_003938.]: Samplename: 75 Root: 74.51 --> Root in between the borders! Added to results. Hyperbolic solved: 86.8549692943716 [20250519_003938.]: Samplename: 87.5 Root: 86.855 --> Root in between the borders! Added to results. Hyperbolic solved: 106.771715286513 [20250519_003938.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 106.772 --> '100 < root < 110' --> substitute 100 [20250519_003938.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 6.82248549455073 [20250519_003938.]: Samplename: 0 Root: 6.822 --> Root in between the borders! Added to results. Hyperbolic solved: 14.2907258003629 [20250519_003938.]: Samplename: 12.5 Root: 14.291 --> Root in between the borders! Added to results. Hyperbolic solved: 24.7343949718638 [20250519_003938.]: Samplename: 25 Root: 24.734 --> Root in between the borders! Added to results. Hyperbolic solved: 31.7110486196861 [20250519_003938.]: Samplename: 37.5 Root: 31.711 --> Root in between the borders! Added to results. Hyperbolic solved: 44.7745870553137 [20250519_003938.]: Samplename: 50 Root: 44.775 --> Root in between the borders! Added to results. Hyperbolic solved: 60.1336283719223 [20250519_003938.]: Samplename: 62.5 Root: 60.134 --> Root in between the borders! Added to results. Hyperbolic solved: 70.3292384995337 [20250519_003938.]: Samplename: 75 Root: 70.329 --> Root in between the borders! Added to results. Hyperbolic solved: 88.2655480529845 [20250519_003938.]: Samplename: 87.5 Root: 88.266 --> Root in between the borders! Added to results. Hyperbolic solved: 108.938360440145 [20250519_003938.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 108.938 --> '100 < root < 110' --> substitute 100 [20250519_003938.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 6.75841824784972 [20250519_003938.]: Samplename: 0 Root: 6.758 --> Root in between the borders! Added to results. Hyperbolic solved: 13.6258602138932 [20250519_003938.]: Samplename: 12.5 Root: 13.626 --> Root in between the borders! Added to results. Hyperbolic solved: 23.2672219789518 [20250519_003938.]: Samplename: 25 Root: 23.267 --> Root in between the borders! Added to results. Hyperbolic solved: 33.4287658578254 [20250519_003938.]: Samplename: 37.5 Root: 33.429 --> Root in between the borders! Added to results. Hyperbolic solved: 47.5236851621158 [20250519_003938.]: Samplename: 50 Root: 47.524 --> Root in between the borders! Added to results. Hyperbolic solved: 60.5558050598499 [20250519_003938.]: Samplename: 62.5 Root: 60.556 --> Root in between the borders! Added to results. Hyperbolic solved: 72.3896034879297 [20250519_003938.]: Samplename: 75 Root: 72.39 --> Root in between the borders! Added to results. Hyperbolic solved: 77.2628610417933 [20250519_003938.]: Samplename: 87.5 Root: 77.263 --> Root in between the borders! Added to results. Hyperbolic solved: 115.187789384315 [20250519_003938.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20250519_003938.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 5.74253131510683 [20250519_003938.]: Samplename: 0 Root: 5.743 --> Root in between the borders! Added to results. Hyperbolic solved: 10.9055216254216 [20250519_003938.]: Samplename: 12.5 Root: 10.906 --> Root in between the borders! Added to results. Hyperbolic solved: 24.5329419436721 [20250519_003938.]: Samplename: 25 Root: 24.533 --> Root in between the borders! Added to results. Hyperbolic solved: 32.1516426249373 [20250519_003938.]: Samplename: 37.5 Root: 32.152 --> Root in between the borders! Added to results. Hyperbolic solved: 49.0919870496865 [20250519_003938.]: Samplename: 50 Root: 49.092 --> Root in between the borders! Added to results. Hyperbolic solved: 62.3843810658735 [20250519_003938.]: Samplename: 62.5 Root: 62.384 --> Root in between the borders! Added to results. Hyperbolic solved: 77.6235905386846 [20250519_003938.]: Samplename: 75 Root: 77.624 --> Root in between the borders! Added to results. Hyperbolic solved: 80.9968312605936 [20250519_003938.]: Samplename: 87.5 Root: 80.997 --> Root in between the borders! Added to results. Hyperbolic solved: 106.570581137258 [20250519_003938.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 106.571 --> '100 < root < 110' --> substitute 100 [20250519_003938.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 8.66981641228949 [20250519_003938.]: Samplename: 0 Root: 8.67 --> Root in between the borders! Added to results. Hyperbolic solved: 14.9680920996829 [20250519_003938.]: Samplename: 12.5 Root: 14.968 --> Root in between the borders! Added to results. Hyperbolic solved: 24.6496838227806 [20250519_003938.]: Samplename: 25 Root: 24.65 --> Root in between the borders! Added to results. Hyperbolic solved: 31.5444702961411 [20250519_003938.]: Samplename: 37.5 Root: 31.544 --> Root in between the borders! Added to results. Hyperbolic solved: 44.0944303975514 [20250519_003938.]: Samplename: 50 Root: 44.094 --> Root in between the borders! Added to results. Hyperbolic solved: 57.2478940106457 [20250519_003938.]: Samplename: 62.5 Root: 57.248 --> Root in between the borders! Added to results. Hyperbolic solved: 69.1518417879612 [20250519_003938.]: Samplename: 75 Root: 69.152 --> Root in between the borders! Added to results. Hyperbolic solved: 83.8189177560508 [20250519_003938.]: Samplename: 87.5 Root: 83.819 --> Root in between the borders! Added to results. Hyperbolic solved: 115.854857900091 [20250519_003938.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20250519_003938.]: Solving hyperbolic regression for row_means Hyperbolic solved: 6.47354965864506 [20250519_003938.]: Samplename: 0 Root: 6.474 --> Root in between the borders! Added to results. Hyperbolic solved: 13.5375540918194 [20250519_003938.]: Samplename: 12.5 Root: 13.538 --> Root in between the borders! Added to results. Hyperbolic solved: 23.976024251167 [20250519_003938.]: Samplename: 25 Root: 23.976 --> Root in between the borders! Added to results. Hyperbolic solved: 32.9614416466039 [20250519_003938.]: Samplename: 37.5 Root: 32.961 --> Root in between the borders! Added to results. Hyperbolic solved: 45.8185453601259 [20250519_003938.]: Samplename: 50 Root: 45.819 --> Root in between the borders! Added to results. Hyperbolic solved: 60.9295837234236 [20250519_003938.]: Samplename: 62.5 Root: 60.93 --> Root in between the borders! Added to results. Hyperbolic solved: 72.9250251350637 [20250519_003938.]: Samplename: 75 Root: 72.925 --> Root in between the borders! Added to results. Hyperbolic solved: 82.8721280175692 [20250519_003938.]: Samplename: 87.5 Root: 82.872 --> Root in between the borders! Added to results. Hyperbolic solved: 110.506149316569 [20250519_003938.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20250519_003938.]: ### Starting with regression calculations ### [20250519_003938.]: Entered 'regression_type1'-Function [20250519_003938.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)c(6.03337011705089, 14.7663097441095, 23.1900365713551, 33.8197153352119, 46.5156833662696, 60.2074431300908, 71.6105733239506, 80.2235915426204)c(6.03337011705089, 2.2663097441095, 1.8099634286449, 3.6802846647881, 3.4843166337304, 2.2925568699092, 3.3894266760494, 7.2764084573796)c(NA, 18.130477952876, 7.2398537145796, 9.81409243943492, 6.9686332674608, 3.66809099185472, 4.51923556806586, 8.3158953798624) [20250519_003938.]: Logging df_agg: CpG#1 [20250519_003938.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)[20250519_003938.]: c(6.03337011705089, 14.7663097441095, 23.1900365713551, 33.8197153352119, 46.5156833662696, 60.2074431300908, 71.6105733239506, 80.2235915426204)[20250519_003938.]: c(6.03337011705089, 2.2663097441095, 1.8099634286449, 3.6802846647881, 3.4843166337304, 2.2925568699092, 3.3894266760494, 7.2764084573796)[20250519_003938.]: c(NA, 18.130477952876, 7.2398537145796, 9.81409243943492, 6.9686332674608, 3.66809099185472, 4.51923556806586, 8.3158953798624) [20250519_003938.]: Entered 'hyperbolic_regression'-Function [20250519_003938.]: 'hyperbolic_regression': minmax = FALSE [20250519_003938.]: Entered 'cubic_regression'-Function [20250519_003938.]: 'cubic_regression': minmax = FALSE [20250519_003938.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.85513576175294, 13.7133339963707, 24.5830349243156, 31.8277435330554, 43.0277185945539, 63.1097818281222, 73.3036858899805, 84.4808447799272, 100)c(6.85513576175294, 1.2133339963707, 0.416965075684399, 5.6722564669446, 6.9722814054461, 0.609781828122202, 1.6963141100195, 3.0191552200728, 0)c(NA, 9.7066719709656, 1.66786030273759, 15.1260172451856, 13.9445628108922, 0.975650924995523, 2.26175214669267, 3.45046310865463, 0) [20250519_003938.]: Logging df_agg: CpG#2 [20250519_003938.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003938.]: c(6.85513576175294, 13.7133339963707, 24.5830349243156, 31.8277435330554, 43.0277185945539, 63.1097818281222, 73.3036858899805, 84.4808447799272, 100)[20250519_003938.]: c(6.85513576175294, 1.2133339963707, 0.416965075684399, 5.6722564669446, 6.9722814054461, 0.609781828122202, 1.6963141100195, 3.0191552200728, 0)[20250519_003938.]: c(NA, 9.7066719709656, 1.66786030273759, 15.1260172451856, 13.9445628108922, 0.975650924995523, 2.26175214669267, 3.45046310865463, 0) [20250519_003938.]: Entered 'hyperbolic_regression'-Function [20250519_003938.]: 'hyperbolic_regression': minmax = FALSE [20250519_003938.]: Entered 'cubic_regression'-Function [20250519_003938.]: 'cubic_regression': minmax = FALSE [20250519_003938.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.27473119209009, 12.7004989900866, 24.2988465005425, 33.7074476249853, 47.0168800224423, 61.5718102366584, 76.091389954119, 79.6023799465703, 100)c(5.27473119209009, 0.200498990086601, 0.701153499457501, 3.7925523750147, 2.9831199775577, 0.928189763341599, 1.09138995411899, 7.8976200534297, 0)c(NA, 1.60399192069281, 2.80461399783, 10.1134730000392, 5.9662399551154, 1.48510362134656, 1.45518660549199, 9.02585148963395, 0) [20250519_003938.]: Logging df_agg: CpG#3 [20250519_003938.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003938.]: c(5.27473119209009, 12.7004989900866, 24.2988465005425, 33.7074476249853, 47.0168800224423, 61.5718102366584, 76.091389954119, 79.6023799465703, 100)[20250519_003938.]: c(5.27473119209009, 0.200498990086601, 0.701153499457501, 3.7925523750147, 2.9831199775577, 0.928189763341599, 1.09138995411899, 7.8976200534297, 0)[20250519_003938.]: c(NA, 1.60399192069281, 2.80461399783, 10.1134730000392, 5.9662399551154, 1.48510362134656, 1.45518660549199, 9.02585148963395, 0) [20250519_003938.]: Entered 'hyperbolic_regression'-Function [20250519_003938.]: 'hyperbolic_regression': minmax = FALSE [20250519_003938.]: Entered 'cubic_regression'-Function [20250519_003938.]: 'cubic_regression': minmax = FALSE [20250519_003938.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.07584235183922, 14.2021272601476, 23.203637284743, 34.7960863609446, 44.4638162018573, 64.2755507391393, 71.7670214785718, 82.5599568822802, 100)c(5.07584235183922, 1.7021272601476, 1.796362715257, 2.7039136390554, 5.5361837981427, 1.77555073913931, 3.23297852142819, 4.9400431177198, 0)c(NA, 13.6170180811808, 7.185450861028, 7.2104363708144, 11.0723675962854, 2.84088118262289, 4.31063802857093, 5.64576356310834, 0) [20250519_003938.]: Logging df_agg: CpG#4 [20250519_003938.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003938.]: c(5.07584235183922, 14.2021272601476, 23.203637284743, 34.7960863609446, 44.4638162018573, 64.2755507391393, 71.7670214785718, 82.5599568822802, 100)[20250519_003938.]: c(5.07584235183922, 1.7021272601476, 1.796362715257, 2.7039136390554, 5.5361837981427, 1.77555073913931, 3.23297852142819, 4.9400431177198, 0)[20250519_003938.]: c(NA, 13.6170180811808, 7.185450861028, 7.2104363708144, 11.0723675962854, 2.84088118262289, 4.31063802857093, 5.64576356310834, 0) [20250519_003938.]: Entered 'hyperbolic_regression'-Function [20250519_003938.]: 'hyperbolic_regression': minmax = FALSE [20250519_003938.]: Entered 'cubic_regression'-Function [20250519_003938.]: 'cubic_regression': minmax = FALSE [20250519_003938.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.96411772281142, 12.4462106087166, 22.9738427992134, 34.1311793989123, 46.7456955019114, 58.6021949543013, 74.5100864344263, 86.8549692943716, 100)c(6.96411772281142, 0.0537893912834004, 2.0261572007866, 3.3688206010877, 3.2543044980886, 3.8978050456987, 0.489913565573701, 0.6450307056284, 0)c(NA, 0.430315130267203, 8.1046288031464, 8.98352160290053, 6.5086089961772, 6.23648807311793, 0.653218087431602, 0.7371779492896, 0) [20250519_003938.]: Logging df_agg: CpG#5 [20250519_003938.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003938.]: c(6.96411772281142, 12.4462106087166, 22.9738427992134, 34.1311793989123, 46.7456955019114, 58.6021949543013, 74.5100864344263, 86.8549692943716, 100)[20250519_003938.]: c(6.96411772281142, 0.0537893912834004, 2.0261572007866, 3.3688206010877, 3.2543044980886, 3.8978050456987, 0.489913565573701, 0.6450307056284, 0)[20250519_003938.]: c(NA, 0.430315130267203, 8.1046288031464, 8.98352160290053, 6.5086089961772, 6.23648807311793, 0.653218087431602, 0.7371779492896, 0) [20250519_003938.]: Entered 'hyperbolic_regression'-Function [20250519_003938.]: 'hyperbolic_regression': minmax = FALSE [20250519_003938.]: Entered 'cubic_regression'-Function [20250519_003938.]: 'cubic_regression': minmax = FALSE [20250519_003938.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.82248549455073, 14.2907258003629, 24.7343949718638, 31.7110486196861, 44.7745870553137, 60.1336283719223, 70.3292384995337, 88.2655480529845, 100)c(6.82248549455073, 1.7907258003629, 0.2656050281362, 5.7889513803139, 5.2254129446863, 2.3663716280777, 4.6707615004663, 0.765548052984499, 0)c(NA, 14.3258064029032, 1.0624201125448, 15.4372036808371, 10.4508258893726, 3.78619460492432, 6.22768200062173, 0.874912060553714, 0) [20250519_003938.]: Logging df_agg: CpG#6 [20250519_003938.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003938.]: c(6.82248549455073, 14.2907258003629, 24.7343949718638, 31.7110486196861, 44.7745870553137, 60.1336283719223, 70.3292384995337, 88.2655480529845, 100)[20250519_003938.]: c(6.82248549455073, 1.7907258003629, 0.2656050281362, 5.7889513803139, 5.2254129446863, 2.3663716280777, 4.6707615004663, 0.765548052984499, 0)[20250519_003938.]: c(NA, 14.3258064029032, 1.0624201125448, 15.4372036808371, 10.4508258893726, 3.78619460492432, 6.22768200062173, 0.874912060553714, 0) [20250519_003938.]: Entered 'hyperbolic_regression'-Function [20250519_003938.]: 'hyperbolic_regression': minmax = FALSE [20250519_003938.]: Entered 'cubic_regression'-Function [20250519_003938.]: 'cubic_regression': minmax = FALSE [20250519_003938.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)c(6.75841824784972, 13.6258602138932, 23.2672219789518, 33.4287658578254, 47.5236851621158, 60.5558050598499, 72.3896034879297, 77.2628610417933)c(6.75841824784972, 1.1258602138932, 1.7327780210482, 4.0712341421746, 2.4763148378842, 1.9441949401501, 2.6103965120703, 10.2371389582067)c(NA, 9.0068817111456, 6.9311120841928, 10.8566243791323, 4.95262967576841, 3.11071190424016, 3.48052868276039, 11.6995873808077) [20250519_003938.]: Logging df_agg: CpG#7 [20250519_003938.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)[20250519_003938.]: c(6.75841824784972, 13.6258602138932, 23.2672219789518, 33.4287658578254, 47.5236851621158, 60.5558050598499, 72.3896034879297, 77.2628610417933)[20250519_003938.]: c(6.75841824784972, 1.1258602138932, 1.7327780210482, 4.0712341421746, 2.4763148378842, 1.9441949401501, 2.6103965120703, 10.2371389582067)[20250519_003938.]: c(NA, 9.0068817111456, 6.9311120841928, 10.8566243791323, 4.95262967576841, 3.11071190424016, 3.48052868276039, 11.6995873808077) [20250519_003938.]: Entered 'hyperbolic_regression'-Function [20250519_003938.]: 'hyperbolic_regression': minmax = FALSE [20250519_003938.]: Entered 'cubic_regression'-Function [20250519_003938.]: 'cubic_regression': minmax = FALSE [20250519_003938.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.74253131510683, 10.9055216254216, 24.5329419436721, 32.1516426249373, 49.0919870496865, 62.3843810658735, 77.6235905386846, 80.9968312605936, 100)c(5.74253131510683, 1.5944783745784, 0.4670580563279, 5.3483573750627, 0.908012950313498, 0.115618934126502, 2.6235905386846, 6.50316873940641, 0)c(NA, 12.7558269966272, 1.8682322253116, 14.2622863335005, 1.816025900627, 0.184990294602403, 3.49812071824613, 7.43219284503589, 0) [20250519_003938.]: Logging df_agg: CpG#8 [20250519_003938.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003938.]: c(5.74253131510683, 10.9055216254216, 24.5329419436721, 32.1516426249373, 49.0919870496865, 62.3843810658735, 77.6235905386846, 80.9968312605936, 100)[20250519_003938.]: c(5.74253131510683, 1.5944783745784, 0.4670580563279, 5.3483573750627, 0.908012950313498, 0.115618934126502, 2.6235905386846, 6.50316873940641, 0)[20250519_003938.]: c(NA, 12.7558269966272, 1.8682322253116, 14.2622863335005, 1.816025900627, 0.184990294602403, 3.49812071824613, 7.43219284503589, 0) [20250519_003938.]: Entered 'hyperbolic_regression'-Function [20250519_003938.]: 'hyperbolic_regression': minmax = FALSE [20250519_003938.]: Entered 'cubic_regression'-Function [20250519_003938.]: 'cubic_regression': minmax = FALSE [20250519_003938.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)c(8.66981641228949, 14.9680920996829, 24.6496838227806, 31.5444702961411, 44.0944303975514, 57.2478940106457, 69.1518417879612, 83.8189177560508)c(8.66981641228949, 2.4680920996829, 0.350316177219401, 5.9555297038589, 5.9055696024486, 5.2521059893543, 5.8481582120388, 3.68108224394921)c(NA, 19.7447367974632, 1.4012647088776, 15.8814125436237, 11.8111392048972, 8.40336958296688, 7.7975442827184, 4.20695113594195) [20250519_003938.]: Logging df_agg: CpG#9 [20250519_003938.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)[20250519_003938.]: c(8.66981641228949, 14.9680920996829, 24.6496838227806, 31.5444702961411, 44.0944303975514, 57.2478940106457, 69.1518417879612, 83.8189177560508)[20250519_003938.]: c(8.66981641228949, 2.4680920996829, 0.350316177219401, 5.9555297038589, 5.9055696024486, 5.2521059893543, 5.8481582120388, 3.68108224394921)[20250519_003938.]: c(NA, 19.7447367974632, 1.4012647088776, 15.8814125436237, 11.8111392048972, 8.40336958296688, 7.7975442827184, 4.20695113594195) [20250519_003938.]: Entered 'hyperbolic_regression'-Function [20250519_003938.]: 'hyperbolic_regression': minmax = FALSE [20250519_003938.]: Entered 'cubic_regression'-Function [20250519_003938.]: 'cubic_regression': minmax = FALSE [20250519_003938.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)c(6.47354965864506, 13.5375540918194, 23.976024251167, 32.9614416466039, 45.8185453601259, 60.9295837234236, 72.9250251350637, 82.8721280175692)c(6.47354965864506, 1.0375540918194, 1.023975748833, 4.5385583533961, 4.1814546398741, 1.5704162765764, 2.07497486493629, 4.6278719824308)c(NA, 8.3004327345552, 4.095902995332, 12.1028222757229, 8.36290927974819, 2.51266604252224, 2.76663315324839, 5.28899655134949) [20250519_003938.]: Logging df_agg: row_means [20250519_003938.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)[20250519_003938.]: c(6.47354965864506, 13.5375540918194, 23.976024251167, 32.9614416466039, 45.8185453601259, 60.9295837234236, 72.9250251350637, 82.8721280175692)[20250519_003938.]: c(6.47354965864506, 1.0375540918194, 1.023975748833, 4.5385583533961, 4.1814546398741, 1.5704162765764, 2.07497486493629, 4.6278719824308)[20250519_003938.]: c(NA, 8.3004327345552, 4.095902995332, 12.1028222757229, 8.36290927974819, 2.51266604252224, 2.76663315324839, 5.28899655134949) [20250519_003938.]: Entered 'hyperbolic_regression'-Function [20250519_003938.]: 'hyperbolic_regression': minmax = FALSE [20250519_003938.]: Entered 'cubic_regression'-Function [20250519_003938.]: 'cubic_regression': minmax = FALSE [20250519_003939.]: ### Starting with plotting ### [20250519_003939.]: Creating BiasCorrected (hyperbolic) plot No. 1 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG1_corrected_h.png [20250519_003939.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = 170413234.729972 , a = 44157086.707734 , b = 170413234.729972 , d = 49999790.9017601 [20250519_003939.]: # CpG-site: CpG#1 Cubic: Using a = -8.02902858014277e-05 , b = 0.0119973831380665 , c = 0.410459267079284 , d = 6.65096545514267 [20250519_003941.]: Creating BiasCorrected (hyperbolic) plot No. 2 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG2_corrected_h.png [20250519_003941.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = 63519198.3583519 , a = 42702123.5062977 , b = 63519198.3583519 , d = 44879882.4102916 [20250519_003941.]: # CpG-site: CpG#2 Cubic: Using a = -4.57917988885898e-05 , b = 0.00978975326683099 , c = 0.400365566888902 , d = 7.17765330717532 [20250519_003942.]: Creating BiasCorrected (hyperbolic) plot No. 3 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG3_corrected_h.png [20250519_003942.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = 60249784.2121432 , a = 37717764.1245422 , b = 60249784.2121432 , d = 39783429.2823954 [20250519_003942.]: # CpG-site: CpG#3 Cubic: Using a = -2.8548625146914e-05 , b = 0.00587956303749884 , c = 0.626869802626483 , d = 4.78057876315863 [20250519_003943.]: Creating BiasCorrected (hyperbolic) plot No. 4 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG4_corrected_h.png [20250519_003943.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = 66787089.0359508 , a = 42175722.8129271 , b = 66787089.0359508 , d = 44465523.7860812 [20250519_003943.]: # CpG-site: CpG#4 Cubic: Using a = -1.99135996570308e-05 , b = 0.00496936895290719 , c = 0.637632604471187 , d = 5.04633670060351 [20250519_003945.]: Creating BiasCorrected (hyperbolic) plot No. 5 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG5_corrected_h.png [20250519_003945.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = 57501729.1236441 , a = 52618093.853152 , b = 57501729.1236441 , d = 54589535.3828564 [20250519_003945.]: # CpG-site: CpG#5 Cubic: Using a = -4.67907948499236e-05 , b = 0.00978122250124343 , c = 0.42296364991001 , d = 6.61748738798833 [20250519_003946.]: Creating BiasCorrected (hyperbolic) plot No. 6 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG6_corrected_h.png [20250519_003946.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = 62067256.5784193 , a = 42512767.1502548 , b = 62067256.5784193 , d = 44640742.1604705 [20250519_003946.]: # CpG-site: CpG#6 Cubic: Using a = -2.66665530276184e-05 , b = 0.00740474965950072 , c = 0.461019694351388 , d = 7.23067904029629 [20250519_003948.]: Creating BiasCorrected (hyperbolic) plot No. 7 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG7_corrected_h.png [20250519_003948.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = 162158036.04427 , a = 37148305.7394653 , b = 162158036.04427 , d = 42707972.2066412 [20250519_003948.]: # CpG-site: CpG#7 Cubic: Using a = -0.000133937721266082 , b = 0.0182624909642956 , c = 0.234528921247686 , d = 7.29090555727988 [20250519_003949.]: Creating BiasCorrected (hyperbolic) plot No. 8 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG8_corrected_h.png [20250519_003949.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = 54899408.7962443 , a = 51832403.6152701 , b = 54899408.7962443 , d = 53714624.7099072 [20250519_003949.]: # CpG-site: CpG#8 Cubic: Using a = -5.74524696829791e-05 , b = 0.00998862677056794 , c = 0.502915955578275 , d = 4.90625922077111 [20250519_003951.]: Creating BiasCorrected (hyperbolic) plot No. 9 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG9_corrected_h.png [20250519_003951.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = 170227220.836014 , a = 37110575.5394583 , b = 170227220.836014 , d = 42946902.7624502 [20250519_003951.]: # CpG-site: CpG#9 Cubic: Using a = -1.38503636743099e-05 , b = 0.00591895875024497 , c = 0.445742549227169 , d = 8.73330013353405 [20250519_003952.]: Creating BiasCorrected (hyperbolic) plot No. 10 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_rowmeans_corrected_h.png [20250519_003952.]: # CpG-site: row_means Hyperbolic: Using bias_weight = 131846991.813373 , a = 45678337.0783695 , b = 131846991.813373 , d = 50198768.4229237 [20250519_003952.]: # CpG-site: row_means Cubic: Using a = -7.67916401132796e-05 , b = 0.0124890194887903 , c = 0.369098504101218 , d = 6.83740913189534 [20250519_003953.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG1_corrected_h.png [20250519_003955.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG2_corrected_h.png [20250519_003956.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG3_corrected_h.png [20250519_003957.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG4_corrected_h.png [20250519_003959.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG5_corrected_h.png [20250519_004000.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG6_corrected_h.png [20250519_004001.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG7_corrected_h.png [20250519_004003.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG8_corrected_h.png [20250519_004004.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG9_corrected_h.png [20250519_004005.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_rowmeans_corrected_h.png [20250519_004007.]: Entered 'solving_equations'-Function [20250519_004007.]: Solving cubic regression for CpG#1 Coefficients: -1.03617340067344Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004007.]: Samplename: 0 Root: 1.334 --> Root in between the borders! Added to results. Coefficients: -8.34150673400678Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004007.]: Samplename: 12.5 Root: 11.446 --> Root in between the borders! Added to results. Coefficients: -15.3881734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004007.]: Samplename: 25 Root: 22.228 --> Root in between the borders! Added to results. Coefficients: -24.2801734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004007.]: Samplename: 37.5 Root: 36.374 --> Root in between the borders! Added to results. Coefficients: -34.9006734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004007.]: Samplename: 50 Root: 52.044 --> Root in between the borders! Added to results. Coefficients: -46.3541734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004007.]: Samplename: 62.5 Root: 66.144 --> Root in between the borders! Added to results. Coefficients: -55.8931734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004007.]: Samplename: 75 Root: 75.864 --> Root in between the borders! Added to results. Coefficients: -63.0981734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004007.]: Samplename: 87.5 Root: 82.254 --> Root in between the borders! Added to results. Coefficients: -91.0461734006735Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004007.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.877 --> '100 < root < 110' --> substitute 100 [20250519_004007.]: Solving cubic regression for CpG#2 Coefficients: -0.283329966329966Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004007.]: Samplename: 0 Root: 0.549 --> Root in between the borders! Added to results. Coefficients: -6.33999663299663Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004007.]: Samplename: 12.5 Root: 11.533 --> Root in between the borders! Added to results. Coefficients: -15.93932996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004007.]: Samplename: 25 Root: 26.628 --> Root in between the borders! Added to results. Coefficients: -22.33732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004007.]: Samplename: 37.5 Root: 35.509 --> Root in between the borders! Added to results. Coefficients: -32.22832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004007.]: Samplename: 50 Root: 47.851 --> Root in between the borders! Added to results. Coefficients: -49.96332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004007.]: Samplename: 62.5 Root: 66.893 --> Root in between the borders! Added to results. Coefficients: -58.96582996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004007.]: Samplename: 75 Root: 75.431 --> Root in between the borders! Added to results. Coefficients: -68.8366632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004007.]: Samplename: 87.5 Root: 84.118 --> Root in between the borders! Added to results. Coefficients: -90.57732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004007.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.287 --> '100 < root < 110' --> substitute 100 [20250519_004007.]: Solving cubic regression for CpG#3 Coefficients: -0.90294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004007.]: Samplename: 0 Root: 1.441 --> Root in between the borders! Added to results. Coefficients: -6.57294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004007.]: Samplename: 12.5 Root: 10.568 --> Root in between the borders! Added to results. Coefficients: -15.4289478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004007.]: Samplename: 25 Root: 24.796 --> Root in between the borders! Added to results. Coefficients: -22.6129478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004007.]: Samplename: 37.5 Root: 35.952 --> Root in between the borders! Added to results. Coefficients: -32.7754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004007.]: Samplename: 50 Root: 50.684 --> Root in between the borders! Added to results. Coefficients: -43.8889478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004007.]: Samplename: 62.5 Root: 65.142 --> Root in between the borders! Added to results. Coefficients: -54.9754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004007.]: Samplename: 75 Root: 77.905 --> Root in between the borders! Added to results. Coefficients: -57.6562811447812Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004007.]: Samplename: 87.5 Root: 80.767 --> Root in between the borders! Added to results. Coefficients: -80.6649478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004007.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.38 --> '100 < root < 110' --> substitute 100 [20250519_004007.]: Solving cubic regression for CpG#4 Coefficients: -0.597449494949524Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004007.]: Samplename: 0 Root: 0.858 --> Root in between the borders! Added to results. Coefficients: -8.25278282828286Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004007.]: Samplename: 12.5 Root: 12.086 --> Root in between the borders! Added to results. Coefficients: -15.8034494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004007.]: Samplename: 25 Root: 23.316 --> Root in between the borders! Added to results. Coefficients: -25.5274494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004007.]: Samplename: 37.5 Root: 37.383 --> Root in between the borders! Added to results. Coefficients: -33.6369494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004007.]: Samplename: 50 Root: 48.353 --> Root in between the borders! Added to results. Coefficients: -50.2554494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004007.]: Samplename: 62.5 Root: 68.082 --> Root in between the borders! Added to results. Coefficients: -56.5394494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004007.]: Samplename: 75 Root: 74.615 --> Root in between the borders! Added to results. Coefficients: -65.5927828282829Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004007.]: Samplename: 87.5 Root: 83.254 --> Root in between the borders! Added to results. Coefficients: -88.3214494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004007.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.715 --> '100 < root < 110' --> substitute 100 [20250519_004007.]: Solving cubic regression for CpG#5 Coefficients: -0.623961279461278Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004007.]: Samplename: 0 Root: 1.458 --> Root in between the borders! Added to results. Coefficients: -4.76796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004007.]: Samplename: 12.5 Root: 10.347 --> Root in between the borders! Added to results. Coefficients: -12.7259612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004007.]: Samplename: 25 Root: 24.815 --> Root in between the borders! Added to results. Coefficients: -21.1599612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004007.]: Samplename: 37.5 Root: 37.902 --> Root in between the borders! Added to results. Coefficients: -30.6954612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004007.]: Samplename: 50 Root: 50.977 --> Root in between the borders! Added to results. Coefficients: -39.6579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004007.]: Samplename: 62.5 Root: 62.126 --> Root in between the borders! Added to results. Coefficients: -51.6829612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004007.]: Samplename: 75 Root: 75.852 --> Root in between the borders! Added to results. Coefficients: -61.0146279461279Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004007.]: Samplename: 87.5 Root: 85.767 --> Root in between the borders! Added to results. Coefficients: -76.0699612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004007.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.743 --> '100 < root < 110' --> substitute 100 [20250519_004007.]: Solving cubic regression for CpG#6 Coefficients: -0.196072390572403Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004007.]: Samplename: 0 Root: 0.349 --> Root in between the borders! Added to results. Coefficients: -6.73873905723907Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004007.]: Samplename: 12.5 Root: 11.718 --> Root in between the borders! Added to results. Coefficients: -15.8880723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004007.]: Samplename: 25 Root: 26.396 --> Root in between the borders! Added to results. Coefficients: -22.0000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004007.]: Samplename: 37.5 Root: 35.301 --> Root in between the borders! Added to results. Coefficients: -33.4445723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004007.]: Samplename: 50 Root: 50.134 --> Root in between the borders! Added to results. Coefficients: -46.9000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004007.]: Samplename: 62.5 Root: 64.993 --> Root in between the borders! Added to results. Coefficients: -55.8320723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004007.]: Samplename: 75 Root: 73.639 --> Root in between the borders! Added to results. Coefficients: -71.5454057239057Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004007.]: Samplename: 87.5 Root: 87.043 --> Root in between the borders! Added to results. Coefficients: -89.6560723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004007.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.329 --> '100 < root < 110' --> substitute 100 [20250519_004007.]: Solving cubic regression for CpG#7 Coefficients: -1.21495454545456Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004007.]: Samplename: 0 Root: 2.13 --> Root in between the borders! Added to results. Coefficients: -5.39562121212123Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004007.]: Samplename: 12.5 Root: 9.973 --> Root in between the borders! Added to results. Coefficients: -11.2649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004007.]: Samplename: 25 Root: 22.206 --> Root in between the borders! Added to results. Coefficients: -17.4509545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004007.]: Samplename: 37.5 Root: 35.814 --> Root in between the borders! Added to results. Coefficients: -26.0314545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004007.]: Samplename: 50 Root: 53.28 --> Root in between the borders! Added to results. Coefficients: -33.9649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004007.]: Samplename: 62.5 Root: 66.598 --> Root in between the borders! Added to results. Coefficients: -41.1689545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004007.]: Samplename: 75 Root: 76.575 --> Root in between the borders! Added to results. Coefficients: -44.1356212121212Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004007.]: Samplename: 87.5 Root: 80.219 --> Root in between the borders! Added to results. Coefficients: -67.2229545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004007.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.506 --> '100 < root < 110' --> substitute 100 [20250519_004007.]: Solving cubic regression for CpG#8 Coefficients: -1.09618518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004007.]: Samplename: 0 Root: 2.016 --> Root in between the borders! Added to results. Coefficients: -5.44685185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004007.]: Samplename: 12.5 Root: 9.458 --> Root in between the borders! Added to results. Coefficients: -16.9301851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004007.]: Samplename: 25 Root: 26.35 --> Root in between the borders! Added to results. Coefficients: -23.3501851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004007.]: Samplename: 37.5 Root: 34.728 --> Root in between the borders! Added to results. Coefficients: -37.6251851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004007.]: Samplename: 50 Root: 51.781 --> Root in between the borders! Added to results. Coefficients: -48.8261851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004007.]: Samplename: 62.5 Root: 64.192 --> Root in between the borders! Added to results. Coefficients: -61.6676851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004007.]: Samplename: 75 Root: 77.804 --> Root in between the borders! Added to results. Coefficients: -64.5101851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004007.]: Samplename: 87.5 Root: 80.758 --> Root in between the borders! Added to results. Coefficients: -86.0601851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004007.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.834 --> '100 < root < 110' --> substitute 100 [20250519_004007.]: Solving cubic regression for CpG#9 Coefficients: -0.989865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004007.]: Samplename: 0 Root: 1.475 --> Root in between the borders! Added to results. Coefficients: -6.39586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004007.]: Samplename: 12.5 Root: 10.12 --> Root in between the borders! Added to results. Coefficients: -14.7058653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004007.]: Samplename: 25 Root: 24.844 --> Root in between the borders! Added to results. Coefficients: -20.6238653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004007.]: Samplename: 37.5 Root: 35.327 --> Root in between the borders! Added to results. Coefficients: -31.3958653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004007.]: Samplename: 50 Root: 51.855 --> Root in between the borders! Added to results. Coefficients: -42.6858653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004007.]: Samplename: 62.5 Root: 65.265 --> Root in between the borders! Added to results. Coefficients: -52.9033653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004007.]: Samplename: 75 Root: 74.915 --> Root in between the borders! Added to results. Coefficients: -65.492531986532Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004007.]: Samplename: 87.5 Root: 84.67 --> Root in between the borders! Added to results. Coefficients: -92.9898653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004007.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.082 --> '100 < root < 110' --> substitute 100 [20250519_004007.]: Solving cubic regression for row_means Coefficients: -0.771215488215478Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_004007.]: Samplename: 0 Root: 1.287 --> Root in between the borders! Added to results. Coefficients: -6.47247474747474Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_004007.]: Samplename: 12.5 Root: 10.847 --> Root in between the borders! Added to results. Coefficients: -14.8972154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_004007.]: Samplename: 25 Root: 24.737 --> Root in between the borders! Added to results. Coefficients: -22.1492154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_004007.]: Samplename: 37.5 Root: 36.02 --> Root in between the borders! Added to results. Coefficients: -32.5259932659933Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_004007.]: Samplename: 50 Root: 50.639 --> Root in between the borders! Added to results. Coefficients: -44.7218821548821Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_004007.]: Samplename: 62.5 Root: 65.497 --> Root in between the borders! Added to results. Coefficients: -54.4032154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_004007.]: Samplename: 75 Root: 75.751 --> Root in between the borders! Added to results. Coefficients: -62.4313636363636Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_004007.]: Samplename: 87.5 Root: 83.403 --> Root in between the borders! Added to results. Coefficients: -84.7343265993266Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_004007.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.573 --> '100 < root < 110' --> substitute 100 [20250519_004007.]: ### Starting with regression calculations ### [20250519_004007.]: Entered 'regression_type1'-Function [20250519_004007.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) [20250519_004007.]: Logging df_agg: CpG#1 [20250519_004007.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004007.]: c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)[20250519_004007.]: c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)[20250519_004007.]: c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) [20250519_004007.]: Entered 'hyperbolic_regression'-Function [20250519_004007.]: 'hyperbolic_regression': minmax = FALSE [20250519_004007.]: Entered 'cubic_regression'-Function [20250519_004007.]: 'cubic_regression': minmax = FALSE [20250519_004007.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) [20250519_004007.]: Logging df_agg: CpG#2 [20250519_004007.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004007.]: c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)[20250519_004007.]: c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)[20250519_004007.]: c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) [20250519_004007.]: Entered 'hyperbolic_regression'-Function [20250519_004007.]: 'hyperbolic_regression': minmax = FALSE [20250519_004007.]: Entered 'cubic_regression'-Function [20250519_004007.]: 'cubic_regression': minmax = FALSE [20250519_004007.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) [20250519_004007.]: Logging df_agg: CpG#3 [20250519_004007.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004007.]: c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)[20250519_004007.]: c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)[20250519_004007.]: c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) [20250519_004007.]: Entered 'hyperbolic_regression'-Function [20250519_004007.]: 'hyperbolic_regression': minmax = FALSE [20250519_004007.]: Entered 'cubic_regression'-Function [20250519_004007.]: 'cubic_regression': minmax = FALSE [20250519_004007.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) [20250519_004007.]: Logging df_agg: CpG#4 [20250519_004007.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004007.]: c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)[20250519_004007.]: c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)[20250519_004007.]: c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) [20250519_004007.]: Entered 'hyperbolic_regression'-Function [20250519_004007.]: 'hyperbolic_regression': minmax = FALSE [20250519_004007.]: Entered 'cubic_regression'-Function [20250519_004007.]: 'cubic_regression': minmax = FALSE [20250519_004007.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) [20250519_004007.]: Logging df_agg: CpG#5 [20250519_004007.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004007.]: c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)[20250519_004007.]: c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)[20250519_004007.]: c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) [20250519_004007.]: Entered 'hyperbolic_regression'-Function [20250519_004007.]: 'hyperbolic_regression': minmax = FALSE [20250519_004007.]: Entered 'cubic_regression'-Function [20250519_004007.]: 'cubic_regression': minmax = FALSE [20250519_004007.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) [20250519_004007.]: Logging df_agg: CpG#6 [20250519_004007.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004007.]: c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)[20250519_004007.]: c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)[20250519_004007.]: c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) [20250519_004007.]: Entered 'hyperbolic_regression'-Function [20250519_004007.]: 'hyperbolic_regression': minmax = FALSE [20250519_004007.]: Entered 'cubic_regression'-Function [20250519_004007.]: 'cubic_regression': minmax = FALSE [20250519_004007.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) [20250519_004007.]: Logging df_agg: CpG#7 [20250519_004007.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004007.]: c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)[20250519_004007.]: c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)[20250519_004007.]: c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) [20250519_004007.]: Entered 'hyperbolic_regression'-Function [20250519_004007.]: 'hyperbolic_regression': minmax = FALSE [20250519_004007.]: Entered 'cubic_regression'-Function [20250519_004007.]: 'cubic_regression': minmax = FALSE [20250519_004007.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) [20250519_004007.]: Logging df_agg: CpG#8 [20250519_004007.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004007.]: c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)[20250519_004007.]: c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)[20250519_004007.]: c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) [20250519_004007.]: Entered 'hyperbolic_regression'-Function [20250519_004007.]: 'hyperbolic_regression': minmax = FALSE [20250519_004007.]: Entered 'cubic_regression'-Function [20250519_004007.]: 'cubic_regression': minmax = FALSE [20250519_004007.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) [20250519_004007.]: Logging df_agg: CpG#9 [20250519_004007.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004007.]: c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)[20250519_004007.]: c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)[20250519_004007.]: c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) [20250519_004007.]: Entered 'hyperbolic_regression'-Function [20250519_004007.]: 'hyperbolic_regression': minmax = FALSE [20250519_004007.]: Entered 'cubic_regression'-Function [20250519_004007.]: 'cubic_regression': minmax = FALSE [20250519_004007.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) [20250519_004007.]: Logging df_agg: row_means [20250519_004007.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004007.]: c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)[20250519_004007.]: c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)[20250519_004007.]: c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) [20250519_004008.]: Entered 'hyperbolic_regression'-Function [20250519_004008.]: 'hyperbolic_regression': minmax = FALSE [20250519_004008.]: Entered 'cubic_regression'-Function [20250519_004008.]: 'cubic_regression': minmax = FALSE [20250519_004008.]: ### Starting with plotting ### [20250519_004008.]: Creating BiasCorrected (cubic) plot No. 1 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG1_corrected_c.png [20250519_004008.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -1164.7742156272 , a = 2039.5018241568 , b = -1164.7742156272 , d = 1955.83895682798 [20250519_004008.]: # CpG-site: CpG#1 Cubic: Using a = -3.42821196240366e-05 , b = 0.00462173155013798 , c = 0.850326258865826 , d = 0.500073874211125 [20250519_004010.]: Creating BiasCorrected (cubic) plot No. 2 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG2_corrected_c.png [20250519_004010.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = -464.155645361511 , a = 4988.81163391504 , b = -464.155645361511 , d = 4925.28408403839 [20250519_004010.]: # CpG-site: CpG#2 Cubic: Using a = -1.40811796906314e-05 , b = 0.00190849431754485 , c = 0.933455870941473 , d = 0.363096210099674 [20250519_004011.]: Creating BiasCorrected (cubic) plot No. 3 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG3_corrected_c.png [20250519_004011.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = -745.602352161431 , a = 2026.54103071808 , b = -745.602352161431 , d = 1953.2909386646 [20250519_004011.]: # CpG-site: CpG#3 Cubic: Using a = -2.78835051857186e-05 , b = 0.00366863707889089 , c = 0.880665171220893 , d = 0.510779308833653 [20250519_004013.]: Creating BiasCorrected (cubic) plot No. 4 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG4_corrected_c.png [20250519_004013.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = -833.06710731958 , a = 2871.12609903594 , b = -833.06710731958 , d = 2796.80085807845 [20250519_004013.]: # CpG-site: CpG#4 Cubic: Using a = -2.30028865643e-05 , b = 0.00309017851940692 , c = 0.897073107777645 , d = 0.443455761841866 [20250519_004014.]: Creating BiasCorrected (cubic) plot No. 5 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG5_corrected_c.png [20250519_004014.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = 221.879400146024 , a = 12476.5710927987 , b = 221.879400146024 , d = 12431.2329555778 [20250519_004014.]: # CpG-site: CpG#5 Cubic: Using a = -1.28632696989134e-05 , b = 0.00184822285299014 , c = 0.931001046631176 , d = 0.437963095185599 [20250519_004015.]: Creating BiasCorrected (cubic) plot No. 6 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG6_corrected_c.png [20250519_004015.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = -700.537394936109 , a = 21298.052707184 , b = -700.537394936109 , d = 21233.4563350159 [20250519_004015.]: # CpG-site: CpG#6 Cubic: Using a = -5.838335151375e-06 , b = 0.000828447163218526 , c = 0.970047764475166 , d = 0.158074701412251 [20250519_004017.]: Creating BiasCorrected (cubic) plot No. 7 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG7_corrected_c.png [20250519_004017.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -1144.91075475684 , a = 1477.92088633595 , b = -1144.91075475684 , d = 1394.70940380214 [20250519_004017.]: # CpG-site: CpG#7 Cubic: Using a = -4.54220535614047e-05 , b = 0.00607993130565131 , c = 0.805411493452389 , d = 0.615125382790254 [20250519_004018.]: Creating BiasCorrected (cubic) plot No. 8 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG8_corrected_c.png [20250519_004018.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = -349.322022819744 , a = 2377.42555416743 , b = -349.322022819744 , d = 2315.25402084122 [20250519_004018.]: # CpG-site: CpG#8 Cubic: Using a = -2.64622306669639e-05 , b = 0.00353621593528015 , c = 0.877916844709074 , d = 0.698684606493051 [20250519_004020.]: Creating BiasCorrected (cubic) plot No. 9 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG9_corrected_c.png [20250519_004020.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -1536.91807795025 , a = 3745.12601816225 , b = -1536.91807795025 , d = 3656.21766932252 [20250519_004020.]: # CpG-site: CpG#9 Cubic: Using a = -2.70976130611725e-05 , b = 0.00378478247182342 , c = 0.871825263051793 , d = 0.455475740974113 [20250519_004021.]: Creating BiasCorrected (cubic) plot No. 10 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_rowmeans_corrected_c.png [20250519_004021.]: # CpG-site: row_means Hyperbolic: Using bias_weight = -820.655299479511 , a = 3198.47118291101 , b = -820.655299479511 , d = 3124.21569118411 [20250519_004021.]: # CpG-site: row_means Cubic: Using a = -2.34328362100009e-05 , b = 0.00319139073176266 , c = 0.891842168032129 , d = 0.493517689787135 [20250519_004023.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG1_corrected_c.png [20250519_004024.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG2_corrected_c.png [20250519_004025.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG3_corrected_c.png [20250519_004027.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG4_corrected_c.png [20250519_004028.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG5_corrected_c.png [20250519_004029.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG6_corrected_c.png [20250519_004031.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG7_corrected_c.png [20250519_004032.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG8_corrected_c.png [20250519_004033.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG9_corrected_c.png [20250519_004035.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_rowmeans_corrected_c.png [20250519_004036.]: Entered 'solving_equations'-Function [20250519_004036.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 77.9602582684049 [20250519_004036.]: Samplename: Sample#1 Root: 77.96 --> Root in between the borders! Added to results. Hyperbolic solved: 27.1420961794087 [20250519_004036.]: Samplename: Sample#10 Root: 27.142 --> Root in between the borders! Added to results. Hyperbolic solved: 36.7640366116659 [20250519_004036.]: Samplename: Sample#2 Root: 36.764 --> Root in between the borders! Added to results. Hyperbolic solved: 51.6231208412737 [20250519_004036.]: Samplename: Sample#3 Root: 51.623 --> Root in between the borders! Added to results. Hyperbolic solved: 12.873561614208 [20250519_004036.]: Samplename: Sample#4 Root: 12.874 --> Root in between the borders! Added to results. Hyperbolic solved: 21.2773642339947 [20250519_004036.]: Samplename: Sample#5 Root: 21.277 --> Root in between the borders! Added to results. Hyperbolic solved: 22.1858835788516 [20250519_004036.]: Samplename: Sample#6 Root: 22.186 --> Root in between the borders! Added to results. Hyperbolic solved: 40.1506639226714 [20250519_004036.]: Samplename: Sample#7 Root: 40.151 --> Root in between the borders! Added to results. Hyperbolic solved: 86.1385429083643 [20250519_004036.]: Samplename: Sample#8 Root: 86.139 --> Root in between the borders! Added to results. Hyperbolic solved: 5.22287566816154 [20250519_004036.]: Samplename: Sample#9 Root: 5.223 --> Root in between the borders! Added to results. [20250519_004036.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 74.4935883316308 [20250519_004036.]: Samplename: Sample#1 Root: 74.494 --> Root in between the borders! Added to results. Hyperbolic solved: 28.4239304939459 [20250519_004036.]: Samplename: Sample#10 Root: 28.424 --> Root in between the borders! Added to results. Hyperbolic solved: 38.422868577211 [20250519_004036.]: Samplename: Sample#2 Root: 38.423 --> Root in between the borders! Added to results. Hyperbolic solved: 54.1812729208768 [20250519_004036.]: Samplename: Sample#3 Root: 54.181 --> Root in between the borders! Added to results. Hyperbolic solved: 9.35760412501846 [20250519_004036.]: Samplename: Sample#4 Root: 9.358 --> Root in between the borders! Added to results. Hyperbolic solved: 20.0638687855045 [20250519_004036.]: Samplename: Sample#5 Root: 20.064 --> Root in between the borders! Added to results. Hyperbolic solved: 18.2691112345675 [20250519_004036.]: Samplename: Sample#6 Root: 18.269 --> Root in between the borders! Added to results. Hyperbolic solved: 36.8485384997942 [20250519_004036.]: Samplename: Sample#7 Root: 36.849 --> Root in between the borders! Added to results. Hyperbolic solved: 86.7213727244426 [20250519_004036.]: Samplename: Sample#8 Root: 86.721 --> Root in between the borders! Added to results. Hyperbolic solved: 8.13467837636389 [20250519_004036.]: Samplename: Sample#9 Root: 8.135 --> Root in between the borders! Added to results. [20250519_004036.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 72.9754823399013 [20250519_004036.]: Samplename: Sample#1 Root: 72.975 --> Root in between the borders! Added to results. Hyperbolic solved: 26.09308106721 [20250519_004036.]: Samplename: Sample#10 Root: 26.093 --> Root in between the borders! Added to results. Hyperbolic solved: 38.5204516929959 [20250519_004036.]: Samplename: Sample#2 Root: 38.52 --> Root in between the borders! Added to results. Hyperbolic solved: 52.0558039343263 [20250519_004036.]: Samplename: Sample#3 Root: 52.056 --> Root in between the borders! Added to results. Hyperbolic solved: 10.6836235174275 [20250519_004036.]: Samplename: Sample#4 Root: 10.684 --> Root in between the borders! Added to results. Hyperbolic solved: 23.1384875877206 [20250519_004036.]: Samplename: Sample#5 Root: 23.138 --> Root in between the borders! Added to results. Hyperbolic solved: 24.8934322445107 [20250519_004036.]: Samplename: Sample#6 Root: 24.893 --> Root in between the borders! Added to results. Hyperbolic solved: 40.6015033285401 [20250519_004036.]: Samplename: Sample#7 Root: 40.602 --> Root in between the borders! Added to results. Hyperbolic solved: 86.3873125270529 [20250519_004036.]: Samplename: Sample#8 Root: 86.387 --> Root in between the borders! Added to results. Hyperbolic solved: 4.44964604860886 [20250519_004036.]: Samplename: Sample#9 Root: 4.45 --> Root in between the borders! Added to results. [20250519_004036.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 74.4016735507272 [20250519_004036.]: Samplename: Sample#1 Root: 74.402 --> Root in between the borders! Added to results. Hyperbolic solved: 26.4295897789912 [20250519_004036.]: Samplename: Sample#10 Root: 26.43 --> Root in between the borders! Added to results. Hyperbolic solved: 39.6008425259518 [20250519_004036.]: Samplename: Sample#2 Root: 39.601 --> Root in between the borders! Added to results. Hyperbolic solved: 54.4093205134833 [20250519_004036.]: Samplename: Sample#3 Root: 54.409 --> Root in between the borders! Added to results. Hyperbolic solved: 12.6125973016001 [20250519_004036.]: Samplename: Sample#4 Root: 12.613 --> Root in between the borders! Added to results. Hyperbolic solved: 21.5537046072918 [20250519_004036.]: Samplename: Sample#5 Root: 21.554 --> Root in between the borders! Added to results. Hyperbolic solved: 25.5116357756709 [20250519_004036.]: Samplename: Sample#6 Root: 25.512 --> Root in between the borders! Added to results. Hyperbolic solved: 38.1579452647512 [20250519_004036.]: Samplename: Sample#7 Root: 38.158 --> Root in between the borders! Added to results. Hyperbolic solved: 86.2182702652395 [20250519_004036.]: Samplename: Sample#8 Root: 86.218 --> Root in between the borders! Added to results. Hyperbolic solved: 7.06911234650237 [20250519_004036.]: Samplename: Sample#9 Root: 7.069 --> Root in between the borders! Added to results. [20250519_004036.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 70.2481376198381 [20250519_004036.]: Samplename: Sample#1 Root: 70.248 --> Root in between the borders! Added to results. Hyperbolic solved: 24.9396715205194 [20250519_004036.]: Samplename: Sample#10 Root: 24.94 --> Root in between the borders! Added to results. Hyperbolic solved: 40.4614457485807 [20250519_004036.]: Samplename: Sample#2 Root: 40.461 --> Root in between the borders! Added to results. Hyperbolic solved: 49.3815751365795 [20250519_004036.]: Samplename: Sample#3 Root: 49.382 --> Root in between the borders! Added to results. Hyperbolic solved: 12.353607679335 [20250519_004036.]: Samplename: Sample#4 Root: 12.354 --> Root in between the borders! Added to results. Hyperbolic solved: 22.0901459497739 [20250519_004036.]: Samplename: Sample#5 Root: 22.09 --> Root in between the borders! Added to results. Hyperbolic solved: 24.5507390437226 [20250519_004036.]: Samplename: Sample#6 Root: 24.551 --> Root in between the borders! Added to results. Hyperbolic solved: 39.0947007538418 [20250519_004036.]: Samplename: Sample#7 Root: 39.095 --> Root in between the borders! Added to results. Hyperbolic solved: 90.4841321892294 [20250519_004036.]: Samplename: Sample#8 Root: 90.484 --> Root in between the borders! Added to results. Hyperbolic solved: 6.91649336851909 [20250519_004036.]: Samplename: Sample#9 Root: 6.916 --> Root in between the borders! Added to results. [20250519_004036.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 77.4558189028663 [20250519_004036.]: Samplename: Sample#1 Root: 77.456 --> Root in between the borders! Added to results. Hyperbolic solved: 27.5880652239037 [20250519_004036.]: Samplename: Sample#10 Root: 27.588 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2657605119474 [20250519_004036.]: Samplename: Sample#2 Root: 37.266 --> Root in between the borders! Added to results. Hyperbolic solved: 51.469303759821 [20250519_004036.]: Samplename: Sample#3 Root: 51.469 --> Root in between the borders! Added to results. Hyperbolic solved: 12.3197919642261 [20250519_004036.]: Samplename: Sample#4 Root: 12.32 --> Root in between the borders! Added to results. Hyperbolic solved: 18.9916700132306 [20250519_004036.]: Samplename: Sample#5 Root: 18.992 --> Root in between the borders! Added to results. Hyperbolic solved: 27.3757521451495 [20250519_004036.]: Samplename: Sample#6 Root: 27.376 --> Root in between the borders! Added to results. Hyperbolic solved: 38.1192529388954 [20250519_004036.]: Samplename: Sample#7 Root: 38.119 --> Root in between the borders! Added to results. Hyperbolic solved: 87.7229687796537 [20250519_004036.]: Samplename: Sample#8 Root: 87.723 --> Root in between the borders! Added to results. Hyperbolic solved: 7.66260533640959 [20250519_004036.]: Samplename: Sample#9 Root: 7.663 --> Root in between the borders! Added to results. [20250519_004036.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 75.6858966925732 [20250519_004036.]: Samplename: Sample#1 Root: 75.686 --> Root in between the borders! Added to results. Hyperbolic solved: 23.9111476853231 [20250519_004036.]: Samplename: Sample#10 Root: 23.911 --> Root in between the borders! Added to results. Hyperbolic solved: 41.6834100561109 [20250519_004036.]: Samplename: Sample#2 Root: 41.683 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3038531152796 [20250519_004036.]: Samplename: Sample#3 Root: 52.304 --> Root in between the borders! Added to results. Hyperbolic solved: 14.584082616843 [20250519_004036.]: Samplename: Sample#4 Root: 14.584 --> Root in between the borders! Added to results. Hyperbolic solved: 23.8322996387272 [20250519_004036.]: Samplename: Sample#5 Root: 23.832 --> Root in between the borders! Added to results. Hyperbolic solved: 26.8055282397054 [20250519_004036.]: Samplename: Sample#6 Root: 26.806 --> Root in between the borders! Added to results. Hyperbolic solved: 39.1748201958722 [20250519_004036.]: Samplename: Sample#7 Root: 39.175 --> Root in between the borders! Added to results. Hyperbolic solved: 86.4968654540458 [20250519_004036.]: Samplename: Sample#8 Root: 86.497 --> Root in between the borders! Added to results. Hyperbolic solved: 5.20445580813888 [20250519_004036.]: Samplename: Sample#9 Root: 5.204 --> Root in between the borders! Added to results. [20250519_004036.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 71.4358559327679 [20250519_004036.]: Samplename: Sample#1 Root: 71.436 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5978231834828 [20250519_004036.]: Samplename: Sample#10 Root: 26.598 --> Root in between the borders! Added to results. Hyperbolic solved: 32.9687797883659 [20250519_004036.]: Samplename: Sample#2 Root: 32.969 --> Root in between the borders! Added to results. Hyperbolic solved: 56.5920244960613 [20250519_004036.]: Samplename: Sample#3 Root: 56.592 --> Root in between the borders! Added to results. Hyperbolic solved: 11.2061554830111 [20250519_004036.]: Samplename: Sample#4 Root: 11.206 --> Root in between the borders! Added to results. Hyperbolic solved: 19.2580019302788 [20250519_004036.]: Samplename: Sample#5 Root: 19.258 --> Root in between the borders! Added to results. Hyperbolic solved: 36.3407423784248 [20250519_004036.]: Samplename: Sample#6 Root: 36.341 --> Root in between the borders! Added to results. Hyperbolic solved: 42.2292176199449 [20250519_004036.]: Samplename: Sample#7 Root: 42.229 --> Root in between the borders! Added to results. Hyperbolic solved: 85.1479651236815 [20250519_004036.]: Samplename: Sample#8 Root: 85.148 --> Root in between the borders! Added to results. Hyperbolic solved: 1.97353330914679 [20250519_004036.]: Samplename: Sample#9 Root: 1.974 --> Root in between the borders! Added to results. [20250519_004036.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 78.362574368733 [20250519_004036.]: Samplename: Sample#1 Root: 78.363 --> Root in between the borders! Added to results. Hyperbolic solved: 24.4725957151698 [20250519_004036.]: Samplename: Sample#10 Root: 24.473 --> Root in between the borders! Added to results. Hyperbolic solved: 38.5471090507298 [20250519_004036.]: Samplename: Sample#2 Root: 38.547 --> Root in between the borders! Added to results. Hyperbolic solved: 48.850764118569 [20250519_004036.]: Samplename: Sample#3 Root: 48.851 --> Root in between the borders! Added to results. Hyperbolic solved: 13.0341042345297 [20250519_004036.]: Samplename: Sample#4 Root: 13.034 --> Root in between the borders! Added to results. Hyperbolic solved: 22.1156929084775 [20250519_004036.]: Samplename: Sample#5 Root: 22.116 --> Root in between the borders! Added to results. Hyperbolic solved: 33.0730206710577 [20250519_004036.]: Samplename: Sample#6 Root: 33.073 --> Root in between the borders! Added to results. Hyperbolic solved: 36.8757560868803 [20250519_004036.]: Samplename: Sample#7 Root: 36.876 --> Root in between the borders! Added to results. Hyperbolic solved: 89.6667208986789 [20250519_004036.]: Samplename: Sample#8 Root: 89.667 --> Root in between the borders! Added to results. Hyperbolic solved: 8.10593089892949 [20250519_004036.]: Samplename: Sample#9 Root: 8.106 --> Root in between the borders! Added to results. [20250519_004036.]: Solving hyperbolic regression for row_means Hyperbolic solved: 74.8404708557895 [20250519_004036.]: Samplename: Sample#1 Root: 74.84 --> Root in between the borders! Added to results. Hyperbolic solved: 26.2806125509682 [20250519_004036.]: Samplename: Sample#10 Root: 26.281 --> Root in between the borders! Added to results. Hyperbolic solved: 38.1683813928189 [20250519_004036.]: Samplename: Sample#2 Root: 38.168 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3554454867083 [20250519_004036.]: Samplename: Sample#3 Root: 52.355 --> Root in between the borders! Added to results. Hyperbolic solved: 12.0337443526331 [20250519_004036.]: Samplename: Sample#4 Root: 12.034 --> Root in between the borders! Added to results. Hyperbolic solved: 21.2452112654646 [20250519_004036.]: Samplename: Sample#5 Root: 21.245 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5661392150144 [20250519_004036.]: Samplename: Sample#6 Root: 26.566 --> Root in between the borders! Added to results. Hyperbolic solved: 38.9839362125804 [20250519_004036.]: Samplename: Sample#7 Root: 38.984 --> Root in between the borders! Added to results. Hyperbolic solved: 87.2201089452416 [20250519_004036.]: Samplename: Sample#8 Root: 87.22 --> Root in between the borders! Added to results. Hyperbolic solved: 6.14149092380673 [20250519_004036.]: Samplename: Sample#9 Root: 6.141 --> Root in between the borders! Added to results. [20250519_004036.]: Entered 'clean_dt'-Function [20250519_004036.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_004036.]: got experimental data [20250519_004036.]: Entered 'clean_dt'-Function [20250519_004036.]: Importing data of type 2: Many loci in one sample (e.g., next-gen seq or microarray data) [20250519_004036.]: got experimental data [20250519_004037.]: Entered 'clean_dt'-Function [20250519_004037.]: Entered 'clean_dt'-Function [20250519_004037.]: Entered 'clean_dt'-Function [20250519_004037.]: Entered 'clean_dt'-Function [20250519_004037.]: Entered 'clean_dt'-Function [20250519_004037.]: Entered 'clean_dt'-Function [20250519_004037.]: Entered 'clean_dt'-Function [20250519_004037.]: Entered 'clean_dt'-Function [20250519_004037.]: Entered 'clean_dt'-Function [20250519_004037.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_004037.]: got calibration data ### ERROR 54: first column cannot be parsed to numeric ### [20250519_004037.]: Entered 'clean_dt'-Function [20250519_004037.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_004037.]: got calibration data ### ERROR 54: first column cannot be parsed to numeric ### [20250519_004037.]: Entered 'clean_dt'-Function [20250519_004037.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_004037.]: got calibration data [20250519_004037.]: ### ERROR ### The data provided contains less than four calibration steps. At least four distinct calibration steps are required to perform bias correction. [20250519_004037.]: Entered 'clean_dt'-Function [20250519_004037.]: Importing data of type 2: Many loci in one sample (e.g., next-gen seq or microarray data) [20250519_004037.]: got calibration data [20250519_004037.]: ### ERROR ### The data provided contains locus ids with heterogeneous counts of CpG-sites. [20250519_004037.]: Entered 'clean_dt'-Function [20250519_004037.]: Importing data of type 2: Many loci in one sample (e.g., next-gen seq or microarray data) [20250519_004037.]: got experimental data [20250519_004037.]: ### ERROR ### The data provided contains locus ids with heterogeneous counts of CpG-sites. [20250519_004037.]: Entered 'clean_dt'-Function [20250519_004037.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_004037.]: got calibration data [20250519_004037.]: Entered 'clean_dt'-Function [20250519_004037.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_004037.]: got calibration data [20250519_004037.]: Entered 'cubic_regression'-Function [20250519_004037.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004037.]: Entered 'cubic_regression'-Function [20250519_004037.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004037.]: Entered 'clean_dt'-Function [20250519_004037.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_004037.]: got calibration data [20250519_004037.]: Entered 'hyperbolic_regression'-Function [20250519_004037.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004037.]: Entered 'hyperbolic_regression'-Function [20250519_004037.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004038.]: on_start: using future::plan("sequential") [20250519_004038.]: Entered 'clean_dt'-Function [20250519_004038.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_004038.]: got experimental data [20250519_004038.]: Entered 'clean_dt'-Function [20250519_004038.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_004038.]: got calibration data [20250519_004038.]: ### Starting with regression calculations ### [20250519_004038.]: Entered 'regression_type1'-Function [20250519_004038.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_004038.]: Logging df_agg: CpG#1 [20250519_004038.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004038.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_004038.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_004038.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_004038.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_004038.]: Entered 'hyperbolic_regression'-Function [20250519_004038.]: 'hyperbolic_regression': minmax = FALSE [20250519_004038.]: Entered 'cubic_regression'-Function [20250519_004038.]: 'cubic_regression': minmax = FALSE [20250519_004038.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_004038.]: Logging df_agg: CpG#2 [20250519_004038.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004038.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_004038.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_004038.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_004038.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_004038.]: Entered 'hyperbolic_regression'-Function [20250519_004038.]: 'hyperbolic_regression': minmax = FALSE [20250519_004038.]: Entered 'cubic_regression'-Function [20250519_004038.]: 'cubic_regression': minmax = FALSE [20250519_004038.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_004038.]: Logging df_agg: CpG#3 [20250519_004038.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004038.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_004038.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_004038.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_004038.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_004038.]: Entered 'hyperbolic_regression'-Function [20250519_004038.]: 'hyperbolic_regression': minmax = FALSE [20250519_004038.]: Entered 'cubic_regression'-Function [20250519_004038.]: 'cubic_regression': minmax = FALSE [20250519_004038.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_004038.]: Logging df_agg: CpG#4 [20250519_004038.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004038.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_004038.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_004038.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_004038.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_004038.]: Entered 'hyperbolic_regression'-Function [20250519_004038.]: 'hyperbolic_regression': minmax = FALSE [20250519_004038.]: Entered 'cubic_regression'-Function [20250519_004038.]: 'cubic_regression': minmax = FALSE [20250519_004038.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_004038.]: Logging df_agg: CpG#5 [20250519_004038.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004038.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_004038.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_004038.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_004038.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_004038.]: Entered 'hyperbolic_regression'-Function [20250519_004038.]: 'hyperbolic_regression': minmax = FALSE [20250519_004038.]: Entered 'cubic_regression'-Function [20250519_004038.]: 'cubic_regression': minmax = FALSE [20250519_004038.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_004038.]: Logging df_agg: CpG#6 [20250519_004038.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004038.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_004038.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_004038.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_004038.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_004038.]: Entered 'hyperbolic_regression'-Function [20250519_004038.]: 'hyperbolic_regression': minmax = FALSE [20250519_004038.]: Entered 'cubic_regression'-Function [20250519_004038.]: 'cubic_regression': minmax = FALSE [20250519_004038.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_004038.]: Logging df_agg: CpG#7 [20250519_004038.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004038.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_004038.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_004038.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_004038.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_004038.]: Entered 'hyperbolic_regression'-Function [20250519_004038.]: 'hyperbolic_regression': minmax = FALSE [20250519_004038.]: Entered 'cubic_regression'-Function [20250519_004038.]: 'cubic_regression': minmax = FALSE [20250519_004038.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_004038.]: Logging df_agg: CpG#8 [20250519_004038.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004038.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_004038.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_004038.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_004038.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_004038.]: Entered 'hyperbolic_regression'-Function [20250519_004038.]: 'hyperbolic_regression': minmax = FALSE [20250519_004038.]: Entered 'cubic_regression'-Function [20250519_004038.]: 'cubic_regression': minmax = FALSE [20250519_004038.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_004038.]: Logging df_agg: CpG#9 [20250519_004038.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004038.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_004038.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_004038.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_004038.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_004038.]: Entered 'hyperbolic_regression'-Function [20250519_004038.]: 'hyperbolic_regression': minmax = FALSE [20250519_004038.]: Entered 'cubic_regression'-Function [20250519_004038.]: 'cubic_regression': minmax = FALSE [20250519_004038.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_004038.]: Logging df_agg: row_means [20250519_004038.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004038.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_004038.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_004038.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_004038.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_004038.]: Entered 'hyperbolic_regression'-Function [20250519_004038.]: 'hyperbolic_regression': minmax = FALSE [20250519_004038.]: Entered 'cubic_regression'-Function [20250519_004038.]: 'cubic_regression': minmax = FALSE [20250519_004039.]: ### Starting with plotting ### [20250519_004039.]: Creating plot No. 1 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Test_CpG1.png [20250519_004039.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -123249135.40483 , a = 49589952.0989949 , b = -123249135.40483 , d = 59280789.0121927 [20250519_004039.]: # CpG-site: CpG#1 Cubic: Using a = 6.53413423120091e-05 , b = -0.0055806968734969 , c = 0.784061853455188 , d = 1.93182659932656 [20250519_004041.]: Creating plot No. 2 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Test_CpG2.png [20250519_004041.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = 167929575.171327 , a = 42299444.0962795 , b = 167929575.171327 , d = 47897274.2220611 [20250519_004041.]: # CpG-site: CpG#2 Cubic: Using a = 8.04237934904601e-06 , b = 0.00293158941798942 , c = 0.514821742825076 , d = 9.27667003367003 [20250519_004042.]: Creating plot No. 3 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Test_CpG3.png [20250519_004042.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = 31004745.3756238 , a = 31050253.3779659 , b = 31004745.3756238 , d = 40665231.758814 [20250519_004042.]: # CpG-site: CpG#3 Cubic: Using a = 2.30555869809204e-05 , b = -0.000797009331409346 , c = 0.62783129228796 , d = 3.88705218855218 [20250519_004044.]: Creating plot No. 4 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Test_CpG4.png [20250519_004044.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = 128022258.276155 , a = 42779497.5538086 , b = 128022258.276155 , d = 50999439.9227065 [20250519_004044.]: # CpG-site: CpG#4 Cubic: Using a = 3.2166356902357e-05 , b = -0.0015913142857143 , c = 0.697398364598366 , d = 6.17055050505048 [20250519_004045.]: Creating plot No. 5 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Test_CpG5.png [20250519_004045.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = 29447513.6184781 , a = 41864399.8673718 , b = 29447513.6184781 , d = 55382346.8470806 [20250519_004045.]: # CpG-site: CpG#5 Cubic: Using a = -4.48459708193036e-06 , b = 0.00375371236171235 , c = 0.422446384479718 , d = 5.17203872053872 [20250519_004047.]: Creating plot No. 6 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Test_CpG6.png [20250519_004047.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = 17150080.5960938 , a = 40932834.1078748 , b = 17150080.5960938 , d = 46723471.6686748 [20250519_004047.]: # CpG-site: CpG#6 Cubic: Using a = 2.38852884399552e-05 , b = 0.000918637037037021 , c = 0.561022132435467 , d = 6.1479276094276 [20250519_004048.]: Creating plot No. 7 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Test_CpG7.png [20250519_004048.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -57204506.2862076 , a = 29554652.6579632 , b = -57204506.2862076 , d = 48548425.6483363 [20250519_004048.]: # CpG-site: CpG#7 Cubic: Using a = 4.98010505050505e-05 , b = -0.00436152150072151 , c = 0.579588917748918 , d = 1.72104545454544 [20250519_004050.]: Creating plot No. 8 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Test_CpG8.png [20250519_004050.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = 263871870.982016 , a = 38159683.1948542 , b = 263871870.982016 , d = 45284547.6704654 [20250519_004050.]: # CpG-site: CpG#8 Cubic: Using a = -1.50060965207632e-05 , b = 0.00447978143338143 , c = 0.534897737694404 , d = 9.56981481481482 [20250519_004051.]: Creating plot No. 9 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Test_CpG9.png [20250519_004051.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -224404013.660658 , a = 51672526.9851193 , b = -224404013.660658 , d = 60201205.3472543 [20250519_004051.]: # CpG-site: CpG#9 Cubic: Using a = 7.72300426487093e-05 , b = -0.00542281173641174 , c = 0.67899229597563 , d = 2.72413468013467 [20250519_004053.]: Creating plot No. 10 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Test_rowmeans.png [20250519_004053.]: # CpG-site: row_means Hyperbolic: Using bias_weight = 29838275.3876239 , a = 33244770.7608578 , b = 29838275.3876239 , d = 41191067.4974404 [20250519_004053.]: # CpG-site: row_means Cubic: Using a = 2.88923726150392e-05 , b = -0.000629959275292592 , c = 0.600117857944524 , d = 5.17789562289563 [20250519_004054.]: on_start: using future::plan("sequential") [20250519_004054.]: Entered 'clean_dt'-Function [20250519_004054.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_004054.]: got experimental data [20250519_004054.]: Entered 'clean_dt'-Function [20250519_004054.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_004054.]: got calibration data [20250519_004054.]: ### Starting with regression calculations ### [20250519_004054.]: Entered 'regression_type1'-Function [20250519_004054.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_004054.]: Logging df_agg: CpG#1 [20250519_004054.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004054.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_004054.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_004054.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_004054.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_004055.]: Entered 'hyperbolic_regression'-Function [20250519_004055.]: 'hyperbolic_regression': minmax = FALSE [20250519_004055.]: Entered 'cubic_regression'-Function [20250519_004055.]: 'cubic_regression': minmax = FALSE [20250519_004055.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_004055.]: Logging df_agg: CpG#2 [20250519_004055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004055.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_004055.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_004055.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_004055.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_004055.]: Entered 'hyperbolic_regression'-Function [20250519_004055.]: 'hyperbolic_regression': minmax = FALSE [20250519_004055.]: Entered 'cubic_regression'-Function [20250519_004055.]: 'cubic_regression': minmax = FALSE [20250519_004055.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_004055.]: Logging df_agg: CpG#3 [20250519_004055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004055.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_004055.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_004055.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_004055.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_004055.]: Entered 'hyperbolic_regression'-Function [20250519_004055.]: 'hyperbolic_regression': minmax = FALSE [20250519_004055.]: Entered 'cubic_regression'-Function [20250519_004055.]: 'cubic_regression': minmax = FALSE [20250519_004055.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_004055.]: Logging df_agg: CpG#4 [20250519_004055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004055.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_004055.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_004055.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_004055.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_004055.]: Entered 'hyperbolic_regression'-Function [20250519_004055.]: 'hyperbolic_regression': minmax = FALSE [20250519_004055.]: Entered 'cubic_regression'-Function [20250519_004055.]: 'cubic_regression': minmax = FALSE [20250519_004055.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_004055.]: Logging df_agg: CpG#5 [20250519_004055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004055.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_004055.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_004055.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_004055.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_004055.]: Entered 'hyperbolic_regression'-Function [20250519_004055.]: 'hyperbolic_regression': minmax = FALSE [20250519_004055.]: Entered 'cubic_regression'-Function [20250519_004055.]: 'cubic_regression': minmax = FALSE [20250519_004055.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_004055.]: Logging df_agg: CpG#6 [20250519_004055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004055.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_004055.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_004055.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_004055.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_004055.]: Entered 'hyperbolic_regression'-Function [20250519_004055.]: 'hyperbolic_regression': minmax = FALSE [20250519_004055.]: Entered 'cubic_regression'-Function [20250519_004055.]: 'cubic_regression': minmax = FALSE [20250519_004055.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_004055.]: Logging df_agg: CpG#7 [20250519_004055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004055.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_004055.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_004055.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_004055.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_004055.]: Entered 'hyperbolic_regression'-Function [20250519_004055.]: 'hyperbolic_regression': minmax = FALSE [20250519_004055.]: Entered 'cubic_regression'-Function [20250519_004055.]: 'cubic_regression': minmax = FALSE [20250519_004055.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_004055.]: Logging df_agg: CpG#8 [20250519_004055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004055.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_004055.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_004055.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_004055.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_004055.]: Entered 'hyperbolic_regression'-Function [20250519_004055.]: 'hyperbolic_regression': minmax = FALSE [20250519_004055.]: Entered 'cubic_regression'-Function [20250519_004055.]: 'cubic_regression': minmax = FALSE [20250519_004055.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_004055.]: Logging df_agg: CpG#9 [20250519_004055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004055.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_004055.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_004055.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_004055.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_004055.]: Entered 'hyperbolic_regression'-Function [20250519_004055.]: 'hyperbolic_regression': minmax = FALSE [20250519_004055.]: Entered 'cubic_regression'-Function [20250519_004055.]: 'cubic_regression': minmax = FALSE [20250519_004055.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_004055.]: Logging df_agg: row_means [20250519_004055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004055.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_004055.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_004055.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_004055.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_004055.]: Entered 'hyperbolic_regression'-Function [20250519_004055.]: 'hyperbolic_regression': minmax = FALSE [20250519_004055.]: Entered 'cubic_regression'-Function [20250519_004055.]: 'cubic_regression': minmax = FALSE [20250519_004056.]: Entered 'solving_equations'-Function [20250519_004056.]: Solving cubic regression for CpG#1 Coefficients: -1.03617340067344Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004056.]: Samplename: 0 Root: 1.334 --> Root in between the borders! Added to results. Coefficients: -8.34150673400678Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004056.]: Samplename: 12.5 Root: 11.446 --> Root in between the borders! Added to results. Coefficients: -15.3881734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004056.]: Samplename: 25 Root: 22.228 --> Root in between the borders! Added to results. Coefficients: -24.2801734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004056.]: Samplename: 37.5 Root: 36.374 --> Root in between the borders! Added to results. Coefficients: -34.9006734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004056.]: Samplename: 50 Root: 52.044 --> Root in between the borders! Added to results. Coefficients: -46.3541734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004056.]: Samplename: 62.5 Root: 66.144 --> Root in between the borders! Added to results. Coefficients: -55.8931734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004056.]: Samplename: 75 Root: 75.864 --> Root in between the borders! Added to results. Coefficients: -63.0981734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004056.]: Samplename: 87.5 Root: 82.254 --> Root in between the borders! Added to results. Coefficients: -91.0461734006735Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004056.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.877 --> '100 < root < 110' --> substitute 100 [20250519_004056.]: Solving cubic regression for CpG#2 Coefficients: -0.283329966329966Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004056.]: Samplename: 0 Root: 0.549 --> Root in between the borders! Added to results. Coefficients: -6.33999663299663Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004056.]: Samplename: 12.5 Root: 11.533 --> Root in between the borders! Added to results. Coefficients: -15.93932996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004056.]: Samplename: 25 Root: 26.628 --> Root in between the borders! Added to results. Coefficients: -22.33732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004056.]: Samplename: 37.5 Root: 35.509 --> Root in between the borders! Added to results. Coefficients: -32.22832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004056.]: Samplename: 50 Root: 47.851 --> Root in between the borders! Added to results. Coefficients: -49.96332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004056.]: Samplename: 62.5 Root: 66.893 --> Root in between the borders! Added to results. Coefficients: -58.96582996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004056.]: Samplename: 75 Root: 75.431 --> Root in between the borders! Added to results. Coefficients: -68.8366632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004056.]: Samplename: 87.5 Root: 84.118 --> Root in between the borders! Added to results. Coefficients: -90.57732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004056.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.287 --> '100 < root < 110' --> substitute 100 [20250519_004056.]: Solving cubic regression for CpG#3 Coefficients: -0.90294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004056.]: Samplename: 0 Root: 1.441 --> Root in between the borders! Added to results. Coefficients: -6.57294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004056.]: Samplename: 12.5 Root: 10.568 --> Root in between the borders! Added to results. Coefficients: -15.4289478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004056.]: Samplename: 25 Root: 24.796 --> Root in between the borders! Added to results. Coefficients: -22.6129478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004056.]: Samplename: 37.5 Root: 35.952 --> Root in between the borders! Added to results. Coefficients: -32.7754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004056.]: Samplename: 50 Root: 50.684 --> Root in between the borders! Added to results. Coefficients: -43.8889478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004056.]: Samplename: 62.5 Root: 65.142 --> Root in between the borders! Added to results. Coefficients: -54.9754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004056.]: Samplename: 75 Root: 77.905 --> Root in between the borders! Added to results. Coefficients: -57.6562811447812Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004056.]: Samplename: 87.5 Root: 80.767 --> Root in between the borders! Added to results. Coefficients: -80.6649478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004056.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.38 --> '100 < root < 110' --> substitute 100 [20250519_004056.]: Solving cubic regression for CpG#4 Coefficients: -0.597449494949524Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004056.]: Samplename: 0 Root: 0.858 --> Root in between the borders! Added to results. Coefficients: -8.25278282828286Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004056.]: Samplename: 12.5 Root: 12.086 --> Root in between the borders! Added to results. Coefficients: -15.8034494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004056.]: Samplename: 25 Root: 23.316 --> Root in between the borders! Added to results. Coefficients: -25.5274494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004056.]: Samplename: 37.5 Root: 37.383 --> Root in between the borders! Added to results. Coefficients: -33.6369494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004056.]: Samplename: 50 Root: 48.353 --> Root in between the borders! Added to results. Coefficients: -50.2554494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004056.]: Samplename: 62.5 Root: 68.082 --> Root in between the borders! Added to results. Coefficients: -56.5394494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004056.]: Samplename: 75 Root: 74.615 --> Root in between the borders! Added to results. Coefficients: -65.5927828282829Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004056.]: Samplename: 87.5 Root: 83.254 --> Root in between the borders! Added to results. Coefficients: -88.3214494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004056.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.715 --> '100 < root < 110' --> substitute 100 [20250519_004056.]: Solving cubic regression for CpG#5 Coefficients: -0.623961279461278Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004056.]: Samplename: 0 Root: 1.458 --> Root in between the borders! Added to results. Coefficients: -4.76796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004056.]: Samplename: 12.5 Root: 10.347 --> Root in between the borders! Added to results. Coefficients: -12.7259612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004056.]: Samplename: 25 Root: 24.815 --> Root in between the borders! Added to results. Coefficients: -21.1599612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004056.]: Samplename: 37.5 Root: 37.902 --> Root in between the borders! Added to results. Coefficients: -30.6954612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004056.]: Samplename: 50 Root: 50.977 --> Root in between the borders! Added to results. Coefficients: -39.6579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004056.]: Samplename: 62.5 Root: 62.126 --> Root in between the borders! Added to results. Coefficients: -51.6829612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004056.]: Samplename: 75 Root: 75.852 --> Root in between the borders! Added to results. Coefficients: -61.0146279461279Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004056.]: Samplename: 87.5 Root: 85.767 --> Root in between the borders! Added to results. Coefficients: -76.0699612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004056.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.743 --> '100 < root < 110' --> substitute 100 [20250519_004056.]: Solving cubic regression for CpG#6 Coefficients: -0.196072390572403Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004056.]: Samplename: 0 Root: 0.349 --> Root in between the borders! Added to results. Coefficients: -6.73873905723907Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004056.]: Samplename: 12.5 Root: 11.718 --> Root in between the borders! Added to results. Coefficients: -15.8880723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004056.]: Samplename: 25 Root: 26.396 --> Root in between the borders! Added to results. Coefficients: -22.0000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004056.]: Samplename: 37.5 Root: 35.301 --> Root in between the borders! Added to results. Coefficients: -33.4445723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004056.]: Samplename: 50 Root: 50.134 --> Root in between the borders! Added to results. Coefficients: -46.9000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004056.]: Samplename: 62.5 Root: 64.993 --> Root in between the borders! Added to results. Coefficients: -55.8320723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004056.]: Samplename: 75 Root: 73.639 --> Root in between the borders! Added to results. Coefficients: -71.5454057239057Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004056.]: Samplename: 87.5 Root: 87.043 --> Root in between the borders! Added to results. Coefficients: -89.6560723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004056.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.329 --> '100 < root < 110' --> substitute 100 [20250519_004056.]: Solving cubic regression for CpG#7 Coefficients: -1.21495454545456Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004056.]: Samplename: 0 Root: 2.13 --> Root in between the borders! Added to results. Coefficients: -5.39562121212123Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004056.]: Samplename: 12.5 Root: 9.973 --> Root in between the borders! Added to results. Coefficients: -11.2649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004056.]: Samplename: 25 Root: 22.206 --> Root in between the borders! Added to results. Coefficients: -17.4509545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004056.]: Samplename: 37.5 Root: 35.814 --> Root in between the borders! Added to results. Coefficients: -26.0314545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004056.]: Samplename: 50 Root: 53.28 --> Root in between the borders! Added to results. Coefficients: -33.9649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004056.]: Samplename: 62.5 Root: 66.598 --> Root in between the borders! Added to results. Coefficients: -41.1689545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004056.]: Samplename: 75 Root: 76.575 --> Root in between the borders! Added to results. Coefficients: -44.1356212121212Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004056.]: Samplename: 87.5 Root: 80.219 --> Root in between the borders! Added to results. Coefficients: -67.2229545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004056.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.506 --> '100 < root < 110' --> substitute 100 [20250519_004056.]: Solving cubic regression for CpG#8 Coefficients: -1.09618518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004056.]: Samplename: 0 Root: 2.016 --> Root in between the borders! Added to results. Coefficients: -5.44685185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004056.]: Samplename: 12.5 Root: 9.458 --> Root in between the borders! Added to results. Coefficients: -16.9301851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004056.]: Samplename: 25 Root: 26.35 --> Root in between the borders! Added to results. Coefficients: -23.3501851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004056.]: Samplename: 37.5 Root: 34.728 --> Root in between the borders! Added to results. Coefficients: -37.6251851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004056.]: Samplename: 50 Root: 51.781 --> Root in between the borders! Added to results. Coefficients: -48.8261851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004056.]: Samplename: 62.5 Root: 64.192 --> Root in between the borders! Added to results. Coefficients: -61.6676851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004056.]: Samplename: 75 Root: 77.804 --> Root in between the borders! Added to results. Coefficients: -64.5101851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004056.]: Samplename: 87.5 Root: 80.758 --> Root in between the borders! Added to results. Coefficients: -86.0601851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004056.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.834 --> '100 < root < 110' --> substitute 100 [20250519_004056.]: Solving cubic regression for CpG#9 Coefficients: -0.989865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004056.]: Samplename: 0 Root: 1.475 --> Root in between the borders! Added to results. Coefficients: -6.39586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004056.]: Samplename: 12.5 Root: 10.12 --> Root in between the borders! Added to results. Coefficients: -14.7058653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004056.]: Samplename: 25 Root: 24.844 --> Root in between the borders! Added to results. Coefficients: -20.6238653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004056.]: Samplename: 37.5 Root: 35.327 --> Root in between the borders! Added to results. Coefficients: -31.3958653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004056.]: Samplename: 50 Root: 51.855 --> Root in between the borders! Added to results. Coefficients: -42.6858653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004056.]: Samplename: 62.5 Root: 65.265 --> Root in between the borders! Added to results. Coefficients: -52.9033653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004056.]: Samplename: 75 Root: 74.915 --> Root in between the borders! Added to results. Coefficients: -65.492531986532Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004056.]: Samplename: 87.5 Root: 84.67 --> Root in between the borders! Added to results. Coefficients: -92.9898653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004056.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.082 --> '100 < root < 110' --> substitute 100 [20250519_004056.]: Solving cubic regression for row_means Coefficients: -0.771215488215478Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 [20250519_004056.]: Samplename: 0 Root: 1.287 --> Root in between the borders! Added to results. Coefficients: -6.47247474747474Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 [20250519_004056.]: Samplename: 12.5 Root: 10.847 --> Root in between the borders! Added to results. Coefficients: -14.8972154882155Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 [20250519_004056.]: Samplename: 25 Root: 24.737 --> Root in between the borders! Added to results. Coefficients: -22.1492154882155Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 [20250519_004056.]: Samplename: 37.5 Root: 36.02 --> Root in between the borders! Added to results. Coefficients: -32.5259932659933Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 [20250519_004056.]: Samplename: 50 Root: 50.639 --> Root in between the borders! Added to results. Coefficients: -44.7218821548821Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 [20250519_004056.]: Samplename: 62.5 Root: 65.497 --> Root in between the borders! Added to results. Coefficients: -54.4032154882155Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 [20250519_004056.]: Samplename: 75 Root: 75.751 --> Root in between the borders! Added to results. Coefficients: -62.4313636363636Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 [20250519_004056.]: Samplename: 87.5 Root: 83.403 --> Root in between the borders! Added to results. Coefficients: -84.7343265993266Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 [20250519_004056.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.573 --> '100 < root < 110' --> substitute 100 [20250519_004056.]: ### Starting with regression calculations ### [20250519_004056.]: Entered 'regression_type1'-Function [20250519_004056.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) [20250519_004056.]: Logging df_agg: CpG#1 [20250519_004056.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004056.]: c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)[20250519_004056.]: c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)[20250519_004056.]: c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) [20250519_004056.]: Entered 'hyperbolic_regression'-Function [20250519_004056.]: 'hyperbolic_regression': minmax = FALSE [20250519_004056.]: Entered 'cubic_regression'-Function [20250519_004056.]: 'cubic_regression': minmax = FALSE [20250519_004056.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) [20250519_004056.]: Logging df_agg: CpG#2 [20250519_004056.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004056.]: c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)[20250519_004056.]: c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)[20250519_004056.]: c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) [20250519_004056.]: Entered 'hyperbolic_regression'-Function [20250519_004056.]: 'hyperbolic_regression': minmax = FALSE [20250519_004056.]: Entered 'cubic_regression'-Function [20250519_004056.]: 'cubic_regression': minmax = FALSE [20250519_004056.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) [20250519_004056.]: Logging df_agg: CpG#3 [20250519_004056.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004056.]: c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)[20250519_004056.]: c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)[20250519_004056.]: c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) [20250519_004056.]: Entered 'hyperbolic_regression'-Function [20250519_004056.]: 'hyperbolic_regression': minmax = FALSE [20250519_004056.]: Entered 'cubic_regression'-Function [20250519_004056.]: 'cubic_regression': minmax = FALSE [20250519_004056.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) [20250519_004056.]: Logging df_agg: CpG#4 [20250519_004056.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004056.]: c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)[20250519_004056.]: c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)[20250519_004056.]: c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) [20250519_004056.]: Entered 'hyperbolic_regression'-Function [20250519_004056.]: 'hyperbolic_regression': minmax = FALSE [20250519_004057.]: Entered 'cubic_regression'-Function [20250519_004057.]: 'cubic_regression': minmax = FALSE [20250519_004057.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) [20250519_004057.]: Logging df_agg: CpG#5 [20250519_004057.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004057.]: c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)[20250519_004057.]: c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)[20250519_004057.]: c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) [20250519_004057.]: Entered 'hyperbolic_regression'-Function [20250519_004057.]: 'hyperbolic_regression': minmax = FALSE [20250519_004057.]: Entered 'cubic_regression'-Function [20250519_004057.]: 'cubic_regression': minmax = FALSE [20250519_004057.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) [20250519_004057.]: Logging df_agg: CpG#6 [20250519_004057.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004057.]: c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)[20250519_004057.]: c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)[20250519_004057.]: c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) [20250519_004057.]: Entered 'hyperbolic_regression'-Function [20250519_004057.]: 'hyperbolic_regression': minmax = FALSE [20250519_004057.]: Entered 'cubic_regression'-Function [20250519_004057.]: 'cubic_regression': minmax = FALSE [20250519_004057.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) [20250519_004057.]: Logging df_agg: CpG#7 [20250519_004057.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004057.]: c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)[20250519_004057.]: c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)[20250519_004057.]: c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) [20250519_004057.]: Entered 'hyperbolic_regression'-Function [20250519_004057.]: 'hyperbolic_regression': minmax = FALSE [20250519_004057.]: Entered 'cubic_regression'-Function [20250519_004057.]: 'cubic_regression': minmax = FALSE [20250519_004057.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) [20250519_004057.]: Logging df_agg: CpG#8 [20250519_004057.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004057.]: c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)[20250519_004057.]: c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)[20250519_004057.]: c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) [20250519_004057.]: Entered 'hyperbolic_regression'-Function [20250519_004057.]: 'hyperbolic_regression': minmax = FALSE [20250519_004057.]: Entered 'cubic_regression'-Function [20250519_004057.]: 'cubic_regression': minmax = FALSE [20250519_004057.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) [20250519_004057.]: Logging df_agg: CpG#9 [20250519_004057.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004057.]: c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)[20250519_004057.]: c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)[20250519_004057.]: c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) [20250519_004057.]: Entered 'hyperbolic_regression'-Function [20250519_004057.]: 'hyperbolic_regression': minmax = FALSE [20250519_004057.]: Entered 'cubic_regression'-Function [20250519_004057.]: 'cubic_regression': minmax = FALSE [20250519_004057.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) [20250519_004057.]: Logging df_agg: row_means [20250519_004057.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004057.]: c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)[20250519_004057.]: c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)[20250519_004057.]: c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) [20250519_004057.]: Entered 'hyperbolic_regression'-Function [20250519_004057.]: 'hyperbolic_regression': minmax = FALSE [20250519_004057.]: Entered 'cubic_regression'-Function [20250519_004057.]: 'cubic_regression': minmax = FALSE [20250519_004057.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Test_error_CpG1_corrected_h.png [20250519_004059.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Test_error_CpG2_corrected_h.png [20250519_004100.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Test_error_CpG3_corrected_h.png [20250519_004101.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Test_error_CpG4_corrected_h.png [20250519_004103.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Test_error_CpG5_corrected_h.png [20250519_004104.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Test_error_CpG6_corrected_h.png [20250519_004105.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Test_error_CpG7_corrected_h.png [20250519_004106.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Test_error_CpG8_corrected_h.png [20250519_004108.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Test_error_CpG9_corrected_h.png [20250519_004109.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Test_error_rowmeans_corrected_h.png [20250519_004110.]: on_start: using future::plan("sequential") [20250519_004112.]: on_start: using future::plan("sequential") [ FAIL 4 | WARN 209 | SKIP 12 | PASS 42 ] == Skipped tests (12) ========================================================== * On CRAN (12): 'test-algorithm_minmax_FALSE.R:113:5', 'test-algorithm_minmax_FALSE_re.R:173:5', 'test-algorithm_minmax_TRUE.R:114:5', 'test-algorithm_minmax_TRUE_re.R:175:5', 'test-clean_dt.R:19:5', 'test-clean_dt.R:61:5', 'test-create_aggregated.R:21:5', 'test-cubic.R:31:5', 'test-cubic.R:60:5', 'test-hyperbolic.R:32:5', 'test-hyperbolic.R:61:5', 'test-lints.R:12:5' == Failed tests ================================================================ -- Failure ('test-algorithm_minmax_FALSE.R:71:5'): algorithm test, type 1, minmax = FALSE -- regression_results$result_list (`actual`) not equal to regression_results_fast$result_list (`expected`). `actual$CpG#1$Coef_hyper$a`: -108.5680 `expected$CpG#1$Coef_hyper$a`: -108.5682 `actual$CpG#1$Coef_hyper$b`: -937.719 `expected$CpG#1$Coef_hyper$b`: -937.718 `actual$CpG#1$Coef_hyper$d`: -232.0571 `expected$CpG#1$Coef_hyper$d`: -232.0574 `actual$CpG#1$Coef_hyper$b1`: 0.5690716 `expected$CpG#1$Coef_hyper$b1`: 0.5690721 `actual$CpG#1$Coef_hyper$s`: 4.075791 `expected$CpG#1$Coef_hyper$s`: 4.075782 `actual$CpG#2$Coef_hyper$a`: -141.862264 `expected$CpG#2$Coef_hyper$a`: -141.862268 `actual$CpG#3$Coef_hyper$a`: -170.4245 `expected$CpG#3$Coef_hyper$a`: -170.4243 `actual$CpG#3$Coef_hyper$b`: -1477.4686 `expected$CpG#3$Coef_hyper$b`: -1477.4687 `actual$CpG#3$Coef_hyper$d`: -327.1902 `expected$CpG#3$Coef_hyper$d`: -327.1899 `actual$CpG#3$Coef_hyper$b1`: 0.6943673 `expected$CpG#3$Coef_hyper$b1`: 0.6943671 `actual$CpG#3$Coef_hyper$s`: 4.540975 `expected$CpG#3$Coef_hyper$s`: 4.540980 `actual$CpG#4$Coef_hyper$a`: -162.1698 `expected$CpG#4$Coef_hyper$a`: -162.1699 `actual$CpG#4$Coef_hyper$b`: -2115.8779 `expected$CpG#4$Coef_hyper$b`: -2115.8781 `actual$CpG#4$Coef_hyper$d`: -299.6623 `expected$CpG#4$Coef_hyper$d`: -299.6625 `actual$CpG#4$Coef_hyper$b1`: 0.6662910 `expected$CpG#4$Coef_hyper$b1`: 0.6662912 `actual$CpG#4$Coef_hyper$s`: 7.075768 `expected$CpG#4$Coef_hyper$s`: 7.075764 `actual$CpG#5$Coef_hyper$a`: -151.44007 `expected$CpG#5$Coef_hyper$a`: -151.44009 `actual$CpG#5$Coef_hyper$b`: -1383.32739 `expected$CpG#5$Coef_hyper$b`: -1383.32735 `actual$CpG#5$Coef_hyper$d`: -304.20025 `expected$CpG#5$Coef_hyper$d`: -304.20028 `actual$CpG#5$Coef_hyper$b1`: 0.67126918 `expected$CpG#5$Coef_hyper$b1`: 0.67126921 `actual$CpG#5$Coef_hyper$s`: 4.5750679 `expected$CpG#5$Coef_hyper$s`: 4.5750673 `actual$CpG#6$Coef_hyper$a`: -125.016146 `expected$CpG#6$Coef_hyper$a`: -125.016150 `actual$CpG#6$Coef_hyper$b`: -1550.59308 `expected$CpG#6$Coef_hyper$b`: -1550.59305 `actual$CpG#6$Coef_hyper$d`: -247.279679 `expected$CpG#6$Coef_hyper$d`: -247.279684 `actual$CpG#6$Coef_hyper$s`: 6.2900686 `expected$CpG#6$Coef_hyper$s`: 6.2900684 `actual$CpG#7$Coef_hyper$a`: -75.2725 `expected$CpG#7$Coef_hyper$a`: -75.2727 `actual$CpG#7$Coef_hyper$b`: -772.8442 `expected$CpG#7$Coef_hyper$b`: -772.8433 `actual$CpG#7$Coef_hyper$d`: -226.6731 `expected$CpG#7$Coef_hyper$d`: -226.6734 `actual$CpG#7$Coef_hyper$b1`: 0.5588361 `expected$CpG#7$Coef_hyper$b1`: 0.5588367 `actual$CpG#7$Coef_hyper$s`: 3.474320 `expected$CpG#7$Coef_hyper$s`: 3.474311 `actual$CpG#8$Coef_hyper$a`: -278.0173 `expected$CpG#8$Coef_hyper$a`: -278.0172 `actual$CpG#8$Coef_hyper$b`: -3872.7362 `expected$CpG#8$Coef_hyper$b`: -3872.7353 `actual$CpG#8$Coef_hyper$d`: -438.8943 `expected$CpG#8$Coef_hyper$d`: -438.8941 `actual$CpG#8$Coef_hyper$b1`: 0.77215469 `expected$CpG#8$Coef_hyper$b1`: 0.77215462 `actual$CpG#8$Coef_hyper$s`: 8.831458 `expected$CpG#8$Coef_hyper$s`: 8.831459 `actual$CpG#9$Coef_hyper$a`: -70.67961 `expected$CpG#9$Coef_hyper$a`: -70.67963 `actual$CpG#9$Coef_hyper$b`: -843.9255 `expected$CpG#9$Coef_hyper$b`: -843.9253 `actual$CpG#9$Coef_hyper$d`: -184.48215 `expected$CpG#9$Coef_hyper$d`: -184.48218 `actual$CpG#9$Coef_hyper$b1`: 0.45794215 `expected$CpG#9$Coef_hyper$b1`: 0.45794222 `actual$CpG#9$Coef_hyper$s`: 4.615973 `expected$CpG#9$Coef_hyper$s`: 4.615971 `actual$row_means$Coef_hyper$a`: -127.67503 `expected$row_means$Coef_hyper$a`: -127.67508 `actual$row_means$Coef_hyper$b`: -1525.7576 `expected$row_means$Coef_hyper$b`: -1525.7575 `actual$row_means$Coef_hyper$d`: -263.00305 `expected$row_means$Coef_hyper$d`: -263.00310 `actual$row_means$Coef_hyper$b1`: 0.61977627 `expected$row_means$Coef_hyper$b1`: 0.61977635 `actual$row_means$Coef_hyper$s`: 5.824068 `expected$row_means$Coef_hyper$s`: 5.824067 -- Failure ('test-algorithm_minmax_FALSE.R:88:5'): algorithm test, type 1, minmax = FALSE -- regression_results$result_list (`actual`) not equal to regression_results_minpack$result_list (`expected`). `actual$CpG#1$SSE_hyper`: 77 `expected$CpG#1$SSE_hyper`: 228 `actual$CpG#1$Coef_hyper$a`: -109 `expected$CpG#1$Coef_hyper$a`: 49589952 `actual$CpG#1$Coef_hyper$b`: -938 `expected$CpG#1$Coef_hyper$b`: -123249135 `actual$CpG#1$Coef_hyper$d`: -232 `expected$CpG#1$Coef_hyper$d`: 59280789 `actual$CpG#1$Coef_hyper$R2`: 0.99 `expected$CpG#1$Coef_hyper$R2`: 0.97 `actual$CpG#1$Coef_hyper$b1`: 0.6 `expected$CpG#1$Coef_hyper$b1`: 1.0 `actual$CpG#1$Coef_hyper$s`: 4 `expected$CpG#1$Coef_hyper$s`: 2 `actual$CpG#2$SSE_hyper`: 46 `expected$CpG#2$SSE_hyper`: 175 `actual$CpG#2$Coef_hyper$a`: -142 `expected$CpG#2$Coef_hyper$a`: 42299444 `actual$CpG#2$Coef_hyper$b`: -2393 `expected$CpG#2$Coef_hyper$b`: 167929575 `actual$CpG#2$Coef_hyper$d`: -268 `expected$CpG#2$Coef_hyper$d`: 47897274 `actual$CpG#2$Coef_hyper$R2`: 0.99 `expected$CpG#2$Coef_hyper$R2`: 0.98 `actual$CpG#2$Coef_hyper$b1`: 0.6 `expected$CpG#2$Coef_hyper$b1`: 1.0 `actual$CpG#2$Coef_hyper$s`: 9 `expected$CpG#2$Coef_hyper$s`: 4 `actual$CpG#3$SSE_hyper`: 67 `expected$CpG#3$SSE_hyper`: 123 `actual$CpG#3$Coef_hyper$a`: -170 `expected$CpG#3$Coef_hyper$a`: 31050253 `actual$CpG#3$Coef_hyper$b`: -1477 `expected$CpG#3$Coef_hyper$b`: 31004745 `actual$CpG#3$Coef_hyper$d`: -327 `expected$CpG#3$Coef_hyper$d`: 40665232 `actual$CpG#3$Coef_hyper$R2`: 0.99 `expected$CpG#3$Coef_hyper$R2`: 0.98 `actual$CpG#3$Coef_hyper$b1`: 0.7 `expected$CpG#3$Coef_hyper$b1`: 1.0 `actual$CpG#3$Coef_hyper$s`: 5 `expected$CpG#3$Coef_hyper$s`: 1 `actual$CpG#4$SSE_hyper`: 58 `expected$CpG#4$SSE_hyper`: 141 `actual$CpG#4$Coef_hyper$a`: -162 `expected$CpG#4$Coef_hyper$a`: 42779498 `actual$CpG#4$Coef_hyper$b`: -2116 `expected$CpG#4$Coef_hyper$b`: 128022258 `actual$CpG#4$Coef_hyper$d`: -300 `expected$CpG#4$Coef_hyper$d`: 50999440 `actual$CpG#4$Coef_hyper$R2`: 0.99 `expected$CpG#4$Coef_hyper$R2`: 0.98 `actual$CpG#4$Coef_hyper$b1`: 0.7 `expected$CpG#4$Coef_hyper$b1`: 1.0 `actual$CpG#4$Coef_hyper$s`: 7 `expected$CpG#4$Coef_hyper$s`: 3 `actual$CpG#5$SSE_hyper`: 8 `expected$CpG#5$SSE_hyper`: 78 `actual$CpG#5$Coef_hyper$a`: -151 `expected$CpG#5$Coef_hyper$a`: 41864400 `actual$CpG#5$Coef_hyper$b`: -1383 `expected$CpG#5$Coef_hyper$b`: 29447514 `actual$CpG#5$Coef_hyper$d`: -304 `expected$CpG#5$Coef_hyper$d`: 55382347 `actual$CpG#5$Coef_hyper$R2`: 1.00 `expected$CpG#5$Coef_hyper$R2`: 0.99 `actual$CpG#5$Coef_hyper$b1`: 0.7 `expected$CpG#5$Coef_hyper$b1`: 1.0 `actual$CpG#5$Coef_hyper$s`: 5 `expected$CpG#5$Coef_hyper$s`: 1 `actual$CpG#6$SSE_hyper`: 12 `expected$CpG#6$SSE_hyper`: 168 `actual$CpG#6$Coef_hyper$a`: -125 `expected$CpG#6$Coef_hyper$a`: 40932834 `actual$CpG#6$Coef_hyper$b`: -1551 `expected$CpG#6$Coef_hyper$b`: 17150081 `actual$CpG#6$Coef_hyper$d`: -247 `expected$CpG#6$Coef_hyper$d`: 46723472 `actual$CpG#6$Coef_hyper$R2`: 1.00 `expected$CpG#6$Coef_hyper$R2`: 0.98 `actual$CpG#6$Coef_hyper$b1`: 0.6 `expected$CpG#6$Coef_hyper$b1`: 1.0 `actual$CpG#6$Coef_hyper$s`: 6 `expected$CpG#6$Coef_hyper$s`: 0 `actual$CpG#7$SSE_hyper`: 72 `expected$CpG#7$SSE_hyper`: 155 `actual$CpG#7$Coef_hyper$a`: -75 `expected$CpG#7$Coef_hyper$a`: 29554653 `actual$CpG#7$Coef_hyper$b`: -773 `expected$CpG#7$Coef_hyper$b`: -57204506 `actual$CpG#7$Coef_hyper$d`: -227 `expected$CpG#7$Coef_hyper$d`: 48548426 `actual$CpG#7$Coef_hyper$R2`: 0.98 `expected$CpG#7$Coef_hyper$R2`: 0.96 `actual$CpG#7$Coef_hyper$b1`: 0.6 `expected$CpG#7$Coef_hyper$b1`: 1.0 `actual$CpG#7$Coef_hyper$s`: 3 `expected$CpG#7$Coef_hyper$s`: 1 `actual$CpG#8$SSE_hyper`: 75 `expected$CpG#8$SSE_hyper`: 112 `actual$CpG#8$Coef_hyper$a`: -278 `expected$CpG#8$Coef_hyper$a`: 38159683 `actual$CpG#8$Coef_hyper$b`: -3873 `expected$CpG#8$Coef_hyper$b`: 263871871 `actual$CpG#8$Coef_hyper$d`: -439 `expected$CpG#8$Coef_hyper$d`: 45284548 `actual$CpG#8$Coef_hyper$R2`: 0.989 `expected$CpG#8$Coef_hyper$R2`: 0.983 `actual$CpG#8$Coef_hyper$b1`: 0.8 `expected$CpG#8$Coef_hyper$b1`: 1.0 `actual$CpG#8$Coef_hyper$s`: 9 `expected$CpG#8$Coef_hyper$s`: 6 `actual$CpG#9$SSE_hyper`: 33 `expected$CpG#9$SSE_hyper`: 352 `actual$CpG#9$Coef_hyper$a`: -71 `expected$CpG#9$Coef_hyper$a`: 51672527 `actual$CpG#9$Coef_hyper$b`: -844 `expected$CpG#9$Coef_hyper$b`: -224404014 `actual$CpG#9$Coef_hyper$d`: -184 `expected$CpG#9$Coef_hyper$d`: 60201205 `actual$CpG#9$Coef_hyper$R2`: 1.00 `expected$CpG#9$Coef_hyper$R2`: 0.95 `actual$CpG#9$Coef_hyper$b1`: 0.5 `expected$CpG#9$Coef_hyper$b1`: 1.0 `actual$CpG#9$Coef_hyper$s`: 4.6 `expected$CpG#9$Coef_hyper$s`: 3.7 `actual$row_means$SSE_hyper`: 35 `expected$row_means$SSE_hyper`: 144 `actual$row_means$Coef_hyper$a`: -128 `expected$row_means$Coef_hyper$a`: 33244771 `actual$row_means$Coef_hyper$b`: -1526 `expected$row_means$Coef_hyper$b`: 29838275 `actual$row_means$Coef_hyper$d`: -263 `expected$row_means$Coef_hyper$d`: 41191067 `actual$row_means$Coef_hyper$R2`: 0.99 `expected$row_means$Coef_hyper$R2`: 0.98 `actual$row_means$Coef_hyper$b1`: 0.6 `expected$row_means$Coef_hyper$b1`: 1.0 `actual$row_means$Coef_hyper$s`: 6 `expected$row_means$Coef_hyper$s`: 1 -- Failure ('test-algorithm_minmax_TRUE.R:71:5'): algorithm test, type 1, minmax = TRUE -- regression_results$result_list (`actual`) not equal to regression_results_fast$result_list (`expected`). `actual$CpG#1$Coef_hyper$b`: 0.5364372 `expected$CpG#1$Coef_hyper$b`: 0.5364373 actual$CpG#1$Coef_cubic$a != expected$CpG#1$Coef_cubic$a but don't know how to show the difference actual$CpG#1$Coef_cubic$b != expected$CpG#1$Coef_cubic$b but don't know how to show the difference actual$CpG#2$Coef_cubic$a != expected$CpG#2$Coef_cubic$a but don't know how to show the difference actual$CpG#2$Coef_cubic$b != expected$CpG#2$Coef_cubic$b but don't know how to show the difference actual$CpG#3$Coef_cubic$a != expected$CpG#3$Coef_cubic$a but don't know how to show the difference `actual$CpG#3$Coef_cubic$b`: -0.00173040 `expected$CpG#3$Coef_cubic$b`: -0.00173041 `actual$CpG#4$Coef_hyper$b`: 0.6263293 `expected$CpG#4$Coef_hyper$b`: 0.6263302 actual$CpG#4$Coef_cubic$a != expected$CpG#4$Coef_cubic$a but don't know how to show the difference actual$CpG#4$Coef_cubic$b != expected$CpG#4$Coef_cubic$b but don't know how to show the difference actual$CpG#5$Coef_cubic$a != expected$CpG#5$Coef_cubic$a but don't know how to show the difference actual$CpG#5$Coef_cubic$b != expected$CpG#5$Coef_cubic$b but don't know how to show the difference actual$CpG#6$Coef_cubic$a != expected$CpG#6$Coef_cubic$a but don't know how to show the difference actual$CpG#6$Coef_cubic$b != expected$CpG#6$Coef_cubic$b but don't know how to show the difference `actual$CpG#7$Coef_hyper$b`: 0.50578129 `expected$CpG#7$Coef_hyper$b`: 0.50578130 actual$CpG#7$Coef_cubic$a != expected$CpG#7$Coef_cubic$a but don't know how to show the difference actual$CpG#7$Coef_cubic$b != expected$CpG#7$Coef_cubic$b but don't know how to show the difference `actual$CpG#8$Coef_hyper$b`: 0.68763945 `expected$CpG#8$Coef_hyper$b`: 0.68763952 actual$CpG#8$Coef_cubic$a != expected$CpG#8$Coef_cubic$a but don't know how to show the difference actual$CpG#8$Coef_cubic$b != expected$CpG#8$Coef_cubic$b but don't know how to show the difference `actual$CpG#9$Coef_hyper$b`: 0.44888828 `expected$CpG#9$Coef_hyper$b`: 0.44888829 actual$CpG#9$Coef_cubic$a != expected$CpG#9$Coef_cubic$a but don't know how to show the difference actual$CpG#9$Coef_cubic$b != expected$CpG#9$Coef_cubic$b but don't know how to show the difference actual$row_means$Coef_cubic$a != expected$row_means$Coef_cubic$a but don't know how to show the difference actual$row_means$Coef_cubic$b != expected$row_means$Coef_cubic$b but don't know how to show the difference -- Failure ('test-algorithm_minmax_TRUE.R:88:5'): algorithm test, type 1, minmax = TRUE -- regression_results$result_list (`actual`) not equal to regression_results_minpack$result_list (`expected`). `actual$CpG#1$Coef_hyper$b`: 0.5364372 `expected$CpG#1$Coef_hyper$b`: 0.5364373 actual$CpG#1$Coef_cubic$a != expected$CpG#1$Coef_cubic$a but don't know how to show the difference actual$CpG#1$Coef_cubic$b != expected$CpG#1$Coef_cubic$b but don't know how to show the difference actual$CpG#2$Coef_cubic$a != expected$CpG#2$Coef_cubic$a but don't know how to show the difference actual$CpG#2$Coef_cubic$b != expected$CpG#2$Coef_cubic$b but don't know how to show the difference `actual$CpG#3$Coef_hyper$b`: 0.6287998 `expected$CpG#3$Coef_hyper$b`: 0.6287994 actual$CpG#3$Coef_cubic$a != expected$CpG#3$Coef_cubic$a but don't know how to show the difference actual$CpG#3$Coef_cubic$b != expected$CpG#3$Coef_cubic$b but don't know how to show the difference `actual$CpG#4$Coef_hyper$b`: 0.6263293 `expected$CpG#4$Coef_hyper$b`: 0.6263302 `actual$CpG#4$Coef_cubic$a`: 0.00004340 `expected$CpG#4$Coef_cubic$a`: 0.00004341 actual$CpG#4$Coef_cubic$b != expected$CpG#4$Coef_cubic$b but don't know how to show the difference `actual$CpG#5$Coef_hyper$b`: 0.63653152 `expected$CpG#5$Coef_hyper$b`: 0.63653153 actual$CpG#5$Coef_cubic$a != expected$CpG#5$Coef_cubic$a but don't know how to show the difference actual$CpG#5$Coef_cubic$b != expected$CpG#5$Coef_cubic$b but don't know how to show the difference actual$CpG#6$Coef_cubic$a != expected$CpG#6$Coef_cubic$a but don't know how to show the difference actual$CpG#6$Coef_cubic$b != expected$CpG#6$Coef_cubic$b but don't know how to show the difference `actual$CpG#7$Coef_hyper$b`: 0.5057813 `expected$CpG#7$Coef_hyper$b`: 0.5057809 actual$CpG#7$Coef_cubic$a != expected$CpG#7$Coef_cubic$a but don't know how to show the difference `actual$CpG#7$Coef_cubic$b`: -0.00511940 `expected$CpG#7$Coef_cubic$b`: -0.00511941 `actual$CpG#8$Coef_hyper$b`: 0.68763945 `expected$CpG#8$Coef_hyper$b`: 0.68763952 actual$CpG#8$Coef_cubic$a != expected$CpG#8$Coef_cubic$a but don't know how to show the difference actual$CpG#8$Coef_cubic$b != expected$CpG#8$Coef_cubic$b but don't know how to show the difference `actual$CpG#9$Coef_hyper$b`: 0.44888828 `expected$CpG#9$Coef_hyper$b`: 0.44888824 actual$CpG#9$Coef_cubic$b != expected$CpG#9$Coef_cubic$b but don't know how to show the difference actual$row_means$Coef_cubic$a != expected$row_means$Coef_cubic$a but don't know how to show the difference `actual$row_means$Coef_cubic$b`: -0.00123898 `expected$row_means$Coef_cubic$b`: -0.00123897 [ FAIL 4 | WARN 209 | SKIP 12 | PASS 42 ] Error: Test failures Execution halted Error in deferred_run(env) : could not find function "deferred_run" Calls: * checking for unstated dependencies in vignettes ... OK * checking package vignettes ... OK * checking re-building of vignette outputs ... OK * checking PDF version of manual ... OK * DONE Status: 1 ERROR See '/tmp/th798/17369309/R-devel/1149/rBiasCorrection.Rcheck/00check.log' for details. [1] "2025-05-19 00:41:25 MST" [1] "2025-05-19 00:41:25 MST" Installing package into '/tmp/th798/17369309/R-devel/1149/library' (as 'lib' is unspecified) * installing *source* package 'data.table' ... ** this is package 'data.table' version '1.17.99' ** using staged installation zlib 1.2.13 is available ok * checking if R installation supports OpenMP without any extra hints... yes ** libs using C compiler: 'gcc (Spack GCC) 12.2.0' /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c assign.c -o assign.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c between.c -o between.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c bmerge.c -o bmerge.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c chmatch.c -o chmatch.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c cj.c -o cj.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c coalesce.c -o coalesce.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c dogroups.c -o dogroups.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c fastmean.c -o fastmean.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c fcast.c -o fcast.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c fifelse.c -o fifelse.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c fmelt.c -o fmelt.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c forder.c -o forder.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c frank.c -o frank.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c fread.c -o fread.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c freadR.c -o freadR.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c froll.c -o froll.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c frollR.c -o frollR.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c frolladaptive.c -o frolladaptive.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c fsort.c -o fsort.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c fwrite.c -o fwrite.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c fwriteR.c -o fwriteR.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c gsumm.c -o gsumm.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c idatetime.c -o idatetime.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c ijoin.c -o ijoin.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c init.c -o init.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c inrange.c -o inrange.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c nafill.c -o nafill.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c negate.c -o negate.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c nqrecreateindices.c -o nqrecreateindices.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c openmp-utils.c -o openmp-utils.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c programming.c -o programming.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c quickselect.c -o quickselect.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c rbindlist.c -o rbindlist.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c reorder.c -o reorder.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c shift.c -o shift.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c snprintf.c -o snprintf.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c subset.c -o subset.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c transpose.c -o transpose.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c types.c -o types.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c uniqlist.c -o uniqlist.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c utils.c -o utils.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c vecseq.c -o vecseq.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-devel/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c wrappers.c -o wrappers.o /packages/gcc/12.2.0-nnbserq/bin/gcc -shared -L/home/th798/.conda/envs/emacs1/lib -Wl,-rpath=/home/th798/.conda/envs/emacs1/lib -L/home/th798/lib -Wl,-rpath=/home/th798/lib -L/home/th798/lib64 -Wl,-rpath=/home/th798/lib64 -o data.table.so assign.o between.o bmerge.o chmatch.o cj.o coalesce.o dogroups.o fastmean.o fcast.o fifelse.o fmelt.o forder.o frank.o fread.o freadR.o froll.o frollR.o frolladaptive.o fsort.o fwrite.o fwriteR.o gsumm.o idatetime.o ijoin.o init.o inrange.o nafill.o negate.o nqrecreateindices.o openmp-utils.o programming.o quickselect.o rbindlist.o reorder.o shift.o snprintf.o subset.o transpose.o types.o uniqlist.o utils.o vecseq.o wrappers.o -fopenmp -L/home/th798/.conda/envs/emacs1/lib -lz PKG_CFLAGS = -fopenmp -I/home/th798/.conda/envs/emacs1/include PKG_LIBS = -fopenmp -L/home/th798/.conda/envs/emacs1/lib -lz if [ "data.table.so" != "data_table.so" ]; then mv data.table.so data_table.so; fi if [ "" != "Windows_NT" ] && [ `uname -s` = 'Darwin' ]; then install_name_tool -id data_table.so data_table.so; fi installing to /tmp/th798/17369309/R-devel/1149/library/00LOCK-data.table/00new/data.table/libs ** R ** inst ** byte-compile and prepare package for lazy loading ** help *** installing help indices ** building package indices ** installing vignettes ** testing if installed package can be loaded from temporary location ** checking absolute paths in shared objects and dynamic libraries ** testing if installed package can be loaded from final location ** testing if installed package keeps a record of temporary installation path * DONE (data.table) [1] "2025-05-19 00:41:47 MST" * using log directory '/tmp/th798/17369309/R-devel/1149/rBiasCorrection.Rcheck' * using R Under development (unstable) (2025-05-18 r88216) * using platform: x86_64-pc-linux-gnu * R was compiled by gcc (Spack GCC) 12.2.0 GNU Fortran (Spack GCC) 12.2.0 * running under: Red Hat Enterprise Linux 8.10 (Ootpa) * using session charset: ASCII * checking for file 'rBiasCorrection/DESCRIPTION' ... OK * this is package 'rBiasCorrection' version '0.3.5' * package encoding: UTF-8 * checking package namespace information ... OK * checking package dependencies ... OK * checking if this is a source package ... OK * checking if there is a namespace ... OK * checking for executable files ... OK * checking for hidden files and directories ... OK * checking for portable file names ... OK * checking for sufficient/correct file permissions ... OK * checking whether package 'rBiasCorrection' can be installed ... OK * checking installed package size ... OK * checking package directory ... OK * checking 'build' directory ... OK * checking DESCRIPTION meta-information ... OK * checking top-level files ... OK * checking for left-over files ... OK * checking index information ... OK * checking package subdirectories ... OK * checking code files for non-ASCII characters ... OK * checking R files for syntax errors ... OK * checking whether the package can be loaded ... OK * checking whether the package can be loaded with stated dependencies ... OK * checking whether the package can be unloaded cleanly ... OK * checking whether the namespace can be loaded with stated dependencies ... OK * checking whether the namespace can be unloaded cleanly ... OK * checking loading without being on the library search path ... OK * checking whether startup messages can be suppressed ... OK * checking dependencies in R code ... OK * checking S3 generic/method consistency ... OK * checking replacement functions ... OK * checking foreign function calls ... OK * checking R code for possible problems ... OK * checking Rd files ... OK * checking Rd metadata ... OK * checking Rd cross-references ... OK * checking for missing documentation entries ... OK * checking for code/documentation mismatches ... OK * checking Rd \usage sections ... OK * checking Rd contents ... OK * checking for unstated dependencies in examples ... OK * checking contents of 'data' directory ... OK * checking data for non-ASCII characters ... OK * checking LazyData ... OK * checking data for ASCII and uncompressed saves ... OK * checking installed files from 'inst/doc' ... OK * checking files in 'vignettes' ... OK * checking examples ... OK * checking for unstated dependencies in 'tests' ... OK * checking tests ... Running 'testthat.R' ERROR Running the tests in 'tests/testthat.R' failed. Complete output: > # https://github.com/Rdatatable/data.table/issues/5658 > Sys.setenv("OMP_THREAD_LIMIT" = 2) > Sys.setenv("Ncpu" = 2) > > library(testthat) > library(rBiasCorrection) > > local_edition(3) > > test_check("rBiasCorrection") [20250519_004252.]: Entered 'clean_dt'-Function [20250519_004252.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_004252.]: got experimental data [20250519_004252.]: Entered 'clean_dt'-Function [20250519_004252.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_004252.]: got calibration data [20250519_004252.]: ### Starting with regression calculations ### [20250519_004252.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_004252.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_004252.]: Logging df_agg: CpG#1 [20250519_004252.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004252.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_004252.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_004252.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_004252.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_004252.]: Entered 'hyperbolic_regression'-Function [20250519_004252.]: 'hyperbolic_regression': minmax = FALSE [20250519_004253.]: Entered 'cubic_regression'-Function [20250519_004253.]: 'cubic_regression': minmax = FALSE [20250519_004253.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_004253.]: Logging df_agg: CpG#2 [20250519_004253.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004253.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_004253.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_004253.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_004253.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_004253.]: Entered 'hyperbolic_regression'-Function [20250519_004253.]: 'hyperbolic_regression': minmax = FALSE [20250519_004253.]: Entered 'cubic_regression'-Function [20250519_004253.]: 'cubic_regression': minmax = FALSE [20250519_004253.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_004253.]: Logging df_agg: CpG#3 [20250519_004253.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004253.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_004253.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_004253.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_004253.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_004253.]: Entered 'hyperbolic_regression'-Function [20250519_004253.]: 'hyperbolic_regression': minmax = FALSE [20250519_004253.]: Entered 'cubic_regression'-Function [20250519_004253.]: 'cubic_regression': minmax = FALSE [20250519_004253.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_004253.]: Logging df_agg: CpG#4 [20250519_004253.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004253.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_004253.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_004253.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_004253.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_004253.]: Entered 'hyperbolic_regression'-Function [20250519_004253.]: 'hyperbolic_regression': minmax = FALSE [20250519_004253.]: Entered 'cubic_regression'-Function [20250519_004253.]: 'cubic_regression': minmax = FALSE [20250519_004253.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_004253.]: Logging df_agg: CpG#5 [20250519_004253.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004253.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_004253.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_004253.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_004253.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_004253.]: Entered 'hyperbolic_regression'-Function [20250519_004253.]: 'hyperbolic_regression': minmax = FALSE [20250519_004254.]: Entered 'cubic_regression'-Function [20250519_004254.]: 'cubic_regression': minmax = FALSE [20250519_004254.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_004254.]: Logging df_agg: CpG#6 [20250519_004254.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004254.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_004254.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_004254.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_004254.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_004254.]: Entered 'hyperbolic_regression'-Function [20250519_004254.]: 'hyperbolic_regression': minmax = FALSE [20250519_004254.]: Entered 'cubic_regression'-Function [20250519_004254.]: 'cubic_regression': minmax = FALSE [20250519_004254.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_004254.]: Logging df_agg: CpG#7 [20250519_004254.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004254.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_004254.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_004254.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_004254.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_004254.]: Entered 'hyperbolic_regression'-Function [20250519_004254.]: 'hyperbolic_regression': minmax = FALSE [20250519_004254.]: Entered 'cubic_regression'-Function [20250519_004254.]: 'cubic_regression': minmax = FALSE [20250519_004254.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_004254.]: Logging df_agg: CpG#8 [20250519_004254.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004254.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_004254.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_004254.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_004254.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_004254.]: Entered 'hyperbolic_regression'-Function [20250519_004254.]: 'hyperbolic_regression': minmax = FALSE [20250519_004254.]: Entered 'cubic_regression'-Function [20250519_004254.]: 'cubic_regression': minmax = FALSE [20250519_004254.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_004254.]: Logging df_agg: CpG#9 [20250519_004254.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004254.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_004254.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_004254.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_004254.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_004254.]: Entered 'hyperbolic_regression'-Function [20250519_004254.]: 'hyperbolic_regression': minmax = FALSE [20250519_004255.]: Entered 'cubic_regression'-Function [20250519_004255.]: 'cubic_regression': minmax = FALSE [20250519_004255.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_004255.]: Logging df_agg: row_means [20250519_004255.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004255.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_004255.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_004255.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_004255.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_004255.]: Entered 'hyperbolic_regression'-Function [20250519_004255.]: 'hyperbolic_regression': minmax = FALSE [20250519_004255.]: Entered 'cubic_regression'-Function [20250519_004255.]: 'cubic_regression': minmax = FALSE [20250519_004256.]: ### Starting with regression calculations ### [20250519_004256.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_004256.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_004256.]: Logging df_agg: CpG#1 [20250519_004256.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004256.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_004256.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_004256.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_004256.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_004256.]: Entered 'hyperbolic_regression'-Function [20250519_004256.]: 'hyperbolic_regression': minmax = FALSE [20250519_004256.]: Entered 'cubic_regression'-Function [20250519_004256.]: 'cubic_regression': minmax = FALSE [20250519_004256.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_004256.]: Logging df_agg: CpG#2 [20250519_004256.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004256.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_004256.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_004256.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_004256.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_004256.]: Entered 'hyperbolic_regression'-Function [20250519_004256.]: 'hyperbolic_regression': minmax = FALSE [20250519_004256.]: Entered 'cubic_regression'-Function [20250519_004256.]: 'cubic_regression': minmax = FALSE [20250519_004256.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_004256.]: Logging df_agg: CpG#3 [20250519_004256.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004256.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_004256.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_004256.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_004256.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_004256.]: Entered 'hyperbolic_regression'-Function [20250519_004256.]: 'hyperbolic_regression': minmax = FALSE [20250519_004257.]: Entered 'cubic_regression'-Function [20250519_004257.]: 'cubic_regression': minmax = FALSE [20250519_004257.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_004257.]: Logging df_agg: CpG#4 [20250519_004257.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004257.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_004257.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_004257.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_004257.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_004257.]: Entered 'hyperbolic_regression'-Function [20250519_004257.]: 'hyperbolic_regression': minmax = FALSE [20250519_004257.]: Entered 'cubic_regression'-Function [20250519_004257.]: 'cubic_regression': minmax = FALSE [20250519_004257.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_004257.]: Logging df_agg: CpG#5 [20250519_004257.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004257.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_004257.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_004257.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_004257.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_004257.]: Entered 'hyperbolic_regression'-Function [20250519_004257.]: 'hyperbolic_regression': minmax = FALSE [20250519_004257.]: Entered 'cubic_regression'-Function [20250519_004257.]: 'cubic_regression': minmax = FALSE [20250519_004257.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_004257.]: Logging df_agg: CpG#6 [20250519_004257.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004257.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_004257.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_004257.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_004257.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_004257.]: Entered 'hyperbolic_regression'-Function [20250519_004257.]: 'hyperbolic_regression': minmax = FALSE [20250519_004257.]: Entered 'cubic_regression'-Function [20250519_004257.]: 'cubic_regression': minmax = FALSE [20250519_004257.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_004257.]: Logging df_agg: CpG#7 [20250519_004257.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004257.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_004257.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_004257.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_004257.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_004257.]: Entered 'hyperbolic_regression'-Function [20250519_004257.]: 'hyperbolic_regression': minmax = FALSE [20250519_004258.]: Entered 'cubic_regression'-Function [20250519_004258.]: 'cubic_regression': minmax = FALSE [20250519_004258.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_004258.]: Logging df_agg: CpG#8 [20250519_004258.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004258.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_004258.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_004258.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_004258.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_004258.]: Entered 'hyperbolic_regression'-Function [20250519_004258.]: 'hyperbolic_regression': minmax = FALSE [20250519_004258.]: Entered 'cubic_regression'-Function [20250519_004258.]: 'cubic_regression': minmax = FALSE [20250519_004258.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_004258.]: Logging df_agg: CpG#9 [20250519_004258.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004258.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_004258.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_004258.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_004258.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_004258.]: Entered 'hyperbolic_regression'-Function [20250519_004258.]: 'hyperbolic_regression': minmax = FALSE [20250519_004258.]: Entered 'cubic_regression'-Function [20250519_004258.]: 'cubic_regression': minmax = FALSE [20250519_004258.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_004258.]: Logging df_agg: row_means [20250519_004258.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004258.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_004258.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_004258.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_004258.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_004258.]: Entered 'hyperbolic_regression'-Function [20250519_004258.]: 'hyperbolic_regression': minmax = FALSE [20250519_004258.]: Entered 'cubic_regression'-Function [20250519_004258.]: 'cubic_regression': minmax = FALSE [20250519_004300.]: ### Starting with regression calculations ### [20250519_004300.]: Entered 'regression_type1'-Function [20250519_004300.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_004300.]: Logging df_agg: CpG#1 [20250519_004300.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004300.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_004300.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_004300.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_004300.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_004300.]: Entered 'hyperbolic_regression'-Function [20250519_004300.]: 'hyperbolic_regression': minmax = FALSE [20250519_004300.]: Entered 'cubic_regression'-Function [20250519_004300.]: 'cubic_regression': minmax = FALSE [20250519_004300.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_004300.]: Logging df_agg: CpG#2 [20250519_004300.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004300.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_004300.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_004300.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_004300.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_004300.]: Entered 'hyperbolic_regression'-Function [20250519_004300.]: 'hyperbolic_regression': minmax = FALSE [20250519_004300.]: Entered 'cubic_regression'-Function [20250519_004300.]: 'cubic_regression': minmax = FALSE [20250519_004300.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_004300.]: Logging df_agg: CpG#3 [20250519_004300.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004300.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_004300.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_004300.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_004300.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_004300.]: Entered 'hyperbolic_regression'-Function [20250519_004300.]: 'hyperbolic_regression': minmax = FALSE [20250519_004300.]: Entered 'cubic_regression'-Function [20250519_004300.]: 'cubic_regression': minmax = FALSE [20250519_004300.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_004300.]: Logging df_agg: CpG#4 [20250519_004300.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004300.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_004300.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_004300.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_004300.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_004300.]: Entered 'hyperbolic_regression'-Function [20250519_004300.]: 'hyperbolic_regression': minmax = FALSE [20250519_004300.]: Entered 'cubic_regression'-Function [20250519_004300.]: 'cubic_regression': minmax = FALSE [20250519_004300.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_004300.]: Logging df_agg: CpG#5 [20250519_004300.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004300.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_004300.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_004300.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_004300.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_004300.]: Entered 'hyperbolic_regression'-Function [20250519_004300.]: 'hyperbolic_regression': minmax = FALSE [20250519_004300.]: Entered 'cubic_regression'-Function [20250519_004300.]: 'cubic_regression': minmax = FALSE [20250519_004300.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_004300.]: Logging df_agg: CpG#6 [20250519_004300.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004300.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_004300.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_004300.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_004300.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_004300.]: Entered 'hyperbolic_regression'-Function [20250519_004300.]: 'hyperbolic_regression': minmax = FALSE [20250519_004300.]: Entered 'cubic_regression'-Function [20250519_004300.]: 'cubic_regression': minmax = FALSE [20250519_004300.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_004300.]: Logging df_agg: CpG#7 [20250519_004300.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004300.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_004300.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_004300.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_004300.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_004300.]: Entered 'hyperbolic_regression'-Function [20250519_004300.]: 'hyperbolic_regression': minmax = FALSE [20250519_004301.]: Entered 'cubic_regression'-Function [20250519_004301.]: 'cubic_regression': minmax = FALSE [20250519_004301.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_004301.]: Logging df_agg: CpG#8 [20250519_004301.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004301.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_004301.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_004301.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_004301.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_004301.]: Entered 'hyperbolic_regression'-Function [20250519_004301.]: 'hyperbolic_regression': minmax = FALSE [20250519_004301.]: Entered 'cubic_regression'-Function [20250519_004301.]: 'cubic_regression': minmax = FALSE [20250519_004301.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_004301.]: Logging df_agg: CpG#9 [20250519_004301.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004301.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_004301.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_004301.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_004301.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_004301.]: Entered 'hyperbolic_regression'-Function [20250519_004301.]: 'hyperbolic_regression': minmax = FALSE [20250519_004301.]: Entered 'cubic_regression'-Function [20250519_004301.]: 'cubic_regression': minmax = FALSE [20250519_004301.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_004301.]: Logging df_agg: row_means [20250519_004301.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004301.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_004301.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_004301.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_004301.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_004301.]: Entered 'hyperbolic_regression'-Function [20250519_004301.]: 'hyperbolic_regression': minmax = FALSE [20250519_004301.]: Entered 'cubic_regression'-Function [20250519_004301.]: 'cubic_regression': minmax = FALSE [20250519_004302.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_004303.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_004303.]: Logging df_agg: CpG#1 [20250519_004303.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004303.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_004303.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_004303.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_004303.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_004303.]: Entered 'hyperbolic_regression'-Function [20250519_004303.]: 'hyperbolic_regression': minmax = FALSE [20250519_004303.]: Entered 'cubic_regression'-Function [20250519_004303.]: 'cubic_regression': minmax = FALSE [20250519_004303.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_004303.]: Logging df_agg: CpG#2 [20250519_004303.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004303.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_004303.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_004303.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_004303.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_004303.]: Entered 'hyperbolic_regression'-Function [20250519_004303.]: 'hyperbolic_regression': minmax = FALSE [20250519_004303.]: Entered 'cubic_regression'-Function [20250519_004303.]: 'cubic_regression': minmax = FALSE [20250519_004303.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_004303.]: Logging df_agg: CpG#3 [20250519_004303.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004303.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_004303.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_004303.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_004303.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_004303.]: Entered 'hyperbolic_regression'-Function [20250519_004303.]: 'hyperbolic_regression': minmax = FALSE [20250519_004304.]: Entered 'cubic_regression'-Function [20250519_004304.]: 'cubic_regression': minmax = FALSE [20250519_004304.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_004304.]: Logging df_agg: CpG#4 [20250519_004304.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004304.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_004304.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_004304.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_004304.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_004304.]: Entered 'hyperbolic_regression'-Function [20250519_004304.]: 'hyperbolic_regression': minmax = FALSE [20250519_004304.]: Entered 'cubic_regression'-Function [20250519_004304.]: 'cubic_regression': minmax = FALSE [20250519_004304.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_004304.]: Logging df_agg: CpG#5 [20250519_004304.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004304.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_004304.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_004304.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_004304.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_004304.]: Entered 'hyperbolic_regression'-Function [20250519_004304.]: 'hyperbolic_regression': minmax = FALSE [20250519_004304.]: Entered 'cubic_regression'-Function [20250519_004304.]: 'cubic_regression': minmax = FALSE [20250519_004304.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_004304.]: Logging df_agg: CpG#6 [20250519_004304.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004304.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_004304.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_004304.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_004304.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_004304.]: Entered 'hyperbolic_regression'-Function [20250519_004304.]: 'hyperbolic_regression': minmax = FALSE [20250519_004304.]: Entered 'cubic_regression'-Function [20250519_004304.]: 'cubic_regression': minmax = FALSE [20250519_004304.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_004304.]: Logging df_agg: CpG#7 [20250519_004304.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004304.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_004304.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_004304.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_004304.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_004304.]: Entered 'hyperbolic_regression'-Function [20250519_004304.]: 'hyperbolic_regression': minmax = FALSE [20250519_004305.]: Entered 'cubic_regression'-Function [20250519_004305.]: 'cubic_regression': minmax = FALSE [20250519_004305.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_004305.]: Logging df_agg: CpG#8 [20250519_004305.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004305.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_004305.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_004305.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_004305.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_004305.]: Entered 'hyperbolic_regression'-Function [20250519_004305.]: 'hyperbolic_regression': minmax = FALSE [20250519_004305.]: Entered 'cubic_regression'-Function [20250519_004305.]: 'cubic_regression': minmax = FALSE [20250519_004305.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_004305.]: Logging df_agg: CpG#9 [20250519_004305.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004305.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_004305.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_004305.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_004305.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_004305.]: Entered 'hyperbolic_regression'-Function [20250519_004305.]: 'hyperbolic_regression': minmax = FALSE [20250519_004305.]: Entered 'cubic_regression'-Function [20250519_004305.]: 'cubic_regression': minmax = FALSE [20250519_004305.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_004305.]: Logging df_agg: row_means [20250519_004305.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004305.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_004305.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_004305.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_004305.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_004305.]: Entered 'hyperbolic_regression'-Function [20250519_004305.]: 'hyperbolic_regression': minmax = FALSE [20250519_004306.]: Entered 'cubic_regression'-Function [20250519_004306.]: 'cubic_regression': minmax = FALSE [20250519_004306.]: Entered 'clean_dt'-Function [20250519_004306.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_004306.]: got experimental data [20250519_004306.]: Entered 'clean_dt'-Function [20250519_004306.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_004306.]: got calibration data [20250519_004306.]: ### Starting with regression calculations ### [20250519_004306.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_004306.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_004306.]: Logging df_agg: CpG#1 [20250519_004306.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004306.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_004306.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_004306.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_004306.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_004306.]: Entered 'hyperbolic_regression'-Function [20250519_004306.]: 'hyperbolic_regression': minmax = FALSE [20250519_004307.]: Entered 'cubic_regression'-Function [20250519_004307.]: 'cubic_regression': minmax = FALSE [20250519_004307.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_004307.]: Logging df_agg: CpG#2 [20250519_004307.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004307.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_004307.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_004307.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_004307.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_004307.]: Entered 'hyperbolic_regression'-Function [20250519_004307.]: 'hyperbolic_regression': minmax = FALSE [20250519_004307.]: Entered 'cubic_regression'-Function [20250519_004307.]: 'cubic_regression': minmax = FALSE [20250519_004307.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_004307.]: Logging df_agg: CpG#3 [20250519_004307.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004307.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_004307.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_004307.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_004307.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_004307.]: Entered 'hyperbolic_regression'-Function [20250519_004307.]: 'hyperbolic_regression': minmax = FALSE [20250519_004307.]: Entered 'cubic_regression'-Function [20250519_004307.]: 'cubic_regression': minmax = FALSE [20250519_004307.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_004307.]: Logging df_agg: CpG#4 [20250519_004307.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004307.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_004307.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_004307.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_004307.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_004307.]: Entered 'hyperbolic_regression'-Function [20250519_004307.]: 'hyperbolic_regression': minmax = FALSE [20250519_004307.]: Entered 'cubic_regression'-Function [20250519_004307.]: 'cubic_regression': minmax = FALSE [20250519_004307.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_004307.]: Logging df_agg: CpG#5 [20250519_004307.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004307.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_004307.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_004307.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_004307.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_004307.]: Entered 'hyperbolic_regression'-Function [20250519_004307.]: 'hyperbolic_regression': minmax = FALSE [20250519_004308.]: Entered 'cubic_regression'-Function [20250519_004308.]: 'cubic_regression': minmax = FALSE [20250519_004308.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_004308.]: Logging df_agg: CpG#6 [20250519_004308.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004308.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_004308.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_004308.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_004308.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_004308.]: Entered 'hyperbolic_regression'-Function [20250519_004308.]: 'hyperbolic_regression': minmax = FALSE [20250519_004308.]: Entered 'cubic_regression'-Function [20250519_004308.]: 'cubic_regression': minmax = FALSE [20250519_004308.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_004308.]: Logging df_agg: CpG#7 [20250519_004308.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004308.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_004308.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_004308.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_004308.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_004308.]: Entered 'hyperbolic_regression'-Function [20250519_004308.]: 'hyperbolic_regression': minmax = FALSE [20250519_004308.]: Entered 'cubic_regression'-Function [20250519_004308.]: 'cubic_regression': minmax = FALSE [20250519_004308.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_004308.]: Logging df_agg: CpG#8 [20250519_004308.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004308.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_004308.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_004308.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_004308.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_004308.]: Entered 'hyperbolic_regression'-Function [20250519_004308.]: 'hyperbolic_regression': minmax = FALSE [20250519_004308.]: Entered 'cubic_regression'-Function [20250519_004308.]: 'cubic_regression': minmax = FALSE [20250519_004308.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_004308.]: Logging df_agg: CpG#9 [20250519_004308.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004308.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_004308.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_004308.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_004308.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_004308.]: Entered 'hyperbolic_regression'-Function [20250519_004308.]: 'hyperbolic_regression': minmax = FALSE [20250519_004309.]: Entered 'cubic_regression'-Function [20250519_004309.]: 'cubic_regression': minmax = FALSE [20250519_004309.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_004309.]: Logging df_agg: row_means [20250519_004309.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004309.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_004309.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_004309.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_004309.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_004309.]: Entered 'hyperbolic_regression'-Function [20250519_004309.]: 'hyperbolic_regression': minmax = FALSE [20250519_004309.]: Entered 'cubic_regression'-Function [20250519_004309.]: 'cubic_regression': minmax = FALSE [20250519_004309.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_004310.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_004310.]: Logging df_agg: CpG#1 [20250519_004310.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004310.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_004310.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_004310.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_004310.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_004310.]: Entered 'hyperbolic_regression'-Function [20250519_004310.]: 'hyperbolic_regression': minmax = FALSE [20250519_004310.]: Entered 'cubic_regression'-Function [20250519_004310.]: 'cubic_regression': minmax = FALSE [20250519_004310.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_004310.]: Logging df_agg: CpG#2 [20250519_004310.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004310.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_004310.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_004310.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_004310.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_004310.]: Entered 'hyperbolic_regression'-Function [20250519_004310.]: 'hyperbolic_regression': minmax = FALSE [20250519_004310.]: Entered 'cubic_regression'-Function [20250519_004310.]: 'cubic_regression': minmax = FALSE [20250519_004310.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_004310.]: Logging df_agg: CpG#3 [20250519_004310.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004310.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_004310.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_004310.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_004310.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_004310.]: Entered 'hyperbolic_regression'-Function [20250519_004310.]: 'hyperbolic_regression': minmax = FALSE [20250519_004310.]: Entered 'cubic_regression'-Function [20250519_004310.]: 'cubic_regression': minmax = FALSE [20250519_004310.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_004310.]: Logging df_agg: CpG#4 [20250519_004310.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004310.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_004310.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_004310.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_004310.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_004310.]: Entered 'hyperbolic_regression'-Function [20250519_004310.]: 'hyperbolic_regression': minmax = FALSE [20250519_004311.]: Entered 'cubic_regression'-Function [20250519_004311.]: 'cubic_regression': minmax = FALSE [20250519_004311.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_004311.]: Logging df_agg: CpG#5 [20250519_004311.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004311.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_004311.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_004311.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_004311.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_004311.]: Entered 'hyperbolic_regression'-Function [20250519_004311.]: 'hyperbolic_regression': minmax = FALSE [20250519_004311.]: Entered 'cubic_regression'-Function [20250519_004311.]: 'cubic_regression': minmax = FALSE [20250519_004311.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_004311.]: Logging df_agg: CpG#6 [20250519_004311.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004311.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_004311.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_004311.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_004311.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_004311.]: Entered 'hyperbolic_regression'-Function [20250519_004311.]: 'hyperbolic_regression': minmax = FALSE [20250519_004311.]: Entered 'cubic_regression'-Function [20250519_004311.]: 'cubic_regression': minmax = FALSE [20250519_004311.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_004311.]: Logging df_agg: CpG#7 [20250519_004311.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004311.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_004311.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_004311.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_004311.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_004311.]: Entered 'hyperbolic_regression'-Function [20250519_004311.]: 'hyperbolic_regression': minmax = FALSE [20250519_004311.]: Entered 'cubic_regression'-Function [20250519_004311.]: 'cubic_regression': minmax = FALSE [20250519_004311.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_004311.]: Logging df_agg: CpG#8 [20250519_004311.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004311.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_004311.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_004311.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_004311.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_004311.]: Entered 'hyperbolic_regression'-Function [20250519_004311.]: 'hyperbolic_regression': minmax = FALSE [20250519_004311.]: Entered 'cubic_regression'-Function [20250519_004312.]: 'cubic_regression': minmax = FALSE [20250519_004312.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_004312.]: Logging df_agg: CpG#9 [20250519_004312.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004312.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_004312.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_004312.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_004312.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_004312.]: Entered 'hyperbolic_regression'-Function [20250519_004312.]: 'hyperbolic_regression': minmax = FALSE [20250519_004312.]: Entered 'cubic_regression'-Function [20250519_004312.]: 'cubic_regression': minmax = FALSE [20250519_004312.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_004312.]: Logging df_agg: row_means [20250519_004312.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004312.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_004312.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_004312.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_004312.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_004312.]: Entered 'hyperbolic_regression'-Function [20250519_004312.]: 'hyperbolic_regression': minmax = FALSE [20250519_004312.]: Entered 'cubic_regression'-Function [20250519_004312.]: 'cubic_regression': minmax = FALSE [20250519_004313.]: Entered 'solving_equations'-Function [20250519_004313.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: -2.23222837469517 [20250519_004313.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.232 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.1698391693126 [20250519_004313.]: Samplename: 12.5 Root: 12.17 --> Root in between the borders! Added to results. Hyperbolic solved: 24.4781729791561 [20250519_004313.]: Samplename: 25 Root: 24.478 --> Root in between the borders! Added to results. Hyperbolic solved: 38.1730159074047 [20250519_004313.]: Samplename: 37.5 Root: 38.173 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3348987967717 [20250519_004313.]: Samplename: 50 Root: 52.335 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4582305966058 [20250519_004313.]: Samplename: 62.5 Root: 65.458 --> Root in between the borders! Added to results. Hyperbolic solved: 75.0090269722885 [20250519_004313.]: Samplename: 75 Root: 75.009 --> Root in between the borders! Added to results. Hyperbolic solved: 81.527135738891 [20250519_004313.]: Samplename: 87.5 Root: 81.527 --> Root in between the borders! Added to results. Hyperbolic solved: 102.400825350044 [20250519_004313.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.401 --> '100 < root < 110' --> substitute 100 [20250519_004313.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 1.13663087266051 [20250519_004313.]: Samplename: 0 Root: 1.137 --> Root in between the borders! Added to results. Hyperbolic solved: 11.4130277963576 [20250519_004313.]: Samplename: 12.5 Root: 11.413 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1741025868157 [20250519_004313.]: Samplename: 25 Root: 26.174 --> Root in between the borders! Added to results. Hyperbolic solved: 35.1051721736724 [20250519_004313.]: Samplename: 37.5 Root: 35.105 --> Root in between the borders! Added to results. Hyperbolic solved: 47.6856613240114 [20250519_004313.]: Samplename: 50 Root: 47.686 --> Root in between the borders! Added to results. Hyperbolic solved: 67.1442585354696 [20250519_004313.]: Samplename: 62.5 Root: 67.144 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7646957109439 [20250519_004313.]: Samplename: 75 Root: 75.765 --> Root in between the borders! Added to results. Hyperbolic solved: 84.4056634834815 [20250519_004313.]: Samplename: 87.5 Root: 84.406 --> Root in between the borders! Added to results. Hyperbolic solved: 100.94855337669 [20250519_004313.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.949 --> '100 < root < 110' --> substitute 100 [20250519_004313.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 0.512348257854635 [20250519_004313.]: Samplename: 0 Root: 0.512 --> Root in between the borders! Added to results. Hyperbolic solved: 10.7523476241811 [20250519_004313.]: Samplename: 12.5 Root: 10.752 --> Root in between the borders! Added to results. Hyperbolic solved: 25.5218062516799 [20250519_004313.]: Samplename: 25 Root: 25.522 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5269315729339 [20250519_004313.]: Samplename: 37.5 Root: 36.527 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7907738274884 [20250519_004313.]: Samplename: 50 Root: 50.791 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8684489988589 [20250519_004313.]: Samplename: 62.5 Root: 64.868 --> Root in between the borders! Added to results. Hyperbolic solved: 77.5522101073979 [20250519_004313.]: Samplename: 75 Root: 77.552 --> Root in between the borders! Added to results. Hyperbolic solved: 80.4372474691229 [20250519_004313.]: Samplename: 87.5 Root: 80.437 --> Root in between the borders! Added to results. Hyperbolic solved: 102.703772810881 [20250519_004313.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.704 --> '100 < root < 110' --> substitute 100 [20250519_004313.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: -0.519513573026794 [20250519_004313.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.52 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.4933760051342 [20250519_004313.]: Samplename: 12.5 Root: 12.493 --> Root in between the borders! Added to results. Hyperbolic solved: 24.2684789512555 [20250519_004313.]: Samplename: 25 Root: 24.268 --> Root in between the borders! Added to results. Hyperbolic solved: 38.0816229393195 [20250519_004313.]: Samplename: 37.5 Root: 38.082 --> Root in between the borders! Added to results. Hyperbolic solved: 48.5842089589187 [20250519_004313.]: Samplename: 50 Root: 48.584 --> Root in between the borders! Added to results. Hyperbolic solved: 67.6720983549562 [20250519_004313.]: Samplename: 62.5 Root: 67.672 --> Root in between the borders! Added to results. Hyperbolic solved: 74.1547759694059 [20250519_004313.]: Samplename: 75 Root: 74.155 --> Root in between the borders! Added to results. Hyperbolic solved: 82.8820147905386 [20250519_004313.]: Samplename: 87.5 Root: 82.882 --> Root in between the borders! Added to results. Hyperbolic solved: 102.078935346876 [20250519_004313.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.079 --> '100 < root < 110' --> substitute 100 [20250519_004313.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 2.41558040143479 [20250519_004313.]: Samplename: 0 Root: 2.416 --> Root in between the borders! Added to results. Hyperbolic solved: 10.1649584830834 [20250519_004313.]: Samplename: 12.5 Root: 10.165 --> Root in between the borders! Added to results. Hyperbolic solved: 23.9830670020905 [20250519_004313.]: Samplename: 25 Root: 23.983 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2773406385708 [20250519_004313.]: Samplename: 37.5 Root: 37.277 --> Root in between the borders! Added to results. Hyperbolic solved: 50.8659103346102 [20250519_004313.]: Samplename: 50 Root: 50.866 --> Root in between the borders! Added to results. Hyperbolic solved: 62.4341926937382 [20250519_004313.]: Samplename: 62.5 Root: 62.434 --> Root in between the borders! Added to results. Hyperbolic solved: 76.3914832329149 [20250519_004313.]: Samplename: 75 Root: 76.391 --> Root in between the borders! Added to results. Hyperbolic solved: 86.1597399215782 [20250519_004313.]: Samplename: 87.5 Root: 86.16 --> Root in between the borders! Added to results. Hyperbolic solved: 100.267701841999 [20250519_004313.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.268 --> '100 < root < 110' --> substitute 100 [20250519_004313.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 0.138179963459196 [20250519_004313.]: Samplename: 0 Root: 0.138 --> Root in between the borders! Added to results. Hyperbolic solved: 11.8635989635202 [20250519_004313.]: Samplename: 12.5 Root: 11.864 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5108198460409 [20250519_004313.]: Samplename: 25 Root: 26.511 --> Root in between the borders! Added to results. Hyperbolic solved: 35.3206004021833 [20250519_004313.]: Samplename: 37.5 Root: 35.321 --> Root in between the borders! Added to results. Hyperbolic solved: 50.0571987473308 [20250519_004313.]: Samplename: 50 Root: 50.057 --> Root in between the borders! Added to results. Hyperbolic solved: 64.9604436607063 [20250519_004313.]: Samplename: 62.5 Root: 64.96 --> Root in between the borders! Added to results. Hyperbolic solved: 73.6690699266294 [20250519_004313.]: Samplename: 75 Root: 73.669 --> Root in between the borders! Added to results. Hyperbolic solved: 87.1267946879604 [20250519_004313.]: Samplename: 87.5 Root: 87.127 --> Root in between the borders! Added to results. Hyperbolic solved: 100.261842767865 [20250519_004313.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.262 --> '100 < root < 110' --> substitute 100 [20250519_004313.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: -1.37239249181669 [20250519_004313.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -1.372 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.199366215418 [20250519_004313.]: Samplename: 12.5 Root: 10.199 --> Root in between the borders! Added to results. Hyperbolic solved: 24.595295361327 [20250519_004313.]: Samplename: 25 Root: 24.595 --> Root in between the borders! Added to results. Hyperbolic solved: 37.8312095633391 [20250519_004313.]: Samplename: 37.5 Root: 37.831 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5590896582852 [20250519_004313.]: Samplename: 50 Root: 53.559 --> Root in between the borders! Added to results. Hyperbolic solved: 65.9367389282431 [20250519_004313.]: Samplename: 62.5 Root: 65.937 --> Root in between the borders! Added to results. Hyperbolic solved: 75.736370152841 [20250519_004313.]: Samplename: 75 Root: 75.736 --> Root in between the borders! Added to results. Hyperbolic solved: 79.433089359524 [20250519_004313.]: Samplename: 87.5 Root: 79.433 --> Root in between the borders! Added to results. Hyperbolic solved: 103.004516225662 [20250519_004313.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 103.005 --> '100 < root < 110' --> substitute 100 [20250519_004313.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 2.80067549526643 [20250519_004313.]: Samplename: 0 Root: 2.801 --> Root in between the borders! Added to results. Hyperbolic solved: 9.27534686003087 [20250519_004313.]: Samplename: 12.5 Root: 9.275 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4762624089952 [20250519_004313.]: Samplename: 25 Root: 25.476 --> Root in between the borders! Added to results. Hyperbolic solved: 34.0122098045585 [20250519_004313.]: Samplename: 37.5 Root: 34.012 --> Root in between the borders! Added to results. Hyperbolic solved: 51.784270967861 [20250519_004313.]: Samplename: 50 Root: 51.784 --> Root in between the borders! Added to results. Hyperbolic solved: 64.6732380258789 [20250519_004313.]: Samplename: 62.5 Root: 64.673 --> Root in between the borders! Added to results. Hyperbolic solved: 78.4327054483192 [20250519_004313.]: Samplename: 75 Root: 78.433 --> Root in between the borders! Added to results. Hyperbolic solved: 81.3427308951795 [20250519_004313.]: Samplename: 87.5 Root: 81.343 --> Root in between the borders! Added to results. Hyperbolic solved: 101.964413523995 [20250519_004313.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.964 --> '100 < root < 110' --> substitute 100 [20250519_004313.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: -2.13402763663736 [20250519_004313.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.134 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.5081558897243 [20250519_004313.]: Samplename: 12.5 Root: 10.508 --> Root in between the borders! Added to results. Hyperbolic solved: 26.9163121098627 [20250519_004313.]: Samplename: 25 Root: 26.916 --> Root in between the borders! Added to results. Hyperbolic solved: 36.8332915301825 [20250519_004313.]: Samplename: 37.5 Root: 36.833 --> Root in between the borders! Added to results. Hyperbolic solved: 52.00955 [20250519_004313.]: Samplename: 50 Root: 52.01 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8927781893359 [20250519_004313.]: Samplename: 62.5 Root: 64.893 --> Root in between the borders! Added to results. Hyperbolic solved: 74.566810799042 [20250519_004313.]: Samplename: 75 Root: 74.567 --> Root in between the borders! Added to results. Hyperbolic solved: 84.5291854856128 [20250519_004313.]: Samplename: 87.5 Root: 84.529 --> Root in between the borders! Added to results. Hyperbolic solved: 101.046823491232 [20250519_004313.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.047 --> '100 < root < 110' --> substitute 100 [20250519_004313.]: Solving hyperbolic regression for row_means Hyperbolic solved: 0.290936038655697 [20250519_004313.]: Samplename: 0 Root: 0.291 --> Root in between the borders! Added to results. Hyperbolic solved: 11.0412364555656 [20250519_004313.]: Samplename: 12.5 Root: 11.041 --> Root in between the borders! Added to results. Hyperbolic solved: 25.408146358228 [20250519_004313.]: Samplename: 25 Root: 25.408 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5243683648753 [20250519_004313.]: Samplename: 37.5 Root: 36.524 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7348788337891 [20250519_004313.]: Samplename: 50 Root: 50.735 --> Root in between the borders! Added to results. Hyperbolic solved: 65.3135169308955 [20250519_004313.]: Samplename: 62.5 Root: 65.314 --> Root in between the borders! Added to results. Hyperbolic solved: 75.5342663138126 [20250519_004313.]: Samplename: 75 Root: 75.534 --> Root in between the borders! Added to results. Hyperbolic solved: 83.2411177153911 [20250519_004313.]: Samplename: 87.5 Root: 83.241 --> Root in between the borders! Added to results. Hyperbolic solved: 101.666935931185 [20250519_004313.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.667 --> '100 < root < 110' --> substitute 100 [20250519_004313.]: ### Starting with regression calculations ### [20250519_004313.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_004313.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.1698391693126, 24.4781729791561, 38.1730159074047, 52.3348987967717, 65.4582305966058, 75.0090269722885, 81.527135738891, 100)c(0, 0.330160830687401, 0.521827020843901, 0.673015907404697, 2.3348987967717, 2.9582305966058, 0.00902697228849547, 5.972864261109, 0)c(NA, 2.64128664549921, 2.0873080833756, 1.79470908641252, 4.66979759354339, 4.73316895456928, 0.0120359630513273, 6.82613058412457, 0) [20250519_004313.]: Logging df_agg: CpG#1 [20250519_004313.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004313.]: c(0, 12.1698391693126, 24.4781729791561, 38.1730159074047, 52.3348987967717, 65.4582305966058, 75.0090269722885, 81.527135738891, 100)[20250519_004313.]: c(0, 0.330160830687401, 0.521827020843901, 0.673015907404697, 2.3348987967717, 2.9582305966058, 0.00902697228849547, 5.972864261109, 0)[20250519_004313.]: c(NA, 2.64128664549921, 2.0873080833756, 1.79470908641252, 4.66979759354339, 4.73316895456928, 0.0120359630513273, 6.82613058412457, 0) [20250519_004313.]: Entered 'hyperbolic_regression'-Function [20250519_004313.]: 'hyperbolic_regression': minmax = FALSE [20250519_004313.]: Entered 'cubic_regression'-Function [20250519_004313.]: 'cubic_regression': minmax = FALSE [20250519_004313.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.13663087266051, 11.4130277963576, 26.1741025868157, 35.1051721736724, 47.6856613240114, 67.1442585354696, 75.7646957109439, 84.4056634834815, 100)c(1.13663087266051, 1.0869722036424, 1.1741025868157, 2.3948278263276, 2.3143386759886, 4.6442585354696, 0.764695710943897, 3.0943365165185, 0)c(NA, 8.6957776291392, 4.6964103472628, 6.3862075368736, 4.6286773519772, 7.43081365675137, 1.01959428125853, 3.53638459030685, 0) [20250519_004313.]: Logging df_agg: CpG#2 [20250519_004314.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004314.]: c(1.13663087266051, 11.4130277963576, 26.1741025868157, 35.1051721736724, 47.6856613240114, 67.1442585354696, 75.7646957109439, 84.4056634834815, 100)[20250519_004314.]: c(1.13663087266051, 1.0869722036424, 1.1741025868157, 2.3948278263276, 2.3143386759886, 4.6442585354696, 0.764695710943897, 3.0943365165185, 0)[20250519_004314.]: c(NA, 8.6957776291392, 4.6964103472628, 6.3862075368736, 4.6286773519772, 7.43081365675137, 1.01959428125853, 3.53638459030685, 0) [20250519_004314.]: Entered 'hyperbolic_regression'-Function [20250519_004314.]: 'hyperbolic_regression': minmax = FALSE [20250519_004314.]: Entered 'cubic_regression'-Function [20250519_004314.]: 'cubic_regression': minmax = FALSE [20250519_004314.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.512348257854635, 10.7523476241811, 25.5218062516799, 36.5269315729339, 50.7907738274884, 64.8684489988589, 77.5522101073979, 80.4372474691229, 100)c(0.512348257854635, 1.7476523758189, 0.521806251679902, 0.973068427066103, 0.790773827488401, 2.3684489988589, 2.5522101073979, 7.06275253087711, 0)c(NA, 13.9812190065512, 2.08722500671961, 2.59484913884294, 1.5815476549768, 3.78951839817423, 3.40294680986386, 8.07171717814526, 0) [20250519_004314.]: Logging df_agg: CpG#3 [20250519_004314.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004314.]: c(0.512348257854635, 10.7523476241811, 25.5218062516799, 36.5269315729339, 50.7907738274884, 64.8684489988589, 77.5522101073979, 80.4372474691229, 100)[20250519_004314.]: c(0.512348257854635, 1.7476523758189, 0.521806251679902, 0.973068427066103, 0.790773827488401, 2.3684489988589, 2.5522101073979, 7.06275253087711, 0)[20250519_004314.]: c(NA, 13.9812190065512, 2.08722500671961, 2.59484913884294, 1.5815476549768, 3.78951839817423, 3.40294680986386, 8.07171717814526, 0) [20250519_004314.]: Entered 'hyperbolic_regression'-Function [20250519_004314.]: 'hyperbolic_regression': minmax = FALSE [20250519_004314.]: Entered 'cubic_regression'-Function [20250519_004314.]: 'cubic_regression': minmax = FALSE [20250519_004314.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.4933760051342, 24.2684789512555, 38.0816229393195, 48.5842089589187, 67.6720983549562, 74.1547759694059, 82.8820147905386, 100)c(0, 0.00662399486579979, 0.731521048744501, 0.581622939319502, 1.4157910410813, 5.1720983549562, 0.845224030594096, 4.61798520946139, 0)c(NA, 0.0529919589263983, 2.926084194978, 1.550994504852, 2.8315820821626, 8.27535736792993, 1.12696537412546, 5.27769738224159, 0) [20250519_004314.]: Logging df_agg: CpG#4 [20250519_004314.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004314.]: c(0, 12.4933760051342, 24.2684789512555, 38.0816229393195, 48.5842089589187, 67.6720983549562, 74.1547759694059, 82.8820147905386, 100)[20250519_004314.]: c(0, 0.00662399486579979, 0.731521048744501, 0.581622939319502, 1.4157910410813, 5.1720983549562, 0.845224030594096, 4.61798520946139, 0)[20250519_004314.]: c(NA, 0.0529919589263983, 2.926084194978, 1.550994504852, 2.8315820821626, 8.27535736792993, 1.12696537412546, 5.27769738224159, 0) [20250519_004314.]: Entered 'hyperbolic_regression'-Function [20250519_004314.]: 'hyperbolic_regression': minmax = FALSE [20250519_004314.]: Entered 'cubic_regression'-Function [20250519_004314.]: 'cubic_regression': minmax = FALSE [20250519_004314.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.41558040143479, 10.1649584830834, 23.9830670020905, 37.2773406385708, 50.8659103346102, 62.4341926937382, 76.3914832329149, 86.1597399215782, 100)c(2.41558040143479, 2.3350415169166, 1.0169329979095, 0.222659361429201, 0.865910334610199, 0.065807306261803, 1.39148323291489, 1.34026007842181, 0)c(NA, 18.6803321353328, 4.067731991638, 0.593758297144537, 1.7318206692204, 0.105291690018885, 1.85531097721986, 1.53172580391064, 0) [20250519_004314.]: Logging df_agg: CpG#5 [20250519_004314.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004314.]: c(2.41558040143479, 10.1649584830834, 23.9830670020905, 37.2773406385708, 50.8659103346102, 62.4341926937382, 76.3914832329149, 86.1597399215782, 100)[20250519_004314.]: c(2.41558040143479, 2.3350415169166, 1.0169329979095, 0.222659361429201, 0.865910334610199, 0.065807306261803, 1.39148323291489, 1.34026007842181, 0)[20250519_004314.]: c(NA, 18.6803321353328, 4.067731991638, 0.593758297144537, 1.7318206692204, 0.105291690018885, 1.85531097721986, 1.53172580391064, 0) [20250519_004314.]: Entered 'hyperbolic_regression'-Function [20250519_004314.]: 'hyperbolic_regression': minmax = FALSE [20250519_004314.]: Entered 'cubic_regression'-Function [20250519_004314.]: 'cubic_regression': minmax = FALSE [20250519_004314.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.138179963459196, 11.8635989635202, 26.5108198460409, 35.3206004021833, 50.0571987473308, 64.9604436607063, 73.6690699266294, 87.1267946879604, 100)c(0.138179963459196, 0.6364010364798, 1.5108198460409, 2.1793995978167, 0.0571987473308013, 2.4604436607063, 1.3309300733706, 0.373205312039602, 0)c(NA, 5.0912082918384, 6.0432793841636, 5.81173226084453, 0.114397494661603, 3.93670985713009, 1.77457343116079, 0.426520356616688, 0) [20250519_004314.]: Logging df_agg: CpG#6 [20250519_004314.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004314.]: c(0.138179963459196, 11.8635989635202, 26.5108198460409, 35.3206004021833, 50.0571987473308, 64.9604436607063, 73.6690699266294, 87.1267946879604, 100)[20250519_004314.]: c(0.138179963459196, 0.6364010364798, 1.5108198460409, 2.1793995978167, 0.0571987473308013, 2.4604436607063, 1.3309300733706, 0.373205312039602, 0)[20250519_004314.]: c(NA, 5.0912082918384, 6.0432793841636, 5.81173226084453, 0.114397494661603, 3.93670985713009, 1.77457343116079, 0.426520356616688, 0) [20250519_004314.]: Entered 'hyperbolic_regression'-Function [20250519_004314.]: 'hyperbolic_regression': minmax = FALSE [20250519_004315.]: Entered 'cubic_regression'-Function [20250519_004315.]: 'cubic_regression': minmax = FALSE [20250519_004315.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.199366215418, 24.595295361327, 37.8312095633391, 53.5590896582852, 65.9367389282431, 75.736370152841, 79.433089359524, 100)c(0, 2.300633784582, 0.404704638673, 0.331209563339101, 3.5590896582852, 3.4367389282431, 0.736370152841005, 8.066910640476, 0)c(NA, 18.405070276656, 1.618818554692, 0.883225502237603, 7.11817931657041, 5.49878228518896, 0.981826870454673, 9.21932644625828, 0) [20250519_004315.]: Logging df_agg: CpG#7 [20250519_004315.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004315.]: c(0, 10.199366215418, 24.595295361327, 37.8312095633391, 53.5590896582852, 65.9367389282431, 75.736370152841, 79.433089359524, 100)[20250519_004315.]: c(0, 2.300633784582, 0.404704638673, 0.331209563339101, 3.5590896582852, 3.4367389282431, 0.736370152841005, 8.066910640476, 0)[20250519_004315.]: c(NA, 18.405070276656, 1.618818554692, 0.883225502237603, 7.11817931657041, 5.49878228518896, 0.981826870454673, 9.21932644625828, 0) [20250519_004315.]: Entered 'hyperbolic_regression'-Function [20250519_004315.]: 'hyperbolic_regression': minmax = FALSE [20250519_004315.]: Entered 'cubic_regression'-Function [20250519_004315.]: 'cubic_regression': minmax = FALSE [20250519_004315.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.80067549526643, 9.27534686003087, 25.4762624089952, 34.0122098045585, 51.784270967861, 64.6732380258789, 78.4327054483192, 81.3427308951795, 100)c(2.80067549526643, 3.22465313996913, 0.476262408995201, 3.4877901954415, 1.784270967861, 2.1732380258789, 3.4327054483192, 6.1572691048205, 0)c(NA, 25.797225119753, 1.9050496359808, 9.30077385451066, 3.568541935722, 3.47718084140624, 4.57694059775893, 7.03687897693771, 0) [20250519_004315.]: Logging df_agg: CpG#8 [20250519_004315.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004315.]: c(2.80067549526643, 9.27534686003087, 25.4762624089952, 34.0122098045585, 51.784270967861, 64.6732380258789, 78.4327054483192, 81.3427308951795, 100)[20250519_004315.]: c(2.80067549526643, 3.22465313996913, 0.476262408995201, 3.4877901954415, 1.784270967861, 2.1732380258789, 3.4327054483192, 6.1572691048205, 0)[20250519_004315.]: c(NA, 25.797225119753, 1.9050496359808, 9.30077385451066, 3.568541935722, 3.47718084140624, 4.57694059775893, 7.03687897693771, 0) [20250519_004315.]: Entered 'hyperbolic_regression'-Function [20250519_004315.]: 'hyperbolic_regression': minmax = FALSE [20250519_004315.]: Entered 'cubic_regression'-Function [20250519_004315.]: 'cubic_regression': minmax = FALSE [20250519_004315.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.5081558897243, 26.9163121098627, 36.8332915301825, 52.00955, 64.8927781893359, 74.566810799042, 84.5291854856128, 100)c(0, 1.9918441102757, 1.9163121098627, 0.6667084698175, 2.00955, 2.3927781893359, 0.433189200957997, 2.9708145143872, 0)c(NA, 15.9347528822056, 7.66524843945081, 1.77788925284667, 4.01909999999999, 3.82844510293744, 0.57758560127733, 3.39521658787109, 0) [20250519_004315.]: Logging df_agg: CpG#9 [20250519_004315.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004315.]: c(0, 10.5081558897243, 26.9163121098627, 36.8332915301825, 52.00955, 64.8927781893359, 74.566810799042, 84.5291854856128, 100)[20250519_004315.]: c(0, 1.9918441102757, 1.9163121098627, 0.6667084698175, 2.00955, 2.3927781893359, 0.433189200957997, 2.9708145143872, 0)[20250519_004315.]: c(NA, 15.9347528822056, 7.66524843945081, 1.77788925284667, 4.01909999999999, 3.82844510293744, 0.57758560127733, 3.39521658787109, 0) [20250519_004315.]: Entered 'hyperbolic_regression'-Function [20250519_004315.]: 'hyperbolic_regression': minmax = FALSE [20250519_004315.]: Entered 'cubic_regression'-Function [20250519_004315.]: 'cubic_regression': minmax = FALSE [20250519_004315.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.290936038655697, 11.0412364555656, 25.408146358228, 36.5243683648753, 50.7348788337891, 65.3135169308955, 75.5342663138126, 83.2411177153911, 100)c(0.290936038655697, 1.4587635444344, 0.408146358227999, 0.975631635124699, 0.734878833789097, 2.8135169308955, 0.534266313812594, 4.2588822846089, 0)c(NA, 11.6701083554752, 1.63258543291199, 2.60168436033253, 1.46975766757819, 4.5016270894328, 0.712355085083459, 4.86729403955303, 0) [20250519_004315.]: Logging df_agg: row_means [20250519_004315.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004315.]: c(0.290936038655697, 11.0412364555656, 25.408146358228, 36.5243683648753, 50.7348788337891, 65.3135169308955, 75.5342663138126, 83.2411177153911, 100)[20250519_004315.]: c(0.290936038655697, 1.4587635444344, 0.408146358227999, 0.975631635124699, 0.734878833789097, 2.8135169308955, 0.534266313812594, 4.2588822846089, 0)[20250519_004315.]: c(NA, 11.6701083554752, 1.63258543291199, 2.60168436033253, 1.46975766757819, 4.5016270894328, 0.712355085083459, 4.86729403955303, 0) [20250519_004315.]: Entered 'hyperbolic_regression'-Function [20250519_004315.]: 'hyperbolic_regression': minmax = FALSE [20250519_004316.]: Entered 'cubic_regression'-Function [20250519_004316.]: 'cubic_regression': minmax = FALSE [20250519_004316.]: Entered 'solving_equations'-Function [20250519_004316.]: Solving cubic regression for CpG#1 Coefficients: -1.036Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20250519_004316.]: Samplename: 0 Root: 1.335 --> Root in between the borders! Added to results. Coefficients: -8.34133333333333Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20250519_004316.]: Samplename: 12.5 Root: 11.684 --> Root in between the borders! Added to results. Coefficients: -15.388Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20250519_004316.]: Samplename: 25 Root: 24.056 --> Root in between the borders! Added to results. Coefficients: -24.28Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20250519_004316.]: Samplename: 37.5 Root: 50.441 --> Root in between the borders! Added to results. Coefficients: -34.9005Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20250519_004316.]: Samplename: 50 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -46.354Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20250519_004316.]: Samplename: 62.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -55.893Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20250519_004316.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -63.098Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20250519_004316.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -91.046Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20250519_004316.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20250519_004316.]: Solving cubic regression for CpG#2 Coefficients: -0.283000000000001Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20250519_004316.]: Samplename: 0 Root: 0.548 --> Root in between the borders! Added to results. Coefficients: -6.33966666666667Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20250519_004316.]: Samplename: 12.5 Root: 11.535 --> Root in between the borders! Added to results. Coefficients: -15.939Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20250519_004316.]: Samplename: 25 Root: 26.774 --> Root in between the borders! Added to results. Coefficients: -22.337Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20250519_004316.]: Samplename: 37.5 Root: 35.875 --> Root in between the borders! Added to results. Coefficients: -32.228Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20250519_004316.]: Samplename: 50 Root: 48.74 --> Root in between the borders! Added to results. Coefficients: -49.963Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20250519_004316.]: Samplename: 62.5 Root: 69.156 --> Root in between the borders! Added to results. Coefficients: -58.9655Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20250519_004316.]: Samplename: 75 Root: 78.552 --> Root in between the borders! Added to results. Coefficients: -68.8363333333333Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20250519_004316.]: Samplename: 87.5 Root: 88.272 --> Root in between the borders! Added to results. Coefficients: -90.577Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20250519_004316.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 107.97 --> '100 < root < 110' --> substitute 100 [20250519_004316.]: Solving cubic regression for CpG#3 Coefficients: -0.903Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20250519_004316.]: Samplename: 0 Root: 1.441 --> Root in between the borders! Added to results. Coefficients: -6.573Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20250519_004316.]: Samplename: 12.5 Root: 10.647 --> Root in between the borders! Added to results. Coefficients: -15.429Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20250519_004316.]: Samplename: 25 Root: 25.613 --> Root in between the borders! Added to results. Coefficients: -22.613Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20250519_004316.]: Samplename: 37.5 Root: 38.35 --> Root in between the borders! Added to results. Coefficients: -32.7755Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20250519_004316.]: Samplename: 50 Root: 57.445 --> Root in between the borders! Added to results. Coefficients: -43.889Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20250519_004316.]: Samplename: 62.5 Root: 80.105 --> Root in between the borders! Added to results. Coefficients: -54.9755Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20250519_004316.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 105.145 --> '100 < root < 110' --> substitute 100 Coefficients: -57.6563333333333Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20250519_004316.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -80.665Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20250519_004316.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20250519_004316.]: Solving cubic regression for CpG#4 Coefficients: -0.597Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20250519_004316.]: Samplename: 0 Root: 0.859 --> Root in between the borders! Added to results. Coefficients: -8.25233333333333Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20250519_004316.]: Samplename: 12.5 Root: 12.272 --> Root in between the borders! Added to results. Coefficients: -15.803Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20250519_004316.]: Samplename: 25 Root: 24.378 --> Root in between the borders! Added to results. Coefficients: -25.527Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20250519_004316.]: Samplename: 37.5 Root: 41.587 --> Root in between the borders! Added to results. Coefficients: -33.6365Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20250519_004316.]: Samplename: 50 Root: 57.868 --> Root in between the borders! Added to results. Coefficients: -50.255Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20250519_004316.]: Samplename: 62.5 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.893 --> '100 < root < 110' --> substitute 100 Coefficients: -56.539Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20250519_004316.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -65.5923333333333Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20250519_004316.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -88.321Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20250519_004316.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20250519_004316.]: Solving cubic regression for CpG#5 Coefficients: -0.624000000000001Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20250519_004316.]: Samplename: 0 Root: 1.459 --> Root in between the borders! Added to results. Coefficients: -4.768Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20250519_004316.]: Samplename: 12.5 Root: 10.294 --> Root in between the borders! Added to results. Coefficients: -12.726Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20250519_004316.]: Samplename: 25 Root: 24.477 --> Root in between the borders! Added to results. Coefficients: -21.16Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20250519_004316.]: Samplename: 37.5 Root: 37.097 --> Root in between the borders! Added to results. Coefficients: -30.6955Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20250519_004316.]: Samplename: 50 Root: 49.507 --> Root in between the borders! Added to results. Coefficients: -39.658Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20250519_004316.]: Samplename: 62.5 Root: 59.931 --> Root in between the borders! Added to results. Coefficients: -51.683Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20250519_004316.]: Samplename: 75 Root: 72.563 --> Root in between the borders! Added to results. Coefficients: -61.0146666666667Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20250519_004316.]: Samplename: 87.5 Root: 81.549 --> Root in between the borders! Added to results. Coefficients: -76.07Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20250519_004316.]: Samplename: 100 Root: 94.898 --> Root in between the borders! Added to results. [20250519_004316.]: Solving cubic regression for CpG#6 Coefficients: -0.196000000000001Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20250519_004316.]: Samplename: 0 Root: 0.349 --> Root in between the borders! Added to results. Coefficients: -6.73866666666667Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20250519_004316.]: Samplename: 12.5 Root: 11.765 --> Root in between the borders! Added to results. Coefficients: -15.888Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20250519_004316.]: Samplename: 25 Root: 27.02 --> Root in between the borders! Added to results. Coefficients: -22Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20250519_004316.]: Samplename: 37.5 Root: 36.802 --> Root in between the borders! Added to results. Coefficients: -33.4445Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20250519_004316.]: Samplename: 50 Root: 54.35 --> Root in between the borders! Added to results. Coefficients: -46.9Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20250519_004316.]: Samplename: 62.5 Root: 73.873 --> Root in between the borders! Added to results. Coefficients: -55.832Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20250519_004316.]: Samplename: 75 Root: 86.259 --> Root in between the borders! Added to results. Coefficients: -71.5453333333333Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20250519_004316.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 107.089 --> '100 < root < 110' --> substitute 100 Coefficients: -89.656Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20250519_004316.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20250519_004316.]: Solving cubic regression for CpG#7 Coefficients: -1.215Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20250519_004316.]: Samplename: 0 Root: 2.126 --> Root in between the borders! Added to results. Coefficients: -5.39566666666667Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20250519_004316.]: Samplename: 12.5 Root: 9.991 --> Root in between the borders! Added to results. Coefficients: -11.265Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20250519_004316.]: Samplename: 25 Root: 23.104 --> Root in between the borders! Added to results. Coefficients: -17.451Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20250519_004316.]: Samplename: 37.5 Root: 42.609 --> Root in between the borders! Added to results. Coefficients: -26.0315Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20250519_004316.]: Samplename: 50 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -33.965Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20250519_004316.]: Samplename: 62.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -41.169Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20250519_004316.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -44.1356666666667Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20250519_004316.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -67.223Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20250519_004316.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20250519_004316.]: Solving cubic regression for CpG#8 Coefficients: -1.096Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20250519_004316.]: Samplename: 0 Root: 2.018 --> Root in between the borders! Added to results. Coefficients: -5.44666666666667Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20250519_004316.]: Samplename: 12.5 Root: 9.505 --> Root in between the borders! Added to results. Coefficients: -16.93Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20250519_004316.]: Samplename: 25 Root: 26.424 --> Root in between the borders! Added to results. Coefficients: -23.35Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20250519_004316.]: Samplename: 37.5 Root: 34.662 --> Root in between the borders! Added to results. Coefficients: -37.625Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20250519_004316.]: Samplename: 50 Root: 50.932 --> Root in between the borders! Added to results. Coefficients: -48.826Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20250519_004316.]: Samplename: 62.5 Root: 62.271 --> Root in between the borders! Added to results. Coefficients: -61.6675Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20250519_004316.]: Samplename: 75 Root: 74.154 --> Root in between the borders! Added to results. Coefficients: -64.51Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20250519_004316.]: Samplename: 87.5 Root: 76.651 --> Root in between the borders! Added to results. Coefficients: -86.06Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20250519_004316.]: Samplename: 100 Root: 94.331 --> Root in between the borders! Added to results. [20250519_004316.]: Solving cubic regression for CpG#9 Coefficients: -0.99Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20250519_004316.]: Samplename: 0 Root: 1.474 --> Root in between the borders! Added to results. Coefficients: -6.396Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20250519_004316.]: Samplename: 12.5 Root: 10.183 --> Root in between the borders! Added to results. Coefficients: -14.706Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20250519_004316.]: Samplename: 25 Root: 27.044 --> Root in between the borders! Added to results. Coefficients: -20.624Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20250519_004316.]: Samplename: 37.5 Root: 45.863 --> Root in between the borders! Added to results. Coefficients: -31.396Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20250519_004316.]: Samplename: 50 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -42.686Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20250519_004316.]: Samplename: 62.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -52.9035Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20250519_004316.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -65.4926666666667Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20250519_004316.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -92.99Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20250519_004316.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20250519_004316.]: Solving cubic regression for row_means Coefficients: -0.771111111111111Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20250519_004316.]: Samplename: 0 Root: 1.288 --> Root in between the borders! Added to results. Coefficients: -6.47237037037037Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20250519_004316.]: Samplename: 12.5 Root: 10.989 --> Root in between the borders! Added to results. Coefficients: -14.8971111111111Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20250519_004316.]: Samplename: 25 Root: 25.951 --> Root in between the borders! Added to results. Coefficients: -22.1491111111111Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20250519_004316.]: Samplename: 37.5 Root: 39.518 --> Root in between the borders! Added to results. Coefficients: -32.5258888888889Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20250519_004316.]: Samplename: 50 Root: 60.262 --> Root in between the borders! Added to results. Coefficients: -44.7217777777778Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20250519_004316.]: Samplename: 62.5 Root: 87.213 --> Root in between the borders! Added to results. Coefficients: -54.4031111111111Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20250519_004316.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -62.4312592592593Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20250519_004316.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -84.7342222222222Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20250519_004316.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20250519_004316.]: ### Starting with regression calculations ### [20250519_004316.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_004317.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5)c(1.3350694679926, 11.6842678967181, 24.056490215457, 50.4411280642076)c(1.3350694679926, 0.815732103281899, 0.943509784543, 12.9411280642076)c(NA, 6.52585682625519, 3.774039138172, 34.5096748378869) [20250519_004317.]: Logging df_agg: CpG#1 [20250519_004317.]: c(0, 12.5, 25, 37.5)[20250519_004317.]: c(1.3350694679926, 11.6842678967181, 24.056490215457, 50.4411280642076)[20250519_004317.]: c(1.3350694679926, 0.815732103281899, 0.943509784543, 12.9411280642076)[20250519_004317.]: c(NA, 6.52585682625519, 3.774039138172, 34.5096748378869) [20250519_004317.]: Entered 'hyperbolic_regression'-Function [20250519_004317.]: 'hyperbolic_regression': minmax = FALSE [20250519_004317.]: Entered 'cubic_regression'-Function [20250519_004317.]: 'cubic_regression': minmax = FALSE [20250519_004317.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.547766708541827, 11.5349537694669, 26.7737761718445, 35.875449331591, 48.7401699196391, 69.1560032174257, 78.551969470104, 88.2723976336729, 100)c(0.547766708541827, 0.965046230533099, 1.7737761718445, 1.624550668409, 1.2598300803609, 6.65600321742571, 3.551969470104, 0.772397633672895, 0)c(NA, 7.72036984426479, 7.095104687378, 4.33213511575733, 2.5196601607218, 10.6496051478811, 4.735959293472, 0.882740152769023, 0) [20250519_004317.]: Logging df_agg: CpG#2 [20250519_004317.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004317.]: c(0.547766708541827, 11.5349537694669, 26.7737761718445, 35.875449331591, 48.7401699196391, 69.1560032174257, 78.551969470104, 88.2723976336729, 100)[20250519_004317.]: c(0.547766708541827, 0.965046230533099, 1.7737761718445, 1.624550668409, 1.2598300803609, 6.65600321742571, 3.551969470104, 0.772397633672895, 0)[20250519_004317.]: c(NA, 7.72036984426479, 7.095104687378, 4.33213511575733, 2.5196601607218, 10.6496051478811, 4.735959293472, 0.882740152769023, 0) [20250519_004317.]: Entered 'hyperbolic_regression'-Function [20250519_004317.]: 'hyperbolic_regression': minmax = FALSE [20250519_004317.]: Entered 'cubic_regression'-Function [20250519_004317.]: 'cubic_regression': minmax = FALSE [20250519_004317.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75)c(1.44120553086336, 10.6470702300701, 25.6131070939596, 38.3498594232174, 57.444937683935, 80.104724288839, 100)c(1.44120553086336, 1.8529297699299, 0.613107093959599, 0.849859423217403, 7.444937683935, 17.604724288839, 25)c(NA, 14.8234381594392, 2.45242837583839, 2.26629179524641, 14.88987536787, 28.1675588621424, 33.3333333333333) [20250519_004317.]: Logging df_agg: CpG#3 [20250519_004317.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75)[20250519_004317.]: c(1.44120553086336, 10.6470702300701, 25.6131070939596, 38.3498594232174, 57.444937683935, 80.104724288839, 100)[20250519_004317.]: c(1.44120553086336, 1.8529297699299, 0.613107093959599, 0.849859423217403, 7.444937683935, 17.604724288839, 25)[20250519_004317.]: c(NA, 14.8234381594392, 2.45242837583839, 2.26629179524641, 14.88987536787, 28.1675588621424, 33.3333333333333) [20250519_004317.]: Entered 'hyperbolic_regression'-Function [20250519_004317.]: 'hyperbolic_regression': minmax = FALSE [20250519_004318.]: Entered 'cubic_regression'-Function [20250519_004318.]: 'cubic_regression': minmax = FALSE [20250519_004318.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5)c(0.858643525693588, 12.2719279243844, 24.3781797668421, 41.5866572861968, 57.86781708526, 100)c(0.858643525693588, 0.2280720756156, 0.621820233157901, 4.0866572861968, 7.86781708526, 37.5)c(NA, 1.8245766049248, 2.48728093263161, 10.8977527631915, 15.73563417052, 60) [20250519_004318.]: Logging df_agg: CpG#4 [20250519_004318.]: c(0, 12.5, 25, 37.5, 50, 62.5)[20250519_004318.]: c(0.858643525693588, 12.2719279243844, 24.3781797668421, 41.5866572861968, 57.86781708526, 100)[20250519_004318.]: c(0.858643525693588, 0.2280720756156, 0.621820233157901, 4.0866572861968, 7.86781708526, 37.5)[20250519_004318.]: c(NA, 1.8245766049248, 2.48728093263161, 10.8977527631915, 15.73563417052, 60) [20250519_004318.]: Entered 'hyperbolic_regression'-Function [20250519_004318.]: 'hyperbolic_regression': minmax = FALSE [20250519_004318.]: Entered 'cubic_regression'-Function [20250519_004318.]: 'cubic_regression': minmax = FALSE [20250519_004318.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.45850947960108, 10.2941313684311, 24.4773429557174, 37.0974401416089, 49.5067234953282, 59.9312428933937, 72.5628584782902, 81.5490289118528, 94.898442253889)c(1.45850947960108, 2.2058686315689, 0.5226570442826, 0.402559858391101, 0.493276504671798, 2.5687571066063, 2.4371415217098, 5.9509710881472, 5.101557746111)c(NA, 17.6469490525512, 2.0906281771304, 1.0734929557096, 0.986553009343595, 4.11001137057008, 3.24952202894641, 6.80110981502537, 5.101557746111) [20250519_004318.]: Logging df_agg: CpG#5 [20250519_004318.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004318.]: c(1.45850947960108, 10.2941313684311, 24.4773429557174, 37.0974401416089, 49.5067234953282, 59.9312428933937, 72.5628584782902, 81.5490289118528, 94.898442253889)[20250519_004318.]: c(1.45850947960108, 2.2058686315689, 0.5226570442826, 0.402559858391101, 0.493276504671798, 2.5687571066063, 2.4371415217098, 5.9509710881472, 5.101557746111)[20250519_004318.]: c(NA, 17.6469490525512, 2.0906281771304, 1.0734929557096, 0.986553009343595, 4.11001137057008, 3.24952202894641, 6.80110981502537, 5.101557746111) [20250519_004318.]: Entered 'hyperbolic_regression'-Function [20250519_004318.]: 'hyperbolic_regression': minmax = FALSE [20250519_004318.]: Entered 'cubic_regression'-Function [20250519_004318.]: 'cubic_regression': minmax = FALSE [20250519_004318.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)c(0.349158802372062, 11.7651478823068, 27.019511576095, 36.8015127603397, 54.3503397041729, 73.8730379134395, 86.2591171327579, 100)c(0.349158802372062, 0.734852117693199, 2.019511576095, 0.698487239660302, 4.3503397041729, 11.3730379134395, 11.2591171327579, 12.5)c(NA, 5.87881694154559, 8.07804630438, 1.86263263909414, 8.70067940834581, 18.1968606615032, 15.0121561770105, 14.2857142857143) [20250519_004318.]: Logging df_agg: CpG#6 [20250519_004318.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)[20250519_004318.]: c(0.349158802372062, 11.7651478823068, 27.019511576095, 36.8015127603397, 54.3503397041729, 73.8730379134395, 86.2591171327579, 100)[20250519_004318.]: c(0.349158802372062, 0.734852117693199, 2.019511576095, 0.698487239660302, 4.3503397041729, 11.3730379134395, 11.2591171327579, 12.5)[20250519_004318.]: c(NA, 5.87881694154559, 8.07804630438, 1.86263263909414, 8.70067940834581, 18.1968606615032, 15.0121561770105, 14.2857142857143) [20250519_004318.]: Entered 'hyperbolic_regression'-Function [20250519_004318.]: 'hyperbolic_regression': minmax = FALSE [20250519_004318.]: Entered 'cubic_regression'-Function [20250519_004318.]: 'cubic_regression': minmax = FALSE [20250519_004318.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5)c(2.12599911899281, 9.99133393413891, 23.1036438590861, 42.6085296447297)c(2.12599911899281, 2.50866606586109, 1.8963561409139, 5.1085296447297)c(NA, 20.0693285268887, 7.58542456365559, 13.6227457192792) [20250519_004318.]: Logging df_agg: CpG#7 [20250519_004318.]: c(0, 12.5, 25, 37.5)[20250519_004318.]: c(2.12599911899281, 9.99133393413891, 23.1036438590861, 42.6085296447297)[20250519_004318.]: c(2.12599911899281, 2.50866606586109, 1.8963561409139, 5.1085296447297)[20250519_004318.]: c(NA, 20.0693285268887, 7.58542456365559, 13.6227457192792) [20250519_004318.]: Entered 'hyperbolic_regression'-Function [20250519_004318.]: 'hyperbolic_regression': minmax = FALSE [20250519_004319.]: Entered 'cubic_regression'-Function [20250519_004319.]: 'cubic_regression': minmax = FALSE [20250519_004319.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.0181464298155, 9.50518258466437, 26.4243334649289, 34.6620160335629, 50.9321119457565, 62.2712954366094, 74.1538645100711, 76.6511844577497, 94.3306625091067)c(2.0181464298155, 2.99481741533563, 1.4243334649289, 2.8379839664371, 0.932111945756503, 0.2287045633906, 0.846135489928898, 10.8488155422503, 5.6693374908933)c(NA, 23.958539322685, 5.6973338597156, 7.56795724383227, 1.86422389151301, 0.365927301424961, 1.12818065323853, 12.3986463340004, 5.6693374908933) [20250519_004319.]: Logging df_agg: CpG#8 [20250519_004319.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004319.]: c(2.0181464298155, 9.50518258466437, 26.4243334649289, 34.6620160335629, 50.9321119457565, 62.2712954366094, 74.1538645100711, 76.6511844577497, 94.3306625091067)[20250519_004319.]: c(2.0181464298155, 2.99481741533563, 1.4243334649289, 2.8379839664371, 0.932111945756503, 0.2287045633906, 0.846135489928898, 10.8488155422503, 5.6693374908933)[20250519_004319.]: c(NA, 23.958539322685, 5.6973338597156, 7.56795724383227, 1.86422389151301, 0.365927301424961, 1.12818065323853, 12.3986463340004, 5.6693374908933) [20250519_004319.]: Entered 'hyperbolic_regression'-Function [20250519_004319.]: 'hyperbolic_regression': minmax = FALSE [20250519_004319.]: Entered 'cubic_regression'-Function [20250519_004319.]: 'cubic_regression': minmax = FALSE [20250519_004319.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5)c(1.47402616445883, 10.1833646164297, 27.0440334834678, 45.8634394698265)c(1.47402616445883, 2.3166353835703, 2.0440334834678, 8.3634394698265)c(NA, 18.5330830685624, 8.1761339338712, 22.3025052528707) [20250519_004319.]: Logging df_agg: CpG#9 [20250519_004319.]: c(0, 12.5, 25, 37.5)[20250519_004319.]: c(1.47402616445883, 10.1833646164297, 27.0440334834678, 45.8634394698265)[20250519_004319.]: c(1.47402616445883, 2.3166353835703, 2.0440334834678, 8.3634394698265)[20250519_004319.]: c(NA, 18.5330830685624, 8.1761339338712, 22.3025052528707) [20250519_004319.]: Entered 'hyperbolic_regression'-Function [20250519_004319.]: 'hyperbolic_regression': minmax = FALSE [20250519_004319.]: Entered 'cubic_regression'-Function [20250519_004319.]: 'cubic_regression': minmax = FALSE [20250519_004319.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5)c(1.28794987665983, 10.9885302801468, 25.950937077156, 39.5179682033924, 60.2624119769469, 87.2132000752344)c(1.28794987665983, 1.5114697198532, 0.950937077155999, 2.0179682033924, 10.2624119769469, 24.7132000752344)c(NA, 12.0917577588256, 3.80374830862399, 5.38124854237973, 20.5248239538938, 39.541120120375) [20250519_004319.]: Logging df_agg: row_means [20250519_004319.]: c(0, 12.5, 25, 37.5, 50, 62.5)[20250519_004319.]: c(1.28794987665983, 10.9885302801468, 25.950937077156, 39.5179682033924, 60.2624119769469, 87.2132000752344)[20250519_004319.]: c(1.28794987665983, 1.5114697198532, 0.950937077155999, 2.0179682033924, 10.2624119769469, 24.7132000752344)[20250519_004319.]: c(NA, 12.0917577588256, 3.80374830862399, 5.38124854237973, 20.5248239538938, 39.541120120375) [20250519_004319.]: Entered 'hyperbolic_regression'-Function [20250519_004319.]: 'hyperbolic_regression': minmax = FALSE [20250519_004319.]: Entered 'cubic_regression'-Function [20250519_004319.]: 'cubic_regression': minmax = FALSE [20250519_004320.]: Entered 'solving_equations'-Function [20250519_004320.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 79.8672902891399 [20250519_004320.]: Samplename: Sample#1 Root: 79.867 --> Root in between the borders! Added to results. Hyperbolic solved: 29.789995526108 [20250519_004320.]: Samplename: Sample#10 Root: 29.79 --> Root in between the borders! Added to results. Hyperbolic solved: 41.6525103283956 [20250519_004320.]: Samplename: Sample#2 Root: 41.653 --> Root in between the borders! Added to results. Hyperbolic solved: 57.4651672980431 [20250519_004320.]: Samplename: Sample#3 Root: 57.465 --> Root in between the borders! Added to results. Hyperbolic solved: 9.20070553821487 [20250519_004320.]: Samplename: Sample#4 Root: 9.201 --> Root in between the borders! Added to results. Hyperbolic solved: 21.8059429711637 [20250519_004320.]: Samplename: Sample#5 Root: 21.806 --> Root in between the borders! Added to results. Hyperbolic solved: 23.0837787089758 [20250519_004320.]: Samplename: Sample#6 Root: 23.084 --> Root in between the borders! Added to results. Hyperbolic solved: 45.5033907021902 [20250519_004320.]: Samplename: Sample#7 Root: 45.503 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6987316769908 [20250519_004320.]: Samplename: Sample#8 Root: 85.699 --> Root in between the borders! Added to results. Hyperbolic solved: -3.66512538562846 [20250519_004320.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -3.665 --> '-10 < root < 0' --> substitute 0 [20250519_004320.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 76.7230240612756 [20250519_004320.]: Samplename: Sample#1 Root: 76.723 --> Root in between the borders! Added to results. Hyperbolic solved: 30.992543297941 [20250519_004320.]: Samplename: Sample#10 Root: 30.993 --> Root in between the borders! Added to results. Hyperbolic solved: 42.6812361010669 [20250519_004320.]: Samplename: Sample#2 Root: 42.681 --> Root in between the borders! Added to results. Hyperbolic solved: 58.933980239834 [20250519_004320.]: Samplename: Sample#3 Root: 58.934 --> Root in between the borders! Added to results. Hyperbolic solved: 4.98024265777963 [20250519_004320.]: Samplename: Sample#4 Root: 4.98 --> Root in between the borders! Added to results. Hyperbolic solved: 20.2481111308688 [20250519_004320.]: Samplename: Sample#5 Root: 20.248 --> Root in between the borders! Added to results. Hyperbolic solved: 17.813386088098 [20250519_004320.]: Samplename: Sample#6 Root: 17.813 --> Root in between the borders! Added to results. Hyperbolic solved: 40.9178176381316 [20250519_004320.]: Samplename: Sample#7 Root: 40.918 --> Root in between the borders! Added to results. Hyperbolic solved: 86.0453226524415 [20250519_004320.]: Samplename: Sample#8 Root: 86.045 --> Root in between the borders! Added to results. Hyperbolic solved: 3.11582988095862 [20250519_004320.]: Samplename: Sample#9 Root: 3.116 --> Root in between the borders! Added to results. [20250519_004320.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 74.934721803959 [20250519_004320.]: Samplename: Sample#1 Root: 74.935 --> Root in between the borders! Added to results. Hyperbolic solved: 27.6843475257834 [20250519_004320.]: Samplename: Sample#10 Root: 27.684 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8518905782652 [20250519_004320.]: Samplename: Sample#2 Root: 41.852 --> Root in between the borders! Added to results. Hyperbolic solved: 55.8323554649537 [20250519_004320.]: Samplename: Sample#3 Root: 55.832 --> Root in between the borders! Added to results. Hyperbolic solved: 8.03516016616019 [20250519_004320.]: Samplename: Sample#4 Root: 8.035 --> Root in between the borders! Added to results. Hyperbolic solved: 24.1065510576898 [20250519_004320.]: Samplename: Sample#5 Root: 24.107 --> Root in between the borders! Added to results. Hyperbolic solved: 26.2418954231184 [20250519_004320.]: Samplename: Sample#6 Root: 26.242 --> Root in between the borders! Added to results. Hyperbolic solved: 44.0943580511304 [20250519_004320.]: Samplename: Sample#7 Root: 44.094 --> Root in between the borders! Added to results. Hyperbolic solved: 85.8277140531902 [20250519_004320.]: Samplename: Sample#8 Root: 85.828 --> Root in between the borders! Added to results. Hyperbolic solved: -0.666486811581751 [20250519_004320.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.666 --> '-10 < root < 0' --> substitute 0 [20250519_004320.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 76.349372671628 [20250519_004320.]: Samplename: Sample#1 Root: 76.349 --> Root in between the borders! Added to results. Hyperbolic solved: 28.2567843724913 [20250519_004320.]: Samplename: Sample#10 Root: 28.257 --> Root in between the borders! Added to results. Hyperbolic solved: 43.4088841408467 [20250519_004320.]: Samplename: Sample#2 Root: 43.409 --> Root in between the borders! Added to results. Hyperbolic solved: 58.5433972042602 [20250519_004320.]: Samplename: Sample#3 Root: 58.543 --> Root in between the borders! Added to results. Hyperbolic solved: 10.3086704325003 [20250519_004320.]: Samplename: Sample#4 Root: 10.309 --> Root in between the borders! Added to results. Hyperbolic solved: 22.182986320858 [20250519_004320.]: Samplename: Sample#5 Root: 22.183 --> Root in between the borders! Added to results. Hyperbolic solved: 27.1337081900258 [20250519_004320.]: Samplename: Sample#6 Root: 27.134 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8320127104856 [20250519_004320.]: Samplename: Sample#7 Root: 41.832 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6888498156134 [20250519_004320.]: Samplename: Sample#8 Root: 85.689 --> Root in between the borders! Added to results. Hyperbolic solved: 2.42230396811441 [20250519_004320.]: Samplename: Sample#9 Root: 2.422 --> Root in between the borders! Added to results. [20250519_004320.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 72.8126507590781 [20250519_004320.]: Samplename: Sample#1 Root: 72.813 --> Root in between the borders! Added to results. Hyperbolic solved: 26.4206774223762 [20250519_004320.]: Samplename: Sample#10 Root: 26.421 --> Root in between the borders! Added to results. Hyperbolic solved: 44.2738516315831 [20250519_004320.]: Samplename: Sample#2 Root: 44.274 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5324088748019 [20250519_004320.]: Samplename: Sample#3 Root: 53.532 --> Root in between the borders! Added to results. Hyperbolic solved: 10.0373628417333 [20250519_004320.]: Samplename: Sample#4 Root: 10.037 --> Root in between the borders! Added to results. Hyperbolic solved: 22.8732969704156 [20250519_004320.]: Samplename: Sample#5 Root: 22.873 --> Root in between the borders! Added to results. Hyperbolic solved: 25.9417756406497 [20250519_004320.]: Samplename: Sample#6 Root: 25.942 --> Root in between the borders! Added to results. Hyperbolic solved: 42.7944833740993 [20250519_004320.]: Samplename: Sample#7 Root: 42.794 --> Root in between the borders! Added to results. Hyperbolic solved: 88.8740708807914 [20250519_004320.]: Samplename: Sample#8 Root: 88.874 --> Root in between the borders! Added to results. Hyperbolic solved: 2.34646946564885 [20250519_004320.]: Samplename: Sample#9 Root: 2.346 --> Root in between the borders! Added to results. [20250519_004320.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 79.2782328649447 [20250519_004320.]: Samplename: Sample#1 Root: 79.278 --> Root in between the borders! Added to results. Hyperbolic solved: 30.2013285011234 [20250519_004320.]: Samplename: Sample#10 Root: 30.201 --> Root in between the borders! Added to results. Hyperbolic solved: 41.847545497608 [20250519_004320.]: Samplename: Sample#2 Root: 41.848 --> Root in between the borders! Added to results. Hyperbolic solved: 56.8424863688491 [20250519_004320.]: Samplename: Sample#3 Root: 56.842 --> Root in between the borders! Added to results. Hyperbolic solved: 8.87859681588532 [20250519_004320.]: Samplename: Sample#4 Root: 8.879 --> Root in between the borders! Added to results. Hyperbolic solved: 18.6902176438696 [20250519_004320.]: Samplename: Sample#5 Root: 18.69 --> Root in between the borders! Added to results. Hyperbolic solved: 29.9310083954849 [20250519_004320.]: Samplename: Sample#6 Root: 29.931 --> Root in between the borders! Added to results. Hyperbolic solved: 42.814964037839 [20250519_004320.]: Samplename: Sample#7 Root: 42.815 --> Root in between the borders! Added to results. Hyperbolic solved: 86.7503685829287 [20250519_004320.]: Samplename: Sample#8 Root: 86.75 --> Root in between the borders! Added to results. Hyperbolic solved: 1.51518138323643 [20250519_004320.]: Samplename: Sample#9 Root: 1.515 --> Root in between the borders! Added to results. [20250519_004320.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 78.2568233815803 [20250519_004320.]: Samplename: Sample#1 Root: 78.257 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4888594923858 [20250519_004320.]: Samplename: Sample#10 Root: 25.489 --> Root in between the borders! Added to results. Hyperbolic solved: 47.3714242177043 [20250519_004320.]: Samplename: Sample#2 Root: 47.371 --> Root in between the borders! Added to results. Hyperbolic solved: 58.3144949237501 [20250519_004320.]: Samplename: Sample#3 Root: 58.314 --> Root in between the borders! Added to results. Hyperbolic solved: 11.7212806729981 [20250519_004320.]: Samplename: Sample#4 Root: 11.721 --> Root in between the borders! Added to results. Hyperbolic solved: 25.3798682873976 [20250519_004320.]: Samplename: Sample#5 Root: 25.38 --> Root in between the borders! Added to results. Hyperbolic solved: 29.4096493828253 [20250519_004320.]: Samplename: Sample#6 Root: 29.41 --> Root in between the borders! Added to results. Hyperbolic solved: 44.5756969430888 [20250519_004320.]: Samplename: Sample#7 Root: 44.576 --> Root in between the borders! Added to results. Hyperbolic solved: 85.9631455147929 [20250519_004320.]: Samplename: Sample#8 Root: 85.963 --> Root in between the borders! Added to results. Hyperbolic solved: -4.16459229634232 [20250519_004320.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -4.165 --> '-10 < root < 0' --> substitute 0 [20250519_004320.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 72.9696678780879 [20250519_004320.]: Samplename: Sample#1 Root: 72.97 --> Root in between the borders! Added to results. Hyperbolic solved: 27.825096438612 [20250519_004320.]: Samplename: Sample#10 Root: 27.825 --> Root in between the borders! Added to results. Hyperbolic solved: 34.9068412412515 [20250519_004320.]: Samplename: Sample#2 Root: 34.907 --> Root in between the borders! Added to results. Hyperbolic solved: 59.1637501357335 [20250519_004320.]: Samplename: Sample#3 Root: 59.164 --> Root in between the borders! Added to results. Hyperbolic solved: 9.6464397671905 [20250519_004320.]: Samplename: Sample#4 Root: 9.646 --> Root in between the borders! Added to results. Hyperbolic solved: 19.3522593577541 [20250519_004320.]: Samplename: Sample#5 Root: 19.352 --> Root in between the borders! Added to results. Hyperbolic solved: 38.5571468548369 [20250519_004320.]: Samplename: Sample#6 Root: 38.557 --> Root in between the borders! Added to results. Hyperbolic solved: 44.775966890921 [20250519_004320.]: Samplename: Sample#7 Root: 44.776 --> Root in between the borders! Added to results. Hyperbolic solved: 84.8599747802978 [20250519_004320.]: Samplename: Sample#8 Root: 84.86 --> Root in between the borders! Added to results. Hyperbolic solved: -2.05045739684141 [20250519_004320.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.05 --> '-10 < root < 0' --> substitute 0 [20250519_004320.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 81.0413625074508 [20250519_004320.]: Samplename: Sample#1 Root: 81.041 --> Root in between the borders! Added to results. Hyperbolic solved: 26.6440232383638 [20250519_004320.]: Samplename: Sample#10 Root: 26.644 --> Root in between the borders! Added to results. Hyperbolic solved: 45.704400902509 [20250519_004320.]: Samplename: Sample#2 Root: 45.704 --> Root in between the borders! Added to results. Hyperbolic solved: 56.9765444860285 [20250519_004320.]: Samplename: Sample#3 Root: 56.977 --> Root in between the borders! Added to results. Hyperbolic solved: 6.81226926030202 [20250519_004320.]: Samplename: Sample#4 Root: 6.812 --> Root in between the borders! Added to results. Hyperbolic solved: 22.9283517775063 [20250519_004320.]: Samplename: Sample#5 Root: 22.928 --> Root in between the borders! Added to results. Hyperbolic solved: 38.8651260750996 [20250519_004320.]: Samplename: Sample#6 Root: 38.865 --> Root in between the borders! Added to results. Hyperbolic solved: 43.6853511825078 [20250519_004320.]: Samplename: Sample#7 Root: 43.685 --> Root in between the borders! Added to results. Hyperbolic solved: 88.0152224353095 [20250519_004320.]: Samplename: Sample#8 Root: 88.015 --> Root in between the borders! Added to results. Hyperbolic solved: -3.35608361520768 [20250519_004320.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -3.356 --> '-10 < root < 0' --> substitute 0 [20250519_004320.]: Solving hyperbolic regression for row_means Hyperbolic solved: 77.069275047056 [20250519_004320.]: Samplename: Sample#1 Root: 77.069 --> Root in between the borders! Added to results. Hyperbolic solved: 28.3620003757916 [20250519_004320.]: Samplename: Sample#10 Root: 28.362 --> Root in between the borders! Added to results. Hyperbolic solved: 42.5026135474906 [20250519_004320.]: Samplename: Sample#2 Root: 42.503 --> Root in between the borders! Added to results. Hyperbolic solved: 57.2972007822813 [20250519_004320.]: Samplename: Sample#3 Root: 57.297 --> Root in between the borders! Added to results. Hyperbolic solved: 8.82703592503651 [20250519_004320.]: Samplename: Sample#4 Root: 8.827 --> Root in between the borders! Added to results. Hyperbolic solved: 21.8102552608375 [20250519_004320.]: Samplename: Sample#5 Root: 21.81 --> Root in between the borders! Added to results. Hyperbolic solved: 28.7228620570755 [20250519_004320.]: Samplename: Sample#6 Root: 28.723 --> Root in between the borders! Added to results. Hyperbolic solved: 43.4105062814289 [20250519_004320.]: Samplename: Sample#7 Root: 43.411 --> Root in between the borders! Added to results. Hyperbolic solved: 86.4143497902066 [20250519_004320.]: Samplename: Sample#8 Root: 86.414 --> Root in between the borders! Added to results. Hyperbolic solved: -0.237025016559672 [20250519_004320.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.237 --> '-10 < root < 0' --> substitute 0 [20250519_004320.]: Entered 'solving_equations'-Function [20250519_004320.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: -2.23222837469517 [20250519_004320.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.232 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.1698391693126 [20250519_004320.]: Samplename: 12.5 Root: 12.17 --> Root in between the borders! Added to results. Hyperbolic solved: 24.4781729791561 [20250519_004320.]: Samplename: 25 Root: 24.478 --> Root in between the borders! Added to results. Hyperbolic solved: 38.1730159074047 [20250519_004320.]: Samplename: 37.5 Root: 38.173 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3348987967717 [20250519_004320.]: Samplename: 50 Root: 52.335 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4582305966058 [20250519_004320.]: Samplename: 62.5 Root: 65.458 --> Root in between the borders! Added to results. Hyperbolic solved: 75.0090269722885 [20250519_004320.]: Samplename: 75 Root: 75.009 --> Root in between the borders! Added to results. Hyperbolic solved: 81.527135738891 [20250519_004320.]: Samplename: 87.5 Root: 81.527 --> Root in between the borders! Added to results. Hyperbolic solved: 102.400825350044 [20250519_004320.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.401 --> '100 < root < 110' --> substitute 100 [20250519_004320.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 1.13663087266051 [20250519_004320.]: Samplename: 0 Root: 1.137 --> Root in between the borders! Added to results. Hyperbolic solved: 11.4130277963576 [20250519_004320.]: Samplename: 12.5 Root: 11.413 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1741025868157 [20250519_004320.]: Samplename: 25 Root: 26.174 --> Root in between the borders! Added to results. Hyperbolic solved: 35.1051721736724 [20250519_004320.]: Samplename: 37.5 Root: 35.105 --> Root in between the borders! Added to results. Hyperbolic solved: 47.6856613240114 [20250519_004320.]: Samplename: 50 Root: 47.686 --> Root in between the borders! Added to results. Hyperbolic solved: 67.1442585354696 [20250519_004320.]: Samplename: 62.5 Root: 67.144 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7646957109439 [20250519_004320.]: Samplename: 75 Root: 75.765 --> Root in between the borders! Added to results. Hyperbolic solved: 84.4056634834815 [20250519_004320.]: Samplename: 87.5 Root: 84.406 --> Root in between the borders! Added to results. Hyperbolic solved: 100.94855337669 [20250519_004320.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.949 --> '100 < root < 110' --> substitute 100 [20250519_004320.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 0.512348257854635 [20250519_004320.]: Samplename: 0 Root: 0.512 --> Root in between the borders! Added to results. Hyperbolic solved: 10.7523476241811 [20250519_004320.]: Samplename: 12.5 Root: 10.752 --> Root in between the borders! Added to results. Hyperbolic solved: 25.5218062516799 [20250519_004320.]: Samplename: 25 Root: 25.522 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5269315729339 [20250519_004320.]: Samplename: 37.5 Root: 36.527 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7907738274884 [20250519_004320.]: Samplename: 50 Root: 50.791 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8684489988589 [20250519_004320.]: Samplename: 62.5 Root: 64.868 --> Root in between the borders! Added to results. Hyperbolic solved: 77.5522101073979 [20250519_004320.]: Samplename: 75 Root: 77.552 --> Root in between the borders! Added to results. Hyperbolic solved: 80.4372474691229 [20250519_004320.]: Samplename: 87.5 Root: 80.437 --> Root in between the borders! Added to results. Hyperbolic solved: 102.703772810881 [20250519_004320.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.704 --> '100 < root < 110' --> substitute 100 [20250519_004320.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: -0.519513573026794 [20250519_004320.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.52 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.4933760051342 [20250519_004320.]: Samplename: 12.5 Root: 12.493 --> Root in between the borders! Added to results. Hyperbolic solved: 24.2684789512555 [20250519_004320.]: Samplename: 25 Root: 24.268 --> Root in between the borders! Added to results. Hyperbolic solved: 38.0816229393195 [20250519_004320.]: Samplename: 37.5 Root: 38.082 --> Root in between the borders! Added to results. Hyperbolic solved: 48.5842089589187 [20250519_004320.]: Samplename: 50 Root: 48.584 --> Root in between the borders! Added to results. Hyperbolic solved: 67.6720983549562 [20250519_004320.]: Samplename: 62.5 Root: 67.672 --> Root in between the borders! Added to results. Hyperbolic solved: 74.1547759694059 [20250519_004320.]: Samplename: 75 Root: 74.155 --> Root in between the borders! Added to results. Hyperbolic solved: 82.8820147905386 [20250519_004320.]: Samplename: 87.5 Root: 82.882 --> Root in between the borders! Added to results. Hyperbolic solved: 102.078935346876 [20250519_004320.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.079 --> '100 < root < 110' --> substitute 100 [20250519_004320.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 2.41558040143479 [20250519_004320.]: Samplename: 0 Root: 2.416 --> Root in between the borders! Added to results. Hyperbolic solved: 10.1649584830834 [20250519_004320.]: Samplename: 12.5 Root: 10.165 --> Root in between the borders! Added to results. Hyperbolic solved: 23.9830670020905 [20250519_004320.]: Samplename: 25 Root: 23.983 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2773406385708 [20250519_004320.]: Samplename: 37.5 Root: 37.277 --> Root in between the borders! Added to results. Hyperbolic solved: 50.8659103346102 [20250519_004320.]: Samplename: 50 Root: 50.866 --> Root in between the borders! Added to results. Hyperbolic solved: 62.4341926937382 [20250519_004320.]: Samplename: 62.5 Root: 62.434 --> Root in between the borders! Added to results. Hyperbolic solved: 76.3914832329149 [20250519_004320.]: Samplename: 75 Root: 76.391 --> Root in between the borders! Added to results. Hyperbolic solved: 86.1597399215782 [20250519_004320.]: Samplename: 87.5 Root: 86.16 --> Root in between the borders! Added to results. Hyperbolic solved: 100.267701841999 [20250519_004320.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.268 --> '100 < root < 110' --> substitute 100 [20250519_004320.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 0.138179963459196 [20250519_004320.]: Samplename: 0 Root: 0.138 --> Root in between the borders! Added to results. Hyperbolic solved: 11.8635989635202 [20250519_004320.]: Samplename: 12.5 Root: 11.864 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5108198460409 [20250519_004320.]: Samplename: 25 Root: 26.511 --> Root in between the borders! Added to results. Hyperbolic solved: 35.3206004021833 [20250519_004320.]: Samplename: 37.5 Root: 35.321 --> Root in between the borders! Added to results. Hyperbolic solved: 50.0571987473308 [20250519_004320.]: Samplename: 50 Root: 50.057 --> Root in between the borders! Added to results. Hyperbolic solved: 64.9604436607063 [20250519_004320.]: Samplename: 62.5 Root: 64.96 --> Root in between the borders! Added to results. Hyperbolic solved: 73.6690699266294 [20250519_004320.]: Samplename: 75 Root: 73.669 --> Root in between the borders! Added to results. Hyperbolic solved: 87.1267946879604 [20250519_004320.]: Samplename: 87.5 Root: 87.127 --> Root in between the borders! Added to results. Hyperbolic solved: 100.261842767865 [20250519_004320.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.262 --> '100 < root < 110' --> substitute 100 [20250519_004320.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: -1.37239249181669 [20250519_004320.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -1.372 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.199366215418 [20250519_004320.]: Samplename: 12.5 Root: 10.199 --> Root in between the borders! Added to results. Hyperbolic solved: 24.595295361327 [20250519_004320.]: Samplename: 25 Root: 24.595 --> Root in between the borders! Added to results. Hyperbolic solved: 37.8312095633391 [20250519_004320.]: Samplename: 37.5 Root: 37.831 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5590896582852 [20250519_004320.]: Samplename: 50 Root: 53.559 --> Root in between the borders! Added to results. Hyperbolic solved: 65.9367389282431 [20250519_004320.]: Samplename: 62.5 Root: 65.937 --> Root in between the borders! Added to results. Hyperbolic solved: 75.736370152841 [20250519_004320.]: Samplename: 75 Root: 75.736 --> Root in between the borders! Added to results. Hyperbolic solved: 79.433089359524 [20250519_004320.]: Samplename: 87.5 Root: 79.433 --> Root in between the borders! Added to results. Hyperbolic solved: 103.004516225662 [20250519_004320.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 103.005 --> '100 < root < 110' --> substitute 100 [20250519_004320.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 2.80067549526643 [20250519_004320.]: Samplename: 0 Root: 2.801 --> Root in between the borders! Added to results. Hyperbolic solved: 9.27534686003087 [20250519_004320.]: Samplename: 12.5 Root: 9.275 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4762624089952 [20250519_004320.]: Samplename: 25 Root: 25.476 --> Root in between the borders! Added to results. Hyperbolic solved: 34.0122098045585 [20250519_004320.]: Samplename: 37.5 Root: 34.012 --> Root in between the borders! Added to results. Hyperbolic solved: 51.784270967861 [20250519_004320.]: Samplename: 50 Root: 51.784 --> Root in between the borders! Added to results. Hyperbolic solved: 64.6732380258789 [20250519_004320.]: Samplename: 62.5 Root: 64.673 --> Root in between the borders! Added to results. Hyperbolic solved: 78.4327054483192 [20250519_004320.]: Samplename: 75 Root: 78.433 --> Root in between the borders! Added to results. Hyperbolic solved: 81.3427308951795 [20250519_004320.]: Samplename: 87.5 Root: 81.343 --> Root in between the borders! Added to results. Hyperbolic solved: 101.964413523995 [20250519_004320.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.964 --> '100 < root < 110' --> substitute 100 [20250519_004320.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: -2.13402763663736 [20250519_004320.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.134 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.5081558897243 [20250519_004320.]: Samplename: 12.5 Root: 10.508 --> Root in between the borders! Added to results. Hyperbolic solved: 26.9163121098627 [20250519_004320.]: Samplename: 25 Root: 26.916 --> Root in between the borders! Added to results. Hyperbolic solved: 36.8332915301825 [20250519_004320.]: Samplename: 37.5 Root: 36.833 --> Root in between the borders! Added to results. Hyperbolic solved: 52.00955 [20250519_004320.]: Samplename: 50 Root: 52.01 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8927781893359 [20250519_004320.]: Samplename: 62.5 Root: 64.893 --> Root in between the borders! Added to results. Hyperbolic solved: 74.566810799042 [20250519_004320.]: Samplename: 75 Root: 74.567 --> Root in between the borders! Added to results. Hyperbolic solved: 84.5291854856128 [20250519_004320.]: Samplename: 87.5 Root: 84.529 --> Root in between the borders! Added to results. Hyperbolic solved: 101.046823491232 [20250519_004320.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.047 --> '100 < root < 110' --> substitute 100 [20250519_004320.]: Solving hyperbolic regression for row_means Hyperbolic solved: 0.290936038655697 [20250519_004320.]: Samplename: 0 Root: 0.291 --> Root in between the borders! Added to results. Hyperbolic solved: 11.0412364555656 [20250519_004320.]: Samplename: 12.5 Root: 11.041 --> Root in between the borders! Added to results. Hyperbolic solved: 25.408146358228 [20250519_004320.]: Samplename: 25 Root: 25.408 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5243683648753 [20250519_004320.]: Samplename: 37.5 Root: 36.524 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7348788337891 [20250519_004320.]: Samplename: 50 Root: 50.735 --> Root in between the borders! Added to results. Hyperbolic solved: 65.3135169308955 [20250519_004320.]: Samplename: 62.5 Root: 65.314 --> Root in between the borders! Added to results. Hyperbolic solved: 75.5342663138126 [20250519_004320.]: Samplename: 75 Root: 75.534 --> Root in between the borders! Added to results. Hyperbolic solved: 83.2411177153911 [20250519_004320.]: Samplename: 87.5 Root: 83.241 --> Root in between the borders! Added to results. Hyperbolic solved: 101.666935931185 [20250519_004320.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.667 --> '100 < root < 110' --> substitute 100 [20250519_004321.]: Entered 'clean_dt'-Function [20250519_004321.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_004321.]: got experimental data [20250519_004321.]: Entered 'clean_dt'-Function [20250519_004321.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_004321.]: got calibration data [20250519_004321.]: ### Starting with regression calculations ### [20250519_004321.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_004321.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_004321.]: Logging df_agg: CpG#1 [20250519_004321.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004321.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_004321.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_004321.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_004321.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_004321.]: Entered 'hyperbolic_regression'-Function [20250519_004321.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004321.]: Entered 'cubic_regression'-Function [20250519_004321.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004321.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_004321.]: Logging df_agg: CpG#2 [20250519_004321.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004321.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_004321.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_004321.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_004321.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_004321.]: Entered 'hyperbolic_regression'-Function [20250519_004321.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004321.]: Entered 'cubic_regression'-Function [20250519_004321.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004321.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_004321.]: Logging df_agg: CpG#3 [20250519_004321.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004321.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_004321.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_004321.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_004321.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_004321.]: Entered 'hyperbolic_regression'-Function [20250519_004321.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004322.]: Entered 'cubic_regression'-Function [20250519_004322.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004322.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_004322.]: Logging df_agg: CpG#4 [20250519_004322.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004322.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_004322.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_004322.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_004322.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_004322.]: Entered 'hyperbolic_regression'-Function [20250519_004322.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004322.]: Entered 'cubic_regression'-Function [20250519_004322.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004322.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_004322.]: Logging df_agg: CpG#5 [20250519_004322.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004322.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_004322.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_004322.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_004322.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_004322.]: Entered 'hyperbolic_regression'-Function [20250519_004322.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004323.]: Entered 'cubic_regression'-Function [20250519_004323.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004323.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_004323.]: Logging df_agg: CpG#6 [20250519_004323.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004323.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_004323.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_004323.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_004323.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_004323.]: Entered 'hyperbolic_regression'-Function [20250519_004323.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004323.]: Entered 'cubic_regression'-Function [20250519_004323.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004323.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_004323.]: Logging df_agg: CpG#7 [20250519_004323.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004323.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_004323.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_004323.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_004323.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_004323.]: Entered 'hyperbolic_regression'-Function [20250519_004323.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004323.]: Entered 'cubic_regression'-Function [20250519_004323.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004323.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_004323.]: Logging df_agg: CpG#8 [20250519_004323.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004323.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_004323.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_004323.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_004323.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_004323.]: Entered 'hyperbolic_regression'-Function [20250519_004323.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004324.]: Entered 'cubic_regression'-Function [20250519_004324.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004324.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_004324.]: Logging df_agg: CpG#9 [20250519_004324.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004324.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_004324.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_004324.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_004324.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_004324.]: Entered 'hyperbolic_regression'-Function [20250519_004324.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004324.]: Entered 'cubic_regression'-Function [20250519_004324.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004324.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_004324.]: Logging df_agg: row_means [20250519_004324.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004324.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_004324.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_004324.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_004324.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_004324.]: Entered 'hyperbolic_regression'-Function [20250519_004324.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004324.]: Entered 'cubic_regression'-Function [20250519_004324.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004325.]: ### Starting with regression calculations ### [20250519_004325.]: Entered 'regression_type1'-Function [20250519_004325.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_004325.]: Logging df_agg: CpG#1 [20250519_004325.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004325.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_004325.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_004325.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_004325.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_004325.]: Entered 'hyperbolic_regression'-Function [20250519_004325.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004326.]: Entered 'cubic_regression'-Function [20250519_004326.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004326.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_004326.]: Logging df_agg: CpG#2 [20250519_004326.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004326.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_004326.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_004326.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_004326.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_004326.]: Entered 'hyperbolic_regression'-Function [20250519_004326.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004326.]: Entered 'cubic_regression'-Function [20250519_004326.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004326.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_004326.]: Logging df_agg: CpG#3 [20250519_004326.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004326.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_004326.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_004326.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_004326.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_004326.]: Entered 'hyperbolic_regression'-Function [20250519_004326.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004326.]: Entered 'cubic_regression'-Function [20250519_004326.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004327.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_004327.]: Logging df_agg: CpG#4 [20250519_004327.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004327.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_004327.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_004327.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_004327.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_004327.]: Entered 'hyperbolic_regression'-Function [20250519_004327.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004327.]: Entered 'cubic_regression'-Function [20250519_004327.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004327.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_004327.]: Logging df_agg: CpG#5 [20250519_004327.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004327.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_004327.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_004327.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_004327.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_004327.]: Entered 'hyperbolic_regression'-Function [20250519_004327.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004327.]: Entered 'cubic_regression'-Function [20250519_004327.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004327.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_004327.]: Logging df_agg: CpG#6 [20250519_004327.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004327.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_004327.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_004327.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_004327.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_004327.]: Entered 'hyperbolic_regression'-Function [20250519_004327.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004328.]: Entered 'cubic_regression'-Function [20250519_004328.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004328.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_004328.]: Logging df_agg: CpG#7 [20250519_004328.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004328.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_004328.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_004328.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_004328.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_004328.]: Entered 'hyperbolic_regression'-Function [20250519_004328.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004328.]: Entered 'cubic_regression'-Function [20250519_004328.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004328.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_004328.]: Logging df_agg: CpG#8 [20250519_004328.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004328.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_004328.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_004328.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_004328.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_004328.]: Entered 'hyperbolic_regression'-Function [20250519_004328.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004328.]: Entered 'cubic_regression'-Function [20250519_004328.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004328.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_004328.]: Logging df_agg: CpG#9 [20250519_004328.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004328.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_004328.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_004328.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_004328.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_004328.]: Entered 'hyperbolic_regression'-Function [20250519_004328.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004329.]: Entered 'cubic_regression'-Function [20250519_004329.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004329.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_004329.]: Logging df_agg: row_means [20250519_004329.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004329.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_004329.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_004329.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_004329.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_004329.]: Entered 'hyperbolic_regression'-Function [20250519_004329.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004329.]: Entered 'cubic_regression'-Function [20250519_004329.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004330.]: ### Starting with regression calculations ### [20250519_004330.]: Entered 'regression_type1'-Function [20250519_004331.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_004331.]: Logging df_agg: CpG#1 [20250519_004331.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004331.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_004331.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_004331.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_004331.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_004331.]: Entered 'hyperbolic_regression'-Function [20250519_004331.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004331.]: Entered 'cubic_regression'-Function [20250519_004331.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004331.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_004331.]: Logging df_agg: CpG#2 [20250519_004331.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004331.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_004331.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_004331.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_004331.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_004331.]: Entered 'hyperbolic_regression'-Function [20250519_004331.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004331.]: Entered 'cubic_regression'-Function [20250519_004331.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004331.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_004331.]: Logging df_agg: CpG#3 [20250519_004331.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004331.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_004331.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_004331.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_004331.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_004331.]: Entered 'hyperbolic_regression'-Function [20250519_004331.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004331.]: Entered 'cubic_regression'-Function [20250519_004331.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004331.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_004331.]: Logging df_agg: CpG#4 [20250519_004331.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004331.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_004331.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_004331.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_004331.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_004331.]: Entered 'hyperbolic_regression'-Function [20250519_004331.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004331.]: Entered 'cubic_regression'-Function [20250519_004331.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004331.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_004331.]: Logging df_agg: CpG#5 [20250519_004331.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004331.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_004331.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_004331.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_004331.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_004331.]: Entered 'hyperbolic_regression'-Function [20250519_004331.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004331.]: Entered 'cubic_regression'-Function [20250519_004331.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004331.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_004331.]: Logging df_agg: CpG#6 [20250519_004331.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004331.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_004331.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_004331.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_004331.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_004331.]: Entered 'hyperbolic_regression'-Function [20250519_004331.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004331.]: Entered 'cubic_regression'-Function [20250519_004331.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004331.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_004331.]: Logging df_agg: CpG#7 [20250519_004331.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004331.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_004331.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_004331.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_004331.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_004331.]: Entered 'hyperbolic_regression'-Function [20250519_004331.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004331.]: Entered 'cubic_regression'-Function [20250519_004331.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004331.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_004331.]: Logging df_agg: CpG#8 [20250519_004331.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004331.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_004331.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_004331.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_004331.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_004331.]: Entered 'hyperbolic_regression'-Function [20250519_004331.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004331.]: Entered 'cubic_regression'-Function [20250519_004331.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004331.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_004331.]: Logging df_agg: CpG#9 [20250519_004331.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004331.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_004331.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_004331.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_004331.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_004331.]: Entered 'hyperbolic_regression'-Function [20250519_004331.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004331.]: Entered 'cubic_regression'-Function [20250519_004331.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004331.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_004331.]: Logging df_agg: row_means [20250519_004331.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004331.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_004331.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_004331.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_004331.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_004331.]: Entered 'hyperbolic_regression'-Function [20250519_004331.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004331.]: Entered 'cubic_regression'-Function [20250519_004331.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004332.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_004333.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_004333.]: Logging df_agg: CpG#1 [20250519_004333.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004333.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_004333.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_004333.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_004333.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_004333.]: Entered 'hyperbolic_regression'-Function [20250519_004333.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004333.]: Entered 'cubic_regression'-Function [20250519_004333.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004333.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_004333.]: Logging df_agg: CpG#2 [20250519_004333.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004333.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_004333.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_004333.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_004333.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_004333.]: Entered 'hyperbolic_regression'-Function [20250519_004333.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004333.]: Entered 'cubic_regression'-Function [20250519_004333.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004334.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_004334.]: Logging df_agg: CpG#3 [20250519_004334.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004334.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_004334.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_004334.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_004334.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_004334.]: Entered 'hyperbolic_regression'-Function [20250519_004334.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004334.]: Entered 'cubic_regression'-Function [20250519_004334.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004334.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_004334.]: Logging df_agg: CpG#4 [20250519_004334.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004334.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_004334.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_004334.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_004334.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_004334.]: Entered 'hyperbolic_regression'-Function [20250519_004334.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004334.]: Entered 'cubic_regression'-Function [20250519_004334.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004334.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_004334.]: Logging df_agg: CpG#5 [20250519_004334.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004334.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_004334.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_004334.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_004334.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_004334.]: Entered 'hyperbolic_regression'-Function [20250519_004334.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004335.]: Entered 'cubic_regression'-Function [20250519_004335.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004335.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_004335.]: Logging df_agg: CpG#6 [20250519_004335.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004335.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_004335.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_004335.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_004335.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_004335.]: Entered 'hyperbolic_regression'-Function [20250519_004335.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004335.]: Entered 'cubic_regression'-Function [20250519_004335.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004335.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_004335.]: Logging df_agg: CpG#7 [20250519_004335.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004335.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_004335.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_004335.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_004335.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_004335.]: Entered 'hyperbolic_regression'-Function [20250519_004335.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004335.]: Entered 'cubic_regression'-Function [20250519_004335.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004335.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_004335.]: Logging df_agg: CpG#8 [20250519_004335.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004335.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_004335.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_004335.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_004335.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_004335.]: Entered 'hyperbolic_regression'-Function [20250519_004335.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004336.]: Entered 'cubic_regression'-Function [20250519_004336.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004336.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_004336.]: Logging df_agg: CpG#9 [20250519_004336.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004336.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_004336.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_004336.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_004336.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_004336.]: Entered 'hyperbolic_regression'-Function [20250519_004336.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004336.]: Entered 'cubic_regression'-Function [20250519_004336.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004336.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_004336.]: Logging df_agg: row_means [20250519_004336.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004336.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_004336.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_004336.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_004336.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_004336.]: Entered 'hyperbolic_regression'-Function [20250519_004336.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004336.]: Entered 'cubic_regression'-Function [20250519_004336.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004337.]: Entered 'clean_dt'-Function [20250519_004337.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_004337.]: got experimental data [20250519_004337.]: Entered 'clean_dt'-Function [20250519_004337.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_004337.]: got calibration data [20250519_004337.]: ### Starting with regression calculations ### [20250519_004337.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_004337.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_004337.]: Logging df_agg: CpG#1 [20250519_004337.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004337.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_004337.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_004337.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_004337.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_004337.]: Entered 'hyperbolic_regression'-Function [20250519_004337.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004337.]: Entered 'cubic_regression'-Function [20250519_004337.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004338.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_004338.]: Logging df_agg: CpG#2 [20250519_004338.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004338.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_004338.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_004338.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_004338.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_004338.]: Entered 'hyperbolic_regression'-Function [20250519_004338.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004338.]: Entered 'cubic_regression'-Function [20250519_004338.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004338.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_004338.]: Logging df_agg: CpG#3 [20250519_004338.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004338.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_004338.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_004338.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_004338.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_004338.]: Entered 'hyperbolic_regression'-Function [20250519_004338.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004338.]: Entered 'cubic_regression'-Function [20250519_004338.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004338.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_004338.]: Logging df_agg: CpG#4 [20250519_004338.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004338.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_004338.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_004338.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_004338.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_004338.]: Entered 'hyperbolic_regression'-Function [20250519_004338.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004339.]: Entered 'cubic_regression'-Function [20250519_004339.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004339.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_004339.]: Logging df_agg: CpG#5 [20250519_004339.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004339.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_004339.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_004339.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_004339.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_004339.]: Entered 'hyperbolic_regression'-Function [20250519_004339.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004339.]: Entered 'cubic_regression'-Function [20250519_004339.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004339.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_004339.]: Logging df_agg: CpG#6 [20250519_004339.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004339.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_004339.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_004339.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_004339.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_004339.]: Entered 'hyperbolic_regression'-Function [20250519_004339.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004339.]: Entered 'cubic_regression'-Function [20250519_004339.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004339.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_004339.]: Logging df_agg: CpG#7 [20250519_004339.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004339.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_004339.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_004339.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_004339.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_004339.]: Entered 'hyperbolic_regression'-Function [20250519_004339.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004340.]: Entered 'cubic_regression'-Function [20250519_004340.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004340.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_004340.]: Logging df_agg: CpG#8 [20250519_004340.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004340.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_004340.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_004340.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_004340.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_004340.]: Entered 'hyperbolic_regression'-Function [20250519_004340.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004340.]: Entered 'cubic_regression'-Function [20250519_004340.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004340.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_004340.]: Logging df_agg: CpG#9 [20250519_004340.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004340.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_004340.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_004340.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_004340.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_004340.]: Entered 'hyperbolic_regression'-Function [20250519_004340.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004340.]: Entered 'cubic_regression'-Function [20250519_004340.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004340.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_004341.]: Logging df_agg: row_means [20250519_004341.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004341.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_004341.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_004341.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_004341.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_004341.]: Entered 'hyperbolic_regression'-Function [20250519_004341.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004341.]: Entered 'cubic_regression'-Function [20250519_004341.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004341.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_004342.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_004342.]: Logging df_agg: CpG#1 [20250519_004342.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004342.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_004342.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_004342.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_004342.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_004342.]: Entered 'hyperbolic_regression'-Function [20250519_004342.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004342.]: Entered 'cubic_regression'-Function [20250519_004342.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004342.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_004342.]: Logging df_agg: CpG#2 [20250519_004342.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004342.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_004342.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_004342.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_004342.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_004342.]: Entered 'hyperbolic_regression'-Function [20250519_004342.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004342.]: Entered 'cubic_regression'-Function [20250519_004342.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004342.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_004342.]: Logging df_agg: CpG#3 [20250519_004342.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004342.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_004342.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_004342.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_004342.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_004342.]: Entered 'hyperbolic_regression'-Function [20250519_004342.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004343.]: Entered 'cubic_regression'-Function [20250519_004343.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004343.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_004343.]: Logging df_agg: CpG#4 [20250519_004343.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004343.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_004343.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_004343.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_004343.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_004343.]: Entered 'hyperbolic_regression'-Function [20250519_004343.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004343.]: Entered 'cubic_regression'-Function [20250519_004343.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004343.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_004343.]: Logging df_agg: CpG#5 [20250519_004343.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004343.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_004343.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_004343.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_004343.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_004343.]: Entered 'hyperbolic_regression'-Function [20250519_004343.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004343.]: Entered 'cubic_regression'-Function [20250519_004343.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004344.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_004344.]: Logging df_agg: CpG#6 [20250519_004344.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004344.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_004344.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_004344.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_004344.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_004344.]: Entered 'hyperbolic_regression'-Function [20250519_004344.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004344.]: Entered 'cubic_regression'-Function [20250519_004344.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004344.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_004344.]: Logging df_agg: CpG#7 [20250519_004344.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004344.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_004344.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_004344.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_004344.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_004344.]: Entered 'hyperbolic_regression'-Function [20250519_004344.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004344.]: Entered 'cubic_regression'-Function [20250519_004344.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004344.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_004344.]: Logging df_agg: CpG#8 [20250519_004344.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004344.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_004344.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_004344.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_004344.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_004344.]: Entered 'hyperbolic_regression'-Function [20250519_004344.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004345.]: Entered 'cubic_regression'-Function [20250519_004345.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004345.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_004345.]: Logging df_agg: CpG#9 [20250519_004345.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004345.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_004345.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_004345.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_004345.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_004345.]: Entered 'hyperbolic_regression'-Function [20250519_004345.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004345.]: Entered 'cubic_regression'-Function [20250519_004345.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004345.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_004345.]: Logging df_agg: row_means [20250519_004345.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004345.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_004345.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_004345.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_004345.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_004345.]: Entered 'hyperbolic_regression'-Function [20250519_004345.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004345.]: Entered 'cubic_regression'-Function [20250519_004345.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004346.]: Entered 'solving_equations'-Function [20250519_004346.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 0 [20250519_004346.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 14.1480167109277 [20250519_004346.]: Samplename: 12.5 Root: 14.148 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1398443790446 [20250519_004346.]: Samplename: 25 Root: 26.14 --> Root in between the borders! Added to results. Hyperbolic solved: 39.3762039523382 [20250519_004346.]: Samplename: 37.5 Root: 39.376 --> Root in between the borders! Added to results. Hyperbolic solved: 52.9476244028783 [20250519_004346.]: Samplename: 50 Root: 52.948 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4195105873419 [20250519_004346.]: Samplename: 62.5 Root: 65.42 --> Root in between the borders! Added to results. Hyperbolic solved: 74.433837698331 [20250519_004346.]: Samplename: 75 Root: 74.434 --> Root in between the borders! Added to results. Hyperbolic solved: 80.5559264925808 [20250519_004346.]: Samplename: 87.5 Root: 80.556 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_004346.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_004346.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 0 [20250519_004346.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 10.7812303822548 [20250519_004346.]: Samplename: 12.5 Root: 10.781 --> Root in between the borders! Added to results. Hyperbolic solved: 26.0648314401535 [20250519_004346.]: Samplename: 25 Root: 26.065 --> Root in between the borders! Added to results. Hyperbolic solved: 35.1980950192158 [20250519_004346.]: Samplename: 37.5 Root: 35.198 --> Root in between the borders! Added to results. Hyperbolic solved: 47.9203836090837 [20250519_004346.]: Samplename: 50 Root: 47.92 --> Root in between the borders! Added to results. Hyperbolic solved: 67.275750545092 [20250519_004346.]: Samplename: 62.5 Root: 67.276 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7278143684244 [20250519_004346.]: Samplename: 75 Root: 75.728 --> Root in between the borders! Added to results. Hyperbolic solved: 84.1258428690749 [20250519_004346.]: Samplename: 87.5 Root: 84.126 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_004346.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_004346.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 0 [20250519_004346.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 10.8467198180169 [20250519_004346.]: Samplename: 12.5 Root: 10.847 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1449700612555 [20250519_004346.]: Samplename: 25 Root: 26.145 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2865760013773 [20250519_004346.]: Samplename: 37.5 Root: 37.287 --> Root in between the borders! Added to results. Hyperbolic solved: 51.4114078772697 [20250519_004346.]: Samplename: 50 Root: 51.411 --> Root in between the borders! Added to results. Hyperbolic solved: 65.0139634978039 [20250519_004346.]: Samplename: 62.5 Root: 65.014 --> Root in between the borders! Added to results. Hyperbolic solved: 76.9921394830877 [20250519_004346.]: Samplename: 75 Root: 76.992 --> Root in between the borders! Added to results. Hyperbolic solved: 79.6808818589171 [20250519_004346.]: Samplename: 87.5 Root: 79.681 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_004346.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_004346.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 0 [20250519_004346.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 13.2494913489925 [20250519_004346.]: Samplename: 12.5 Root: 13.249 --> Root in between the borders! Added to results. Hyperbolic solved: 25.0914701740206 [20250519_004346.]: Samplename: 25 Root: 25.091 --> Root in between the borders! Added to results. Hyperbolic solved: 38.8081740444332 [20250519_004346.]: Samplename: 37.5 Root: 38.808 --> Root in between the borders! Added to results. Hyperbolic solved: 49.1133034150896 [20250519_004346.]: Samplename: 50 Root: 49.113 --> Root in between the borders! Added to results. Hyperbolic solved: 67.573566028728 [20250519_004346.]: Samplename: 62.5 Root: 67.574 --> Root in between the borders! Added to results. Hyperbolic solved: 73.7655861480302 [20250519_004346.]: Samplename: 75 Root: 73.766 --> Root in between the borders! Added to results. Hyperbolic solved: 82.0404940898918 [20250519_004346.]: Samplename: 87.5 Root: 82.04 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_004346.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_004346.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 0 [20250519_004346.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 8.36101271071658 [20250519_004346.]: Samplename: 12.5 Root: 8.361 --> Root in between the borders! Added to results. Hyperbolic solved: 23.0724671456609 [20250519_004346.]: Samplename: 25 Root: 23.072 --> Root in between the borders! Added to results. Hyperbolic solved: 36.9926902689013 [20250519_004346.]: Samplename: 37.5 Root: 36.993 --> Root in between the borders! Added to results. Hyperbolic solved: 50.9902012556783 [20250519_004346.]: Samplename: 50 Root: 50.99 --> Root in between the borders! Added to results. Hyperbolic solved: 62.7269420395599 [20250519_004346.]: Samplename: 62.5 Root: 62.727 --> Root in between the borders! Added to results. Hyperbolic solved: 76.6726263557269 [20250519_004346.]: Samplename: 75 Root: 76.673 --> Root in between the borders! Added to results. Hyperbolic solved: 86.2959101486824 [20250519_004346.]: Samplename: 87.5 Root: 86.296 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_004346.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_004346.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 0 [20250519_004346.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 11.8139244542506 [20250519_004346.]: Samplename: 12.5 Root: 11.814 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5330423964606 [20250519_004346.]: Samplename: 25 Root: 26.533 --> Root in between the borders! Added to results. Hyperbolic solved: 35.365455365759 [20250519_004346.]: Samplename: 37.5 Root: 35.365 --> Root in between the borders! Added to results. Hyperbolic solved: 50.1054345020203 [20250519_004346.]: Samplename: 50 Root: 50.105 --> Root in between the borders! Added to results. Hyperbolic solved: 64.9683746571925 [20250519_004346.]: Samplename: 62.5 Root: 64.968 --> Root in between the borders! Added to results. Hyperbolic solved: 73.6331726521876 [20250519_004346.]: Samplename: 75 Root: 73.633 --> Root in between the borders! Added to results. Hyperbolic solved: 86.9938602772804 [20250519_004346.]: Samplename: 87.5 Root: 86.994 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_004346.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_004346.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 0 [20250519_004346.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 11.7880491100164 [20250519_004346.]: Samplename: 12.5 Root: 11.788 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1959234034328 [20250519_004346.]: Samplename: 25 Root: 26.196 --> Root in between the borders! Added to results. Hyperbolic solved: 39.1978567589215 [20250519_004346.]: Samplename: 37.5 Root: 39.198 --> Root in between the borders! Added to results. Hyperbolic solved: 54.3513505291649 [20250519_004346.]: Samplename: 50 Root: 54.351 --> Root in between the borders! Added to results. Hyperbolic solved: 66.0567953704493 [20250519_004346.]: Samplename: 62.5 Root: 66.057 --> Root in between the borders! Added to results. Hyperbolic solved: 75.1900867269869 [20250519_004346.]: Samplename: 75 Root: 75.19 --> Root in between the borders! Added to results. Hyperbolic solved: 78.6051660349008 [20250519_004346.]: Samplename: 87.5 Root: 78.605 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_004346.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_004346.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 0 [20250519_004346.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 7.27382484148278 [20250519_004346.]: Samplename: 12.5 Root: 7.274 --> Root in between the borders! Added to results. Hyperbolic solved: 24.9765598282779 [20250519_004346.]: Samplename: 25 Root: 24.977 --> Root in between the borders! Added to results. Hyperbolic solved: 34.028313807905 [20250519_004346.]: Samplename: 37.5 Root: 34.028 --> Root in between the borders! Added to results. Hyperbolic solved: 52.2946423509726 [20250519_004346.]: Samplename: 50 Root: 52.295 --> Root in between the borders! Added to results. Hyperbolic solved: 65.0742432627639 [20250519_004346.]: Samplename: 62.5 Root: 65.074 --> Root in between the borders! Added to results. Hyperbolic solved: 78.3047550247862 [20250519_004346.]: Samplename: 75 Root: 78.305 --> Root in between the borders! Added to results. Hyperbolic solved: 81.0501991298613 [20250519_004346.]: Samplename: 87.5 Root: 81.05 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_004346.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_004346.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 0 [20250519_004346.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 12.2068234201026 [20250519_004346.]: Samplename: 12.5 Root: 12.207 --> Root in between the borders! Added to results. Hyperbolic solved: 28.0688737636578 [20250519_004346.]: Samplename: 25 Root: 28.069 --> Root in between the borders! Added to results. Hyperbolic solved: 37.6661823177513 [20250519_004346.]: Samplename: 37.5 Root: 37.666 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3684233460174 [20250519_004346.]: Samplename: 50 Root: 52.368 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8636916649907 [20250519_004346.]: Samplename: 62.5 Root: 64.864 --> Root in between the borders! Added to results. Hyperbolic solved: 74.2551330907062 [20250519_004346.]: Samplename: 75 Root: 74.255 --> Root in between the borders! Added to results. Hyperbolic solved: 83.9343288872042 [20250519_004346.]: Samplename: 87.5 Root: 83.934 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_004346.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_004346.]: Solving hyperbolic regression for row_means Hyperbolic solved: 0.000228160853766634 [20250519_004346.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 11.1587839347378 [20250519_004346.]: Samplename: 12.5 Root: 11.159 --> Root in between the borders! Added to results. Hyperbolic solved: 25.8570776700364 [20250519_004346.]: Samplename: 25 Root: 25.857 --> Root in between the borders! Added to results. Hyperbolic solved: 37.065004454281 [20250519_004346.]: Samplename: 37.5 Root: 37.065 --> Root in between the borders! Added to results. Hyperbolic solved: 51.1881763075298 [20250519_004346.]: Samplename: 50 Root: 51.188 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4439754555105 [20250519_004346.]: Samplename: 62.5 Root: 65.444 --> Root in between the borders! Added to results. Hyperbolic solved: 75.3005883283082 [20250519_004346.]: Samplename: 75 Root: 75.301 --> Root in between the borders! Added to results. Hyperbolic solved: 82.6591025877361 [20250519_004346.]: Samplename: 87.5 Root: 82.659 --> Root in between the borders! Added to results. Hyperbolic solved: 100.000153506599 [20250519_004346.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 [20250519_004346.]: ### Starting with regression calculations ### [20250519_004346.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_004347.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 14.1480167109277, 26.1398443790446, 39.3762039523382, 52.9476244028783, 65.4195105873419, 74.433837698331, 80.5559264925808, 100)c(0, 1.6480167109277, 1.1398443790446, 1.8762039523382, 2.9476244028783, 2.9195105873419, 0.566162301668996, 6.9440735074192, 0)c(NA, 13.1841336874216, 4.5593775161784, 5.00321053956854, 5.89524880575659, 4.67121693974705, 0.754883068891995, 7.93608400847909, 0) [20250519_004347.]: Logging df_agg: CpG#1 [20250519_004347.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004347.]: c(0, 14.1480167109277, 26.1398443790446, 39.3762039523382, 52.9476244028783, 65.4195105873419, 74.433837698331, 80.5559264925808, 100)[20250519_004347.]: c(0, 1.6480167109277, 1.1398443790446, 1.8762039523382, 2.9476244028783, 2.9195105873419, 0.566162301668996, 6.9440735074192, 0)[20250519_004347.]: c(NA, 13.1841336874216, 4.5593775161784, 5.00321053956854, 5.89524880575659, 4.67121693974705, 0.754883068891995, 7.93608400847909, 0) [20250519_004347.]: Entered 'hyperbolic_regression'-Function [20250519_004347.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004347.]: Entered 'cubic_regression'-Function [20250519_004347.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004347.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.7812303822548, 26.0648314401535, 35.1980950192158, 47.9203836090837, 67.275750545092, 75.7278143684244, 84.1258428690749, 100)c(0, 1.7187696177452, 1.0648314401535, 2.3019049807842, 2.0796163909163, 4.775750545092, 0.727814368424404, 3.3741571309251, 0)c(NA, 13.7501569419616, 4.259325760614, 6.1384132820912, 4.1592327818326, 7.64120087214719, 0.970419157899206, 3.85617957820011, 0) [20250519_004347.]: Logging df_agg: CpG#2 [20250519_004347.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004347.]: c(0, 10.7812303822548, 26.0648314401535, 35.1980950192158, 47.9203836090837, 67.275750545092, 75.7278143684244, 84.1258428690749, 100)[20250519_004347.]: c(0, 1.7187696177452, 1.0648314401535, 2.3019049807842, 2.0796163909163, 4.775750545092, 0.727814368424404, 3.3741571309251, 0)[20250519_004347.]: c(NA, 13.7501569419616, 4.259325760614, 6.1384132820912, 4.1592327818326, 7.64120087214719, 0.970419157899206, 3.85617957820011, 0) [20250519_004347.]: Entered 'hyperbolic_regression'-Function [20250519_004347.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004347.]: Entered 'cubic_regression'-Function [20250519_004347.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004347.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.8467198180169, 26.1449700612555, 37.2865760013773, 51.4114078772697, 65.0139634978039, 76.9921394830877, 79.6808818589171, 100)c(0, 1.6532801819831, 1.1449700612555, 0.213423998622702, 1.4114078772697, 2.5139634978039, 1.9921394830877, 7.8191181410829, 0)c(NA, 13.2262414558648, 4.579880245022, 0.569130662993871, 2.82281575453941, 4.02234159648624, 2.65618597745026, 8.93613501838045, 0) [20250519_004347.]: Logging df_agg: CpG#3 [20250519_004347.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004347.]: c(0, 10.8467198180169, 26.1449700612555, 37.2865760013773, 51.4114078772697, 65.0139634978039, 76.9921394830877, 79.6808818589171, 100)[20250519_004347.]: c(0, 1.6532801819831, 1.1449700612555, 0.213423998622702, 1.4114078772697, 2.5139634978039, 1.9921394830877, 7.8191181410829, 0)[20250519_004347.]: c(NA, 13.2262414558648, 4.579880245022, 0.569130662993871, 2.82281575453941, 4.02234159648624, 2.65618597745026, 8.93613501838045, 0) [20250519_004347.]: Entered 'hyperbolic_regression'-Function [20250519_004348.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004348.]: Entered 'cubic_regression'-Function [20250519_004348.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004348.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 13.2494913489925, 25.0914701740206, 38.8081740444332, 49.1133034150896, 67.573566028728, 73.7655861480302, 82.0404940898918, 100)c(0, 0.7494913489925, 0.0914701740205999, 1.3081740444332, 0.886696584910403, 5.073566028728, 1.2344138519698, 5.45950591010821, 0)c(NA, 5.99593079194, 0.365880696082399, 3.48846411848854, 1.77339316982081, 8.11770564596479, 1.64588513595974, 6.23943532583795, 0) [20250519_004348.]: Logging df_agg: CpG#4 [20250519_004348.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004348.]: c(0, 13.2494913489925, 25.0914701740206, 38.8081740444332, 49.1133034150896, 67.573566028728, 73.7655861480302, 82.0404940898918, 100)[20250519_004348.]: c(0, 0.7494913489925, 0.0914701740205999, 1.3081740444332, 0.886696584910403, 5.073566028728, 1.2344138519698, 5.45950591010821, 0)[20250519_004348.]: c(NA, 5.99593079194, 0.365880696082399, 3.48846411848854, 1.77339316982081, 8.11770564596479, 1.64588513595974, 6.23943532583795, 0) [20250519_004348.]: Entered 'hyperbolic_regression'-Function [20250519_004348.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004348.]: Entered 'cubic_regression'-Function [20250519_004348.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004348.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.36101271071658, 23.0724671456609, 36.9926902689013, 50.9902012556783, 62.7269420395599, 76.6726263557269, 86.2959101486824, 100)c(0, 4.13898728928342, 1.9275328543391, 0.507309731098701, 0.990201255678301, 0.226942039559901, 1.6726263557269, 1.2040898513176, 0)c(NA, 33.1118983142674, 7.71013141735639, 1.35282594959654, 1.9804025113566, 0.363107263295842, 2.23016847430254, 1.37610268722012, 0) [20250519_004348.]: Logging df_agg: CpG#5 [20250519_004348.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004348.]: c(0, 8.36101271071658, 23.0724671456609, 36.9926902689013, 50.9902012556783, 62.7269420395599, 76.6726263557269, 86.2959101486824, 100)[20250519_004348.]: c(0, 4.13898728928342, 1.9275328543391, 0.507309731098701, 0.990201255678301, 0.226942039559901, 1.6726263557269, 1.2040898513176, 0)[20250519_004348.]: c(NA, 33.1118983142674, 7.71013141735639, 1.35282594959654, 1.9804025113566, 0.363107263295842, 2.23016847430254, 1.37610268722012, 0) [20250519_004348.]: Entered 'hyperbolic_regression'-Function [20250519_004348.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004349.]: Entered 'cubic_regression'-Function [20250519_004349.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004349.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.8139244542506, 26.5330423964606, 35.365455365759, 50.1054345020203, 64.9683746571925, 73.6331726521876, 86.9938602772804, 100)c(0, 0.6860755457494, 1.5330423964606, 2.134544634241, 0.105434502020302, 2.4683746571925, 1.3668273478124, 0.506139722719595, 0)c(NA, 5.4886043659952, 6.13216958584241, 5.69211902464267, 0.210869004040603, 3.949399451508, 1.82243646374987, 0.578445397393823, 0) [20250519_004349.]: Logging df_agg: CpG#6 [20250519_004349.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004349.]: c(0, 11.8139244542506, 26.5330423964606, 35.365455365759, 50.1054345020203, 64.9683746571925, 73.6331726521876, 86.9938602772804, 100)[20250519_004349.]: c(0, 0.6860755457494, 1.5330423964606, 2.134544634241, 0.105434502020302, 2.4683746571925, 1.3668273478124, 0.506139722719595, 0)[20250519_004349.]: c(NA, 5.4886043659952, 6.13216958584241, 5.69211902464267, 0.210869004040603, 3.949399451508, 1.82243646374987, 0.578445397393823, 0) [20250519_004349.]: Entered 'hyperbolic_regression'-Function [20250519_004349.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004349.]: Entered 'cubic_regression'-Function [20250519_004349.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004349.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.7880491100164, 26.1959234034328, 39.1978567589215, 54.3513505291649, 66.0567953704493, 75.1900867269869, 78.6051660349008, 100)c(0, 0.7119508899836, 1.1959234034328, 1.6978567589215, 4.3513505291649, 3.55679537044929, 0.190086726986905, 8.8948339650992, 0)c(NA, 5.6956071198688, 4.7836936137312, 4.52761802379068, 8.70270105832979, 5.69087259271887, 0.253448969315874, 10.1655245315419, 0) [20250519_004349.]: Logging df_agg: CpG#7 [20250519_004349.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004349.]: c(0, 11.7880491100164, 26.1959234034328, 39.1978567589215, 54.3513505291649, 66.0567953704493, 75.1900867269869, 78.6051660349008, 100)[20250519_004349.]: c(0, 0.7119508899836, 1.1959234034328, 1.6978567589215, 4.3513505291649, 3.55679537044929, 0.190086726986905, 8.8948339650992, 0)[20250519_004349.]: c(NA, 5.6956071198688, 4.7836936137312, 4.52761802379068, 8.70270105832979, 5.69087259271887, 0.253448969315874, 10.1655245315419, 0) [20250519_004349.]: Entered 'hyperbolic_regression'-Function [20250519_004349.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004349.]: Entered 'cubic_regression'-Function [20250519_004349.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004349.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 7.27382484148278, 24.9765598282779, 34.028313807905, 52.2946423509726, 65.0742432627639, 78.3047550247862, 81.0501991298613, 100)c(0, 5.22617515851722, 0.0234401717221004, 3.471686192095, 2.2946423509726, 2.5742432627639, 3.3047550247862, 6.4498008701387, 0)c(NA, 41.8094012681378, 0.0937606868884018, 9.25782984558666, 4.5892847019452, 4.11878922042224, 4.40634003304827, 7.37120099444423, 0) [20250519_004349.]: Logging df_agg: CpG#8 [20250519_004349.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004349.]: c(0, 7.27382484148278, 24.9765598282779, 34.028313807905, 52.2946423509726, 65.0742432627639, 78.3047550247862, 81.0501991298613, 100)[20250519_004349.]: c(0, 5.22617515851722, 0.0234401717221004, 3.471686192095, 2.2946423509726, 2.5742432627639, 3.3047550247862, 6.4498008701387, 0)[20250519_004349.]: c(NA, 41.8094012681378, 0.0937606868884018, 9.25782984558666, 4.5892847019452, 4.11878922042224, 4.40634003304827, 7.37120099444423, 0) [20250519_004349.]: Entered 'hyperbolic_regression'-Function [20250519_004349.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004350.]: Entered 'cubic_regression'-Function [20250519_004350.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004350.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.2068234201026, 28.0688737636578, 37.6661823177513, 52.3684233460174, 64.8636916649907, 74.2551330907062, 83.9343288872042, 100)c(0, 0.2931765798974, 3.0688737636578, 0.1661823177513, 2.3684233460174, 2.3636916649907, 0.744866909293805, 3.5656711127958, 0)c(NA, 2.3454126391792, 12.2754950546312, 0.4431528473368, 4.7368466920348, 3.78190666398511, 0.993155879058406, 4.07505270033805, 0) [20250519_004350.]: Logging df_agg: CpG#9 [20250519_004350.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004350.]: c(0, 12.2068234201026, 28.0688737636578, 37.6661823177513, 52.3684233460174, 64.8636916649907, 74.2551330907062, 83.9343288872042, 100)[20250519_004350.]: c(0, 0.2931765798974, 3.0688737636578, 0.1661823177513, 2.3684233460174, 2.3636916649907, 0.744866909293805, 3.5656711127958, 0)[20250519_004350.]: c(NA, 2.3454126391792, 12.2754950546312, 0.4431528473368, 4.7368466920348, 3.78190666398511, 0.993155879058406, 4.07505270033805, 0) [20250519_004350.]: Entered 'hyperbolic_regression'-Function [20250519_004350.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004350.]: Entered 'cubic_regression'-Function [20250519_004350.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004350.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.000228160853766634, 11.1587839347378, 25.8570776700364, 37.065004454281, 51.1881763075298, 65.4439754555105, 75.3005883283082, 82.6591025877361, 100)c(0.000228160853766634, 1.3412160652622, 0.8570776700364, 0.434995545718998, 1.1881763075298, 2.9439754555105, 0.300588328308194, 4.8408974122639, 0)c(NA, 10.7297285220976, 3.4283106801456, 1.15998812191733, 2.3763526150596, 4.7103607288168, 0.400784437744259, 5.53245418544446, 0) [20250519_004350.]: Logging df_agg: row_means [20250519_004350.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004350.]: c(0.000228160853766634, 11.1587839347378, 25.8570776700364, 37.065004454281, 51.1881763075298, 65.4439754555105, 75.3005883283082, 82.6591025877361, 100)[20250519_004350.]: c(0.000228160853766634, 1.3412160652622, 0.8570776700364, 0.434995545718998, 1.1881763075298, 2.9439754555105, 0.300588328308194, 4.8408974122639, 0)[20250519_004350.]: c(NA, 10.7297285220976, 3.4283106801456, 1.15998812191733, 2.3763526150596, 4.7103607288168, 0.400784437744259, 5.53245418544446, 0) [20250519_004350.]: Entered 'hyperbolic_regression'-Function [20250519_004350.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004350.]: Entered 'cubic_regression'-Function [20250519_004350.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004351.]: Entered 'solving_equations'-Function [20250519_004351.]: Solving cubic regression for CpG#1 Coefficients: 0Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20250519_004351.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -7.30533333333333Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20250519_004351.]: Samplename: 12.5 Root: 4.661 --> Root in between the borders! Added to results. Coefficients: -14.352Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20250519_004351.]: Samplename: 25 Root: 9.352 --> Root in between the borders! Added to results. Coefficients: -23.244Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20250519_004351.]: Samplename: 37.5 Root: 15.59 --> Root in between the borders! Added to results. Coefficients: -33.8645Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20250519_004351.]: Samplename: 50 Root: 23.601 --> Root in between the borders! Added to results. Coefficients: -45.318Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20250519_004351.]: Samplename: 62.5 Root: 33.121 --> Root in between the borders! Added to results. Coefficients: -54.857Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20250519_004351.]: Samplename: 75 Root: 42.001 --> Root in between the borders! Added to results. Coefficients: -62.062Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20250519_004351.]: Samplename: 87.5 Root: 49.51 --> Root in between the borders! Added to results. Coefficients: -90.01Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20250519_004351.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 [20250519_004351.]: Solving cubic regression for CpG#2 Coefficients: 0Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20250519_004351.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -6.05666666666666Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20250519_004351.]: Samplename: 12.5 Root: 8.415 --> Root in between the borders! Added to results. Coefficients: -15.656Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20250519_004351.]: Samplename: 25 Root: 21.016 --> Root in between the borders! Added to results. Coefficients: -22.054Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20250519_004351.]: Samplename: 37.5 Root: 28.984 --> Root in between the borders! Added to results. Coefficients: -31.945Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20250519_004351.]: Samplename: 50 Root: 40.726 --> Root in between the borders! Added to results. Coefficients: -49.68Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20250519_004351.]: Samplename: 62.5 Root: 60.322 --> Root in between the borders! Added to results. Coefficients: -58.6825Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20250519_004351.]: Samplename: 75 Root: 69.671 --> Root in between the borders! Added to results. Coefficients: -68.5533333333333Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20250519_004351.]: Samplename: 87.5 Root: 79.529 --> Root in between the borders! Added to results. Coefficients: -90.294Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20250519_004351.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 [20250519_004351.]: Solving cubic regression for CpG#3 Coefficients: 0Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20250519_004351.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -5.67Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20250519_004351.]: Samplename: 12.5 Root: 5.75 --> Root in between the borders! Added to results. Coefficients: -14.526Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20250519_004351.]: Samplename: 25 Root: 15.012 --> Root in between the borders! Added to results. Coefficients: -21.71Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20250519_004351.]: Samplename: 37.5 Root: 22.804 --> Root in between the borders! Added to results. Coefficients: -31.8725Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20250519_004351.]: Samplename: 50 Root: 34.308 --> Root in between the borders! Added to results. Coefficients: -42.986Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20250519_004351.]: Samplename: 62.5 Root: 47.638 --> Root in between the borders! Added to results. Coefficients: -54.0725Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20250519_004351.]: Samplename: 75 Root: 61.877 --> Root in between the borders! Added to results. Coefficients: -56.7533333333333Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20250519_004351.]: Samplename: 87.5 Root: 65.486 --> Root in between the borders! Added to results. Coefficients: -79.762Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20250519_004351.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 [20250519_004351.]: Solving cubic regression for CpG#4 Coefficients: 0Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20250519_004351.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -7.65533333333333Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20250519_004351.]: Samplename: 12.5 Root: 6.614 --> Root in between the borders! Added to results. Coefficients: -15.206Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20250519_004351.]: Samplename: 25 Root: 13.372 --> Root in between the borders! Added to results. Coefficients: -24.93Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20250519_004351.]: Samplename: 37.5 Root: 22.462 --> Root in between the borders! Added to results. Coefficients: -33.0395Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20250519_004351.]: Samplename: 50 Root: 30.424 --> Root in between the borders! Added to results. Coefficients: -49.658Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20250519_004351.]: Samplename: 62.5 Root: 48.07 --> Root in between the borders! Added to results. Coefficients: -55.942Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20250519_004351.]: Samplename: 75 Root: 55.318 --> Root in between the borders! Added to results. Coefficients: -64.9953333333333Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20250519_004351.]: Samplename: 87.5 Root: 66.469 --> Root in between the borders! Added to results. Coefficients: -87.724Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20250519_004351.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_004351.]: Solving cubic regression for CpG#5 Coefficients: 0Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_004351.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -4.144Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_004351.]: Samplename: 12.5 Root: 10.457 --> Root in between the borders! Added to results. Coefficients: -12.102Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_004351.]: Samplename: 25 Root: 26.323 --> Root in between the borders! Added to results. Coefficients: -20.536Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_004351.]: Samplename: 37.5 Root: 39.937 --> Root in between the borders! Added to results. Coefficients: -30.0715Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_004351.]: Samplename: 50 Root: 53.063 --> Root in between the borders! Added to results. Coefficients: -39.034Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_004351.]: Samplename: 62.5 Root: 63.959 --> Root in between the borders! Added to results. Coefficients: -51.059Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_004351.]: Samplename: 75 Root: 77.051 --> Root in between the borders! Added to results. Coefficients: -60.3906666666667Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_004351.]: Samplename: 87.5 Root: 86.31 --> Root in between the borders! Added to results. Coefficients: -75.446Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_004351.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_004351.]: Solving cubic regression for CpG#6 Coefficients: 0Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20250519_004351.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -6.54266666666667Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20250519_004351.]: Samplename: 12.5 Root: 8.15 --> Root in between the borders! Added to results. Coefficients: -15.692Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20250519_004351.]: Samplename: 25 Root: 19.28 --> Root in between the borders! Added to results. Coefficients: -21.804Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20250519_004351.]: Samplename: 37.5 Root: 26.553 --> Root in between the borders! Added to results. Coefficients: -33.2485Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20250519_004351.]: Samplename: 50 Root: 39.845 --> Root in between the borders! Added to results. Coefficients: -46.704Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20250519_004351.]: Samplename: 62.5 Root: 54.973 --> Root in between the borders! Added to results. Coefficients: -55.636Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20250519_004351.]: Samplename: 75 Root: 64.743 --> Root in between the borders! Added to results. Coefficients: -71.3493333333333Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20250519_004351.]: Samplename: 87.5 Root: 81.445 --> Root in between the borders! Added to results. Coefficients: -89.46Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20250519_004351.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_004351.]: Solving cubic regression for CpG#7 Coefficients: 0Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20250519_004351.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -4.18066666666667Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20250519_004351.]: Samplename: 12.5 Root: 3.662 --> Root in between the borders! Added to results. Coefficients: -10.05Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20250519_004351.]: Samplename: 25 Root: 9.013 --> Root in between the borders! Added to results. Coefficients: -16.236Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20250519_004351.]: Samplename: 37.5 Root: 14.96 --> Root in between the borders! Added to results. Coefficients: -24.8165Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20250519_004351.]: Samplename: 50 Root: 23.842 --> Root in between the borders! Added to results. Coefficients: -32.75Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20250519_004351.]: Samplename: 62.5 Root: 32.894 --> Root in between the borders! Added to results. Coefficients: -39.954Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20250519_004351.]: Samplename: 75 Root: 42.068 --> Root in between the borders! Added to results. Coefficients: -42.9206666666667Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20250519_004351.]: Samplename: 87.5 Root: 46.196 --> Root in between the borders! Added to results. Coefficients: -66.008Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20250519_004351.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_004351.]: Solving cubic regression for CpG#8 Coefficients: 0Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_004351.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -4.35066666666667Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_004351.]: Samplename: 12.5 Root: 8.961 --> Root in between the borders! Added to results. Coefficients: -15.834Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_004351.]: Samplename: 25 Root: 28.16 --> Root in between the borders! Added to results. Coefficients: -22.254Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_004351.]: Samplename: 37.5 Root: 37.189 --> Root in between the borders! Added to results. Coefficients: -36.529Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_004351.]: Samplename: 50 Root: 54.661 --> Root in between the borders! Added to results. Coefficients: -47.73Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_004351.]: Samplename: 62.5 Root: 66.642 --> Root in between the borders! Added to results. Coefficients: -60.5715Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_004351.]: Samplename: 75 Root: 79.079 --> Root in between the borders! Added to results. Coefficients: -63.414Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_004351.]: Samplename: 87.5 Root: 81.681 --> Root in between the borders! Added to results. Coefficients: -84.964Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_004351.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_004351.]: Solving cubic regression for CpG#9 Coefficients: 0Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20250519_004351.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -5.406Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20250519_004351.]: Samplename: 12.5 Root: 3.608 --> Root in between the borders! Added to results. Coefficients: -13.716Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20250519_004351.]: Samplename: 25 Root: 9.37 --> Root in between the borders! Added to results. Coefficients: -19.634Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20250519_004351.]: Samplename: 37.5 Root: 13.653 --> Root in between the borders! Added to results. Coefficients: -30.406Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20250519_004351.]: Samplename: 50 Root: 21.897 --> Root in between the borders! Added to results. Coefficients: -41.696Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20250519_004351.]: Samplename: 62.5 Root: 31.298 --> Root in between the borders! Added to results. Coefficients: -51.9135Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20250519_004351.]: Samplename: 75 Root: 40.689 --> Root in between the borders! Added to results. Coefficients: -64.5026666666667Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20250519_004351.]: Samplename: 87.5 Root: 53.907 --> Root in between the borders! Added to results. Coefficients: -92Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20250519_004351.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_004351.]: Solving cubic regression for row_means Coefficients: -0.000111111111110951Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20250519_004351.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -5.70137037037037Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20250519_004351.]: Samplename: 12.5 Root: 6.107 --> Root in between the borders! Added to results. Coefficients: -14.1261111111111Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20250519_004351.]: Samplename: 25 Root: 15.282 --> Root in between the borders! Added to results. Coefficients: -21.3781111111111Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20250519_004351.]: Samplename: 37.5 Root: 23.331 --> Root in between the borders! Added to results. Coefficients: -31.7548888888889Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20250519_004351.]: Samplename: 50 Root: 35.107 --> Root in between the borders! Added to results. Coefficients: -43.9507777777778Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20250519_004351.]: Samplename: 62.5 Root: 49.368 --> Root in between the borders! Added to results. Coefficients: -53.6321111111111Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20250519_004351.]: Samplename: 75 Root: 61.044 --> Root in between the borders! Added to results. Coefficients: -61.6602592592593Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20250519_004351.]: Samplename: 87.5 Root: 70.984 --> Root in between the borders! Added to results. Coefficients: -83.9632222222222Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20250519_004351.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 [20250519_004351.]: ### Starting with regression calculations ### [20250519_004351.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 [20250519_004352.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 4.66057108506072, 9.3520572622014, 15.5898391276712, 23.6006773278817, 33.1210812975699, 42.000790440049, 49.5097019721065, 100)c(0, 7.83942891493928, 15.6479427377986, 21.9101608723288, 26.3993226721183, 29.3789187024301, 32.999209559951, 37.9902980278935, 0)c(NA, 62.7154313195142, 62.5917709511944, 58.4270956595435, 52.7986453442366, 47.0062699238882, 43.9989460799347, 43.4174834604497, 0) [20250519_004352.]: Logging df_agg: CpG#1 [20250519_004352.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004352.]: c(0, 4.66057108506072, 9.3520572622014, 15.5898391276712, 23.6006773278817, 33.1210812975699, 42.000790440049, 49.5097019721065, 100)[20250519_004352.]: c(0, 7.83942891493928, 15.6479427377986, 21.9101608723288, 26.3993226721183, 29.3789187024301, 32.999209559951, 37.9902980278935, 0)[20250519_004352.]: c(NA, 62.7154313195142, 62.5917709511944, 58.4270956595435, 52.7986453442366, 47.0062699238882, 43.9989460799347, 43.4174834604497, 0) [20250519_004352.]: Entered 'hyperbolic_regression'-Function [20250519_004352.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004352.]: Entered 'cubic_regression'-Function [20250519_004352.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004352.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.41473135558285, 21.0155787157944, 28.98380769729, 40.7258283847219, 60.321752127534, 69.670868358929, 79.5285048174076, 100)c(0, 4.08526864441715, 3.9844212842056, 8.51619230271, 9.2741716152781, 2.178247872466, 5.329131641071, 7.97149518259241, 0)c(NA, 32.6821491553372, 15.9376851368224, 22.70984614056, 18.5483432305562, 3.4851965959456, 7.10550885476133, 9.11028020867704, 0) [20250519_004352.]: Logging df_agg: CpG#2 [20250519_004352.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004352.]: c(0, 8.41473135558285, 21.0155787157944, 28.98380769729, 40.7258283847219, 60.321752127534, 69.670868358929, 79.5285048174076, 100)[20250519_004352.]: c(0, 4.08526864441715, 3.9844212842056, 8.51619230271, 9.2741716152781, 2.178247872466, 5.329131641071, 7.97149518259241, 0)[20250519_004352.]: c(NA, 32.6821491553372, 15.9376851368224, 22.70984614056, 18.5483432305562, 3.4851965959456, 7.10550885476133, 9.11028020867704, 0) [20250519_004352.]: Entered 'hyperbolic_regression'-Function [20250519_004352.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004352.]: Entered 'cubic_regression'-Function [20250519_004352.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004353.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 5.74980505230347, 15.0124787349221, 22.8043512255536, 34.3082682140428, 47.6381691654944, 61.8773912993076, 65.4860304554385, 100)c(0, 6.75019494769653, 9.9875212650779, 14.6956487744464, 15.6917317859572, 14.8618308345056, 13.1226087006924, 22.0139695445615, 0)c(NA, 54.0015595815722, 39.9500850603116, 39.1883967318571, 31.3834635719144, 23.778929335209, 17.4968116009232, 25.1588223366417, 0) [20250519_004353.]: Logging df_agg: CpG#3 [20250519_004353.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004353.]: c(0, 5.74980505230347, 15.0124787349221, 22.8043512255536, 34.3082682140428, 47.6381691654944, 61.8773912993076, 65.4860304554385, 100)[20250519_004353.]: c(0, 6.75019494769653, 9.9875212650779, 14.6956487744464, 15.6917317859572, 14.8618308345056, 13.1226087006924, 22.0139695445615, 0)[20250519_004353.]: c(NA, 54.0015595815722, 39.9500850603116, 39.1883967318571, 31.3834635719144, 23.778929335209, 17.4968116009232, 25.1588223366417, 0) [20250519_004353.]: Entered 'hyperbolic_regression'-Function [20250519_004353.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004353.]: Entered 'cubic_regression'-Function [20250519_004353.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004353.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 6.6142663267093, 13.3723444572473, 22.4624407354305, 30.4240096190196, 48.070278909378, 55.3176226954937, 66.4686809672836, 100)c(0, 5.8857336732907, 11.6276555427527, 15.0375592645695, 19.5759903809804, 14.429721090622, 19.6823773045063, 21.0313190327164, 0)c(NA, 47.0858693863256, 46.5106221710108, 40.100158038852, 39.1519807619608, 23.0875537449952, 26.2431697393417, 24.0357931802473, 0) [20250519_004353.]: Logging df_agg: CpG#4 [20250519_004353.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004353.]: c(0, 6.6142663267093, 13.3723444572473, 22.4624407354305, 30.4240096190196, 48.070278909378, 55.3176226954937, 66.4686809672836, 100)[20250519_004353.]: c(0, 5.8857336732907, 11.6276555427527, 15.0375592645695, 19.5759903809804, 14.429721090622, 19.6823773045063, 21.0313190327164, 0)[20250519_004353.]: c(NA, 47.0858693863256, 46.5106221710108, 40.100158038852, 39.1519807619608, 23.0875537449952, 26.2431697393417, 24.0357931802473, 0) [20250519_004353.]: Entered 'hyperbolic_regression'-Function [20250519_004353.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004353.]: Entered 'cubic_regression'-Function [20250519_004353.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004353.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.4570373964758, 26.3229081557653, 39.9371114374682, 53.0630784939681, 63.9590902125397, 77.051078420522, 86.3095786035144, 100)c(0, 2.0429626035242, 1.3229081557653, 2.4371114374682, 3.0630784939681, 1.4590902125397, 2.051078420522, 1.1904213964856, 0)c(NA, 16.3437008281936, 5.2916326230612, 6.49896383324852, 6.1261569879362, 2.33454434006352, 2.73477122736267, 1.36048159598355, 0) [20250519_004353.]: Logging df_agg: CpG#5 [20250519_004353.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004353.]: c(0, 10.4570373964758, 26.3229081557653, 39.9371114374682, 53.0630784939681, 63.9590902125397, 77.051078420522, 86.3095786035144, 100)[20250519_004353.]: c(0, 2.0429626035242, 1.3229081557653, 2.4371114374682, 3.0630784939681, 1.4590902125397, 2.051078420522, 1.1904213964856, 0)[20250519_004353.]: c(NA, 16.3437008281936, 5.2916326230612, 6.49896383324852, 6.1261569879362, 2.33454434006352, 2.73477122736267, 1.36048159598355, 0) [20250519_004353.]: Entered 'hyperbolic_regression'-Function [20250519_004353.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004354.]: Entered 'cubic_regression'-Function [20250519_004354.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004354.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.15031343762292, 19.2804724179951, 26.5529108075111, 39.8450445790276, 54.9734681583699, 64.742519688394, 81.444841573602, 100)c(0, 4.34968656237708, 5.7195275820049, 10.9470891924889, 10.1549554209724, 7.5265318416301, 10.257480311606, 6.05515842639799, 0)c(NA, 34.7974924990166, 22.8781103280196, 29.1922378466371, 20.3099108419448, 12.0424509466082, 13.6766404154747, 6.92018105874057, 0) [20250519_004354.]: Logging df_agg: CpG#6 [20250519_004354.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004354.]: c(0, 8.15031343762292, 19.2804724179951, 26.5529108075111, 39.8450445790276, 54.9734681583699, 64.742519688394, 81.444841573602, 100)[20250519_004354.]: c(0, 4.34968656237708, 5.7195275820049, 10.9470891924889, 10.1549554209724, 7.5265318416301, 10.257480311606, 6.05515842639799, 0)[20250519_004354.]: c(NA, 34.7974924990166, 22.8781103280196, 29.1922378466371, 20.3099108419448, 12.0424509466082, 13.6766404154747, 6.92018105874057, 0) [20250519_004354.]: Entered 'hyperbolic_regression'-Function [20250519_004354.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004354.]: Entered 'cubic_regression'-Function [20250519_004354.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004354.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 3.66155933245296, 9.01334562885863, 14.9602090097958, 23.8420840440459, 32.8944868748769, 42.0684566960277, 46.1959470960303, 99.9999999999999)c(0, 8.83844066754704, 15.9866543711414, 22.5397909902042, 26.1579159559541, 29.6055131251231, 32.9315433039723, 41.3040529039697, 9.9475983006414e-14)c(NA, 70.7075253403763, 63.9466174845655, 60.1061093072112, 52.3158319119082, 47.368821000197, 43.9087244052964, 47.2046318902511, 9.9475983006414e-14) [20250519_004354.]: Logging df_agg: CpG#7 [20250519_004354.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004354.]: c(0, 3.66155933245296, 9.01334562885863, 14.9602090097958, 23.8420840440459, 32.8944868748769, 42.0684566960277, 46.1959470960303, 99.9999999999999)[20250519_004354.]: c(0, 8.83844066754704, 15.9866543711414, 22.5397909902042, 26.1579159559541, 29.6055131251231, 32.9315433039723, 41.3040529039697, 9.9475983006414e-14)[20250519_004354.]: c(NA, 70.7075253403763, 63.9466174845655, 60.1061093072112, 52.3158319119082, 47.368821000197, 43.9087244052964, 47.2046318902511, 9.9475983006414e-14) [20250519_004354.]: Entered 'hyperbolic_regression'-Function [20250519_004354.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004355.]: Entered 'cubic_regression'-Function [20250519_004355.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004355.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.96146907472175, 28.1602892189673, 37.1893361505397, 54.6609191320759, 66.6424746382684, 79.0794300908275, 81.6808296744085, 100)c(0, 3.53853092527825, 3.1602892189673, 0.310663849460298, 4.6609191320759, 4.1424746382684, 4.0794300908275, 5.8191703255915, 0)c(NA, 28.308247402226, 12.6411568758692, 0.828436931894127, 9.32183826415179, 6.62795942122943, 5.43924012110333, 6.65048037210457, 0) [20250519_004355.]: Logging df_agg: CpG#8 [20250519_004355.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004355.]: c(0, 8.96146907472175, 28.1602892189673, 37.1893361505397, 54.6609191320759, 66.6424746382684, 79.0794300908275, 81.6808296744085, 100)[20250519_004355.]: c(0, 3.53853092527825, 3.1602892189673, 0.310663849460298, 4.6609191320759, 4.1424746382684, 4.0794300908275, 5.8191703255915, 0)[20250519_004355.]: c(NA, 28.308247402226, 12.6411568758692, 0.828436931894127, 9.32183826415179, 6.62795942122943, 5.43924012110333, 6.65048037210457, 0) [20250519_004355.]: Entered 'hyperbolic_regression'-Function [20250519_004355.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004355.]: Entered 'cubic_regression'-Function [20250519_004355.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004355.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 3.60796342124625, 9.37027096567147, 13.6529015267019, 21.8965479410597, 31.2983747080124, 40.6887948365007, 53.9067876123417, 100)c(0, 8.89203657875375, 15.6297290343285, 23.8470984732981, 28.1034520589403, 31.2016252919876, 34.3112051634993, 33.5932123876583, 0)c(NA, 71.13629263003, 62.5189161373141, 63.5922625954616, 56.2069041178806, 49.9226004671802, 45.7482735513324, 38.3922427287523, 0) [20250519_004355.]: Logging df_agg: CpG#9 [20250519_004355.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004355.]: c(0, 3.60796342124625, 9.37027096567147, 13.6529015267019, 21.8965479410597, 31.2983747080124, 40.6887948365007, 53.9067876123417, 100)[20250519_004355.]: c(0, 8.89203657875375, 15.6297290343285, 23.8470984732981, 28.1034520589403, 31.2016252919876, 34.3112051634993, 33.5932123876583, 0)[20250519_004355.]: c(NA, 71.13629263003, 62.5189161373141, 63.5922625954616, 56.2069041178806, 49.9226004671802, 45.7482735513324, 38.3922427287523, 0) [20250519_004355.]: Entered 'hyperbolic_regression'-Function [20250519_004355.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004355.]: Entered 'cubic_regression'-Function [20250519_004355.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004355.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.000118249869729556, 6.10737244290489, 15.2822474794705, 23.3309318476314, 35.1067698776441, 49.3683845454072, 61.0436420088989, 70.9843655625978, 100)c(0.000118249869729556, 6.39262755709511, 9.7177525205295, 14.1690681523686, 14.8932301223559, 13.1316154545928, 13.9563579911011, 16.5156344374022, 0)c(NA, 51.1410204567609, 38.871010082118, 37.7841817396496, 29.7864602447118, 21.0105847273485, 18.6084773214681, 18.8750107856025, 0) [20250519_004355.]: Logging df_agg: row_means [20250519_004355.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004355.]: c(0.000118249869729556, 6.10737244290489, 15.2822474794705, 23.3309318476314, 35.1067698776441, 49.3683845454072, 61.0436420088989, 70.9843655625978, 100)[20250519_004355.]: c(0.000118249869729556, 6.39262755709511, 9.7177525205295, 14.1690681523686, 14.8932301223559, 13.1316154545928, 13.9563579911011, 16.5156344374022, 0)[20250519_004355.]: c(NA, 51.1410204567609, 38.871010082118, 37.7841817396496, 29.7864602447118, 21.0105847273485, 18.6084773214681, 18.8750107856025, 0) [20250519_004355.]: Entered 'hyperbolic_regression'-Function [20250519_004355.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004356.]: Entered 'cubic_regression'-Function [20250519_004356.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004356.]: Entered 'solving_equations'-Function [20250519_004356.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 78.9992198550223 [20250519_004356.]: Samplename: Sample#1 Root: 78.999 --> Root in between the borders! Added to results. Hyperbolic solved: 31.2870579236875 [20250519_004356.]: Samplename: Sample#10 Root: 31.287 --> Root in between the borders! Added to results. Hyperbolic solved: 42.7215290847412 [20250519_004356.]: Samplename: Sample#2 Root: 42.722 --> Root in between the borders! Added to results. Hyperbolic solved: 57.8350974981729 [20250519_004356.]: Samplename: Sample#3 Root: 57.835 --> Root in between the borders! Added to results. Hyperbolic solved: 11.2415690196975 [20250519_004356.]: Samplename: Sample#4 Root: 11.242 --> Root in between the borders! Added to results. Hyperbolic solved: 23.5440570752846 [20250519_004356.]: Samplename: Sample#5 Root: 23.544 --> Root in between the borders! Added to results. Hyperbolic solved: 24.7858714956021 [20250519_004356.]: Samplename: Sample#6 Root: 24.786 --> Root in between the borders! Added to results. Hyperbolic solved: 46.4156211496502 [20250519_004356.]: Samplename: Sample#7 Root: 46.416 --> Root in between the borders! Added to results. Hyperbolic solved: 84.4614182359021 [20250519_004356.]: Samplename: Sample#8 Root: 84.461 --> Root in between the borders! Added to results. Hyperbolic solved: -1.41454023853738 [20250519_004356.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -1.415 --> '-10 < root < 0' --> substitute 0 [20250519_004356.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 76.6628425084681 [20250519_004356.]: Samplename: Sample#1 Root: 76.663 --> Root in between the borders! Added to results. Hyperbolic solved: 31.002922067723 [20250519_004356.]: Samplename: Sample#10 Root: 31.003 --> Root in between the borders! Added to results. Hyperbolic solved: 42.8794162472613 [20250519_004356.]: Samplename: Sample#2 Root: 42.879 --> Root in between the borders! Added to results. Hyperbolic solved: 59.1562251576939 [20250519_004356.]: Samplename: Sample#3 Root: 59.156 --> Root in between the borders! Added to results. Hyperbolic solved: 4.04613842773285 [20250519_004356.]: Samplename: Sample#4 Root: 4.046 --> Root in between the borders! Added to results. Hyperbolic solved: 19.9574827771543 [20250519_004356.]: Samplename: Sample#5 Root: 19.957 --> Root in between the borders! Added to results. Hyperbolic solved: 17.4372350357645 [20250519_004356.]: Samplename: Sample#6 Root: 17.437 --> Root in between the borders! Added to results. Hyperbolic solved: 41.0968872148931 [20250519_004356.]: Samplename: Sample#7 Root: 41.097 --> Root in between the borders! Added to results. Hyperbolic solved: 85.7111018674234 [20250519_004356.]: Samplename: Sample#8 Root: 85.711 --> Root in between the borders! Added to results. Hyperbolic solved: 2.08554114625032 [20250519_004356.]: Samplename: Sample#9 Root: 2.086 --> Root in between the borders! Added to results. [20250519_004356.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 74.5413598656994 [20250519_004356.]: Samplename: Sample#1 Root: 74.541 --> Root in between the borders! Added to results. Hyperbolic solved: 28.3514323412579 [20250519_004356.]: Samplename: Sample#10 Root: 28.351 --> Root in between the borders! Added to results. Hyperbolic solved: 42.6007641487945 [20250519_004356.]: Samplename: Sample#2 Root: 42.601 --> Root in between the borders! Added to results. Hyperbolic solved: 56.3207791919885 [20250519_004356.]: Samplename: Sample#3 Root: 56.321 --> Root in between the borders! Added to results. Hyperbolic solved: 7.98800399083 [20250519_004356.]: Samplename: Sample#4 Root: 7.988 --> Root in between the borders! Added to results. Hyperbolic solved: 24.6963927948962 [20250519_004356.]: Samplename: Sample#5 Root: 24.696 --> Root in between the borders! Added to results. Hyperbolic solved: 26.8806215804592 [20250519_004356.]: Samplename: Sample#6 Root: 26.881 --> Root in between the borders! Added to results. Hyperbolic solved: 44.8239489304102 [20250519_004356.]: Samplename: Sample#7 Root: 44.824 --> Root in between the borders! Added to results. Hyperbolic solved: 84.669654917361 [20250519_004356.]: Samplename: Sample#8 Root: 84.67 --> Root in between the borders! Added to results. Hyperbolic solved: -1.26160051687973 [20250519_004356.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -1.262 --> '-10 < root < 0' --> substitute 0 [20250519_004356.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 75.8530019120299 [20250519_004356.]: Samplename: Sample#1 Root: 75.853 --> Root in between the borders! Added to results. Hyperbolic solved: 29.0711677507654 [20250519_004356.]: Samplename: Sample#10 Root: 29.071 --> Root in between the borders! Added to results. Hyperbolic solved: 44.0485537238583 [20250519_004356.]: Samplename: Sample#2 Root: 44.049 --> Root in between the borders! Added to results. Hyperbolic solved: 58.7878536128746 [20250519_004356.]: Samplename: Sample#3 Root: 58.788 --> Root in between the borders! Added to results. Hyperbolic solved: 11.0370782280591 [20250519_004356.]: Samplename: Sample#4 Root: 11.037 --> Root in between the borders! Added to results. Hyperbolic solved: 23.0042107924666 [20250519_004356.]: Samplename: Sample#5 Root: 23.004 --> Root in between the borders! Added to results. Hyperbolic solved: 27.9521038368967 [20250519_004356.]: Samplename: Sample#6 Root: 27.952 --> Root in between the borders! Added to results. Hyperbolic solved: 42.50025622677 [20250519_004356.]: Samplename: Sample#7 Root: 42.5 --> Root in between the borders! Added to results. Hyperbolic solved: 84.6870942263369 [20250519_004356.]: Samplename: Sample#8 Root: 84.687 --> Root in between the borders! Added to results. Hyperbolic solved: 3.01041300417756 [20250519_004356.]: Samplename: Sample#9 Root: 3.01 --> Root in between the borders! Added to results. [20250519_004357.]: Solving cubic regression for CpG#5 Coefficients: -47.8373333333333Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_004357.]: Samplename: Sample#1 Root: 73.686 --> Root in between the borders! Added to results. Coefficients: -13.588Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_004357.]: Samplename: Sample#10 Root: 28.906 --> Root in between the borders! Added to results. Coefficients: -25.3211428571429Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_004357.]: Samplename: Sample#2 Root: 46.761 --> Root in between the borders! Added to results. Coefficients: -32.064Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_004357.]: Samplename: Sample#3 Root: 55.588 --> Root in between the borders! Added to results. Coefficients: -4.074Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_004357.]: Samplename: Sample#4 Root: 10.297 --> Root in between the borders! Added to results. Coefficients: -11.434Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_004357.]: Samplename: Sample#5 Root: 25.131 --> Root in between the borders! Added to results. Coefficients: -13.294Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_004357.]: Samplename: Sample#6 Root: 28.402 --> Root in between the borders! Added to results. Coefficients: -24.288Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_004357.]: Samplename: Sample#7 Root: 45.332 --> Root in between the borders! Added to results. Coefficients: -63.134Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_004357.]: Samplename: Sample#8 Root: 88.909 --> Root in between the borders! Added to results. Coefficients: 0.0360000000000005Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_004357.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.102 --> '-10 < root < 0' --> substitute 0 [20250519_004357.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 79.2062097176894 [20250519_004357.]: Samplename: Sample#1 Root: 79.206 --> Root in between the borders! Added to results. Hyperbolic solved: 30.234912908094 [20250519_004357.]: Samplename: Sample#10 Root: 30.235 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8992139020538 [20250519_004357.]: Samplename: Sample#2 Root: 41.899 --> Root in between the borders! Added to results. Hyperbolic solved: 56.8778124304882 [20250519_004357.]: Samplename: Sample#3 Root: 56.878 --> Root in between the borders! Added to results. Hyperbolic solved: 8.80900690226663 [20250519_004357.]: Samplename: Sample#4 Root: 8.809 --> Root in between the borders! Added to results. Hyperbolic solved: 18.6793858198319 [20250519_004357.]: Samplename: Sample#5 Root: 18.679 --> Root in between the borders! Added to results. Hyperbolic solved: 29.9638526705022 [20250519_004357.]: Samplename: Sample#6 Root: 29.964 --> Root in between the borders! Added to results. Hyperbolic solved: 42.8669225173731 [20250519_004357.]: Samplename: Sample#7 Root: 42.867 --> Root in between the borders! Added to results. Hyperbolic solved: 86.6206311389839 [20250519_004357.]: Samplename: Sample#8 Root: 86.621 --> Root in between the borders! Added to results. Hyperbolic solved: 1.38882504570857 [20250519_004357.]: Samplename: Sample#9 Root: 1.389 --> Root in between the borders! Added to results. [20250519_004357.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 77.5203002291111 [20250519_004357.]: Samplename: Sample#1 Root: 77.52 --> Root in between the borders! Added to results. Hyperbolic solved: 27.0810020209347 [20250519_004357.]: Samplename: Sample#10 Root: 27.081 --> Root in between the borders! Added to results. Hyperbolic solved: 48.4274819445682 [20250519_004357.]: Samplename: Sample#2 Root: 48.427 --> Root in between the borders! Added to results. Hyperbolic solved: 58.8711296078074 [20250519_004357.]: Samplename: Sample#3 Root: 58.871 --> Root in between the borders! Added to results. Hyperbolic solved: 13.3245830390029 [20250519_004357.]: Samplename: Sample#4 Root: 13.325 --> Root in between the borders! Added to results. Hyperbolic solved: 26.9731029843141 [20250519_004357.]: Samplename: Sample#5 Root: 26.973 --> Root in between the borders! Added to results. Hyperbolic solved: 30.9519756409676 [20250519_004357.]: Samplename: Sample#6 Root: 30.952 --> Root in between the borders! Added to results. Hyperbolic solved: 45.7349250650843 [20250519_004357.]: Samplename: Sample#7 Root: 45.735 --> Root in between the borders! Added to results. Hyperbolic solved: 84.5977214697661 [20250519_004357.]: Samplename: Sample#8 Root: 84.598 --> Root in between the borders! Added to results. Hyperbolic solved: -2.87252278311384 [20250519_004357.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.873 --> '-10 < root < 0' --> substitute 0 [20250519_004357.]: Solving cubic regression for CpG#8 Coefficients: -55.3573333333333Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_004357.]: Samplename: Sample#1 Root: 74.173 --> Root in between the borders! Added to results. Coefficients: -17.574Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_004357.]: Samplename: Sample#10 Root: 30.7 --> Root in between the borders! Added to results. Coefficients: -22.9425714285714Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_004357.]: Samplename: Sample#2 Root: 38.106 --> Root in between the borders! Added to results. Coefficients: -42.849Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_004357.]: Samplename: Sample#3 Root: 61.571 --> Root in between the borders! Added to results. Coefficients: -4.604Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_004357.]: Samplename: Sample#4 Root: 9.446 --> Root in between the borders! Added to results. Coefficients: -11.389Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_004357.]: Samplename: Sample#5 Root: 21.295 --> Root in between the borders! Added to results. Coefficients: -25.784Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_004357.]: Samplename: Sample#6 Root: 41.8 --> Root in between the borders! Added to results. Coefficients: -30.746Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_004357.]: Samplename: Sample#7 Root: 47.937 --> Root in between the borders! Added to results. Coefficients: -66.912Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_004357.]: Samplename: Sample#8 Root: 84.816 --> Root in between the borders! Added to results. Coefficients: 3.176Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_004357.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -7.574 --> '-10 < root < 0' --> substitute 0 [20250519_004357.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 80.5447416574246 [20250519_004357.]: Samplename: Sample#1 Root: 80.545 --> Root in between the borders! Added to results. Hyperbolic solved: 27.8054725532271 [20250519_004357.]: Samplename: Sample#10 Root: 27.805 --> Root in between the borders! Added to results. Hyperbolic solved: 46.257978181877 [20250519_004357.]: Samplename: Sample#2 Root: 46.258 --> Root in between the borders! Added to results. Hyperbolic solved: 57.1842723611193 [20250519_004357.]: Samplename: Sample#3 Root: 57.184 --> Root in between the borders! Added to results. Hyperbolic solved: 8.63689944440251 [20250519_004357.]: Samplename: Sample#4 Root: 8.637 --> Root in between the borders! Added to results. Hyperbolic solved: 24.2116725772085 [20250519_004357.]: Samplename: Sample#5 Root: 24.212 --> Root in between the borders! Added to results. Hyperbolic solved: 39.6334888107959 [20250519_004357.]: Samplename: Sample#6 Root: 39.633 --> Root in between the borders! Added to results. Hyperbolic solved: 44.3019478766814 [20250519_004357.]: Samplename: Sample#7 Root: 44.302 --> Root in between the borders! Added to results. Hyperbolic solved: 87.3231552867261 [20250519_004357.]: Samplename: Sample#8 Root: 87.323 --> Root in between the borders! Added to results. Hyperbolic solved: -1.1792994162273 [20250519_004357.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -1.179 --> '-10 < root < 0' --> substitute 0 [20250519_004357.]: Solving hyperbolic regression for row_means Hyperbolic solved: 76.7712420256811 [20250519_004357.]: Samplename: Sample#1 Root: 76.771 --> Root in between the borders! Added to results. Hyperbolic solved: 28.8491793177963 [20250519_004357.]: Samplename: Sample#10 Root: 28.849 --> Root in between the borders! Added to results. Hyperbolic solved: 43.0342096405004 [20250519_004357.]: Samplename: Sample#2 Root: 43.034 --> Root in between the borders! Added to results. Hyperbolic solved: 57.634070615517 [20250519_004357.]: Samplename: Sample#3 Root: 57.634 --> Root in between the borders! Added to results. Hyperbolic solved: 8.87182398849477 [20250519_004357.]: Samplename: Sample#4 Root: 8.872 --> Root in between the borders! Added to results. Hyperbolic solved: 22.1989155573519 [20250519_004357.]: Samplename: Sample#5 Root: 22.199 --> Root in between the borders! Added to results. Hyperbolic solved: 29.2140219298625 [20250519_004357.]: Samplename: Sample#6 Root: 29.214 --> Root in between the borders! Added to results. Hyperbolic solved: 43.9371957594463 [20250519_004357.]: Samplename: Sample#7 Root: 43.937 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6706924479325 [20250519_004357.]: Samplename: Sample#8 Root: 85.671 --> Root in between the borders! Added to results. Hyperbolic solved: -0.55137023392917 [20250519_004357.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.551 --> '-10 < root < 0' --> substitute 0 [20250519_004357.]: Entered 'solving_equations'-Function [20250519_004357.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 0 [20250519_004357.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 14.1480167109277 [20250519_004357.]: Samplename: 12.5 Root: 14.148 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1398443790446 [20250519_004357.]: Samplename: 25 Root: 26.14 --> Root in between the borders! Added to results. Hyperbolic solved: 39.3762039523382 [20250519_004357.]: Samplename: 37.5 Root: 39.376 --> Root in between the borders! Added to results. Hyperbolic solved: 52.9476244028783 [20250519_004357.]: Samplename: 50 Root: 52.948 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4195105873419 [20250519_004357.]: Samplename: 62.5 Root: 65.42 --> Root in between the borders! Added to results. Hyperbolic solved: 74.433837698331 [20250519_004357.]: Samplename: 75 Root: 74.434 --> Root in between the borders! Added to results. Hyperbolic solved: 80.5559264925808 [20250519_004357.]: Samplename: 87.5 Root: 80.556 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_004357.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_004357.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 0 [20250519_004357.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 10.7812303822548 [20250519_004357.]: Samplename: 12.5 Root: 10.781 --> Root in between the borders! Added to results. Hyperbolic solved: 26.0648314401535 [20250519_004357.]: Samplename: 25 Root: 26.065 --> Root in between the borders! Added to results. Hyperbolic solved: 35.1980950192158 [20250519_004357.]: Samplename: 37.5 Root: 35.198 --> Root in between the borders! Added to results. Hyperbolic solved: 47.9203836090837 [20250519_004357.]: Samplename: 50 Root: 47.92 --> Root in between the borders! Added to results. Hyperbolic solved: 67.275750545092 [20250519_004357.]: Samplename: 62.5 Root: 67.276 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7278143684244 [20250519_004357.]: Samplename: 75 Root: 75.728 --> Root in between the borders! Added to results. Hyperbolic solved: 84.1258428690749 [20250519_004357.]: Samplename: 87.5 Root: 84.126 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_004357.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_004357.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 0 [20250519_004357.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 10.8467198180169 [20250519_004357.]: Samplename: 12.5 Root: 10.847 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1449700612555 [20250519_004357.]: Samplename: 25 Root: 26.145 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2865760013773 [20250519_004357.]: Samplename: 37.5 Root: 37.287 --> Root in between the borders! Added to results. Hyperbolic solved: 51.4114078772697 [20250519_004357.]: Samplename: 50 Root: 51.411 --> Root in between the borders! Added to results. Hyperbolic solved: 65.0139634978039 [20250519_004357.]: Samplename: 62.5 Root: 65.014 --> Root in between the borders! Added to results. Hyperbolic solved: 76.9921394830877 [20250519_004357.]: Samplename: 75 Root: 76.992 --> Root in between the borders! Added to results. Hyperbolic solved: 79.6808818589171 [20250519_004357.]: Samplename: 87.5 Root: 79.681 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_004357.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_004357.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 0 [20250519_004357.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 13.2494913489925 [20250519_004357.]: Samplename: 12.5 Root: 13.249 --> Root in between the borders! Added to results. Hyperbolic solved: 25.0914701740206 [20250519_004357.]: Samplename: 25 Root: 25.091 --> Root in between the borders! Added to results. Hyperbolic solved: 38.8081740444332 [20250519_004357.]: Samplename: 37.5 Root: 38.808 --> Root in between the borders! Added to results. Hyperbolic solved: 49.1133034150896 [20250519_004357.]: Samplename: 50 Root: 49.113 --> Root in between the borders! Added to results. Hyperbolic solved: 67.573566028728 [20250519_004357.]: Samplename: 62.5 Root: 67.574 --> Root in between the borders! Added to results. Hyperbolic solved: 73.7655861480302 [20250519_004357.]: Samplename: 75 Root: 73.766 --> Root in between the borders! Added to results. Hyperbolic solved: 82.0404940898918 [20250519_004357.]: Samplename: 87.5 Root: 82.04 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_004357.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_004357.]: Solving cubic regression for CpG#5 Coefficients: 0Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_004357.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -4.144Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_004357.]: Samplename: 12.5 Root: 10.457 --> Root in between the borders! Added to results. Coefficients: -12.102Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_004357.]: Samplename: 25 Root: 26.323 --> Root in between the borders! Added to results. Coefficients: -20.536Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_004357.]: Samplename: 37.5 Root: 39.937 --> Root in between the borders! Added to results. Coefficients: -30.0715Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_004357.]: Samplename: 50 Root: 53.063 --> Root in between the borders! Added to results. Coefficients: -39.034Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_004357.]: Samplename: 62.5 Root: 63.959 --> Root in between the borders! Added to results. Coefficients: -51.059Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_004357.]: Samplename: 75 Root: 77.051 --> Root in between the borders! Added to results. Coefficients: -60.3906666666667Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_004357.]: Samplename: 87.5 Root: 86.31 --> Root in between the borders! Added to results. Coefficients: -75.446Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_004357.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_004357.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 0 [20250519_004357.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 11.8139244542506 [20250519_004357.]: Samplename: 12.5 Root: 11.814 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5330423964606 [20250519_004357.]: Samplename: 25 Root: 26.533 --> Root in between the borders! Added to results. Hyperbolic solved: 35.365455365759 [20250519_004357.]: Samplename: 37.5 Root: 35.365 --> Root in between the borders! Added to results. Hyperbolic solved: 50.1054345020203 [20250519_004357.]: Samplename: 50 Root: 50.105 --> Root in between the borders! Added to results. Hyperbolic solved: 64.9683746571925 [20250519_004357.]: Samplename: 62.5 Root: 64.968 --> Root in between the borders! Added to results. Hyperbolic solved: 73.6331726521876 [20250519_004357.]: Samplename: 75 Root: 73.633 --> Root in between the borders! Added to results. Hyperbolic solved: 86.9938602772804 [20250519_004357.]: Samplename: 87.5 Root: 86.994 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_004357.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_004357.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 0 [20250519_004357.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 11.7880491100164 [20250519_004357.]: Samplename: 12.5 Root: 11.788 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1959234034328 [20250519_004357.]: Samplename: 25 Root: 26.196 --> Root in between the borders! Added to results. Hyperbolic solved: 39.1978567589215 [20250519_004357.]: Samplename: 37.5 Root: 39.198 --> Root in between the borders! Added to results. Hyperbolic solved: 54.3513505291649 [20250519_004357.]: Samplename: 50 Root: 54.351 --> Root in between the borders! Added to results. Hyperbolic solved: 66.0567953704493 [20250519_004357.]: Samplename: 62.5 Root: 66.057 --> Root in between the borders! Added to results. Hyperbolic solved: 75.1900867269869 [20250519_004357.]: Samplename: 75 Root: 75.19 --> Root in between the borders! Added to results. Hyperbolic solved: 78.6051660349008 [20250519_004357.]: Samplename: 87.5 Root: 78.605 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_004357.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_004357.]: Solving cubic regression for CpG#8 Coefficients: 0Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_004357.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -4.35066666666667Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_004357.]: Samplename: 12.5 Root: 8.961 --> Root in between the borders! Added to results. Coefficients: -15.834Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_004357.]: Samplename: 25 Root: 28.16 --> Root in between the borders! Added to results. Coefficients: -22.254Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_004357.]: Samplename: 37.5 Root: 37.189 --> Root in between the borders! Added to results. Coefficients: -36.529Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_004357.]: Samplename: 50 Root: 54.661 --> Root in between the borders! Added to results. Coefficients: -47.73Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_004357.]: Samplename: 62.5 Root: 66.642 --> Root in between the borders! Added to results. Coefficients: -60.5715Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_004357.]: Samplename: 75 Root: 79.079 --> Root in between the borders! Added to results. Coefficients: -63.414Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_004357.]: Samplename: 87.5 Root: 81.681 --> Root in between the borders! Added to results. Coefficients: -84.964Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_004357.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_004357.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 0 [20250519_004357.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 12.2068234201026 [20250519_004357.]: Samplename: 12.5 Root: 12.207 --> Root in between the borders! Added to results. Hyperbolic solved: 28.0688737636578 [20250519_004357.]: Samplename: 25 Root: 28.069 --> Root in between the borders! Added to results. Hyperbolic solved: 37.6661823177513 [20250519_004357.]: Samplename: 37.5 Root: 37.666 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3684233460174 [20250519_004357.]: Samplename: 50 Root: 52.368 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8636916649907 [20250519_004357.]: Samplename: 62.5 Root: 64.864 --> Root in between the borders! Added to results. Hyperbolic solved: 74.2551330907062 [20250519_004357.]: Samplename: 75 Root: 74.255 --> Root in between the borders! Added to results. Hyperbolic solved: 83.9343288872042 [20250519_004357.]: Samplename: 87.5 Root: 83.934 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_004357.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_004357.]: Solving hyperbolic regression for row_means Hyperbolic solved: 0.000228160853766634 [20250519_004357.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 11.1587839347378 [20250519_004357.]: Samplename: 12.5 Root: 11.159 --> Root in between the borders! Added to results. Hyperbolic solved: 25.8570776700364 [20250519_004357.]: Samplename: 25 Root: 25.857 --> Root in between the borders! Added to results. Hyperbolic solved: 37.065004454281 [20250519_004357.]: Samplename: 37.5 Root: 37.065 --> Root in between the borders! Added to results. Hyperbolic solved: 51.1881763075298 [20250519_004357.]: Samplename: 50 Root: 51.188 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4439754555105 [20250519_004357.]: Samplename: 62.5 Root: 65.444 --> Root in between the borders! Added to results. Hyperbolic solved: 75.3005883283082 [20250519_004357.]: Samplename: 75 Root: 75.301 --> Root in between the borders! Added to results. Hyperbolic solved: 82.6591025877361 [20250519_004357.]: Samplename: 87.5 Root: 82.659 --> Root in between the borders! Added to results. Hyperbolic solved: 100.000153506599 [20250519_004357.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 [20250519_004359.]: on_start: using future::plan("sequential") [20250519_004359.]: Entered 'clean_dt'-Function [20250519_004359.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_004359.]: got experimental data [20250519_004359.]: Entered 'clean_dt'-Function [20250519_004359.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_004359.]: got calibration data [20250519_004359.]: ### Starting with regression calculations ### [20250519_004359.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_004359.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_004359.]: Logging df_agg: CpG#1 [20250519_004359.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004359.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_004359.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_004359.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_004359.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_004359.]: Entered 'hyperbolic_regression'-Function [20250519_004359.]: 'hyperbolic_regression': minmax = FALSE [20250519_004359.]: Entered 'cubic_regression'-Function [20250519_004359.]: 'cubic_regression': minmax = FALSE [20250519_004359.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_004359.]: Logging df_agg: CpG#2 [20250519_004359.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004359.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_004359.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_004359.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_004359.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_004359.]: Entered 'hyperbolic_regression'-Function [20250519_004359.]: 'hyperbolic_regression': minmax = FALSE [20250519_004400.]: Entered 'cubic_regression'-Function [20250519_004400.]: 'cubic_regression': minmax = FALSE [20250519_004400.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_004400.]: Logging df_agg: CpG#3 [20250519_004400.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004400.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_004400.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_004400.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_004400.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_004400.]: Entered 'hyperbolic_regression'-Function [20250519_004400.]: 'hyperbolic_regression': minmax = FALSE [20250519_004400.]: Entered 'cubic_regression'-Function [20250519_004400.]: 'cubic_regression': minmax = FALSE [20250519_004400.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_004400.]: Logging df_agg: CpG#4 [20250519_004400.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004400.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_004400.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_004400.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_004400.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_004400.]: Entered 'hyperbolic_regression'-Function [20250519_004400.]: 'hyperbolic_regression': minmax = FALSE [20250519_004400.]: Entered 'cubic_regression'-Function [20250519_004400.]: 'cubic_regression': minmax = FALSE [20250519_004400.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_004400.]: Logging df_agg: CpG#5 [20250519_004400.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004400.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_004400.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_004400.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_004400.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_004400.]: Entered 'hyperbolic_regression'-Function [20250519_004400.]: 'hyperbolic_regression': minmax = FALSE [20250519_004400.]: Entered 'cubic_regression'-Function [20250519_004400.]: 'cubic_regression': minmax = FALSE [20250519_004400.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_004400.]: Logging df_agg: CpG#6 [20250519_004400.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004400.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_004400.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_004400.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_004400.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_004400.]: Entered 'hyperbolic_regression'-Function [20250519_004400.]: 'hyperbolic_regression': minmax = FALSE [20250519_004401.]: Entered 'cubic_regression'-Function [20250519_004401.]: 'cubic_regression': minmax = FALSE [20250519_004401.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_004401.]: Logging df_agg: CpG#7 [20250519_004401.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004401.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_004401.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_004401.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_004401.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_004401.]: Entered 'hyperbolic_regression'-Function [20250519_004401.]: 'hyperbolic_regression': minmax = FALSE [20250519_004401.]: Entered 'cubic_regression'-Function [20250519_004401.]: 'cubic_regression': minmax = FALSE [20250519_004401.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_004401.]: Logging df_agg: CpG#8 [20250519_004401.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004401.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_004401.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_004401.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_004401.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_004401.]: Entered 'hyperbolic_regression'-Function [20250519_004401.]: 'hyperbolic_regression': minmax = FALSE [20250519_004401.]: Entered 'cubic_regression'-Function [20250519_004401.]: 'cubic_regression': minmax = FALSE [20250519_004401.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_004401.]: Logging df_agg: CpG#9 [20250519_004401.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004401.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_004401.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_004401.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_004401.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_004401.]: Entered 'hyperbolic_regression'-Function [20250519_004401.]: 'hyperbolic_regression': minmax = FALSE [20250519_004401.]: Entered 'cubic_regression'-Function [20250519_004401.]: 'cubic_regression': minmax = FALSE [20250519_004401.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723122, 2.47760998435345)c(5.94911111111111, 0.84962962962963, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703704, 19.6995555555555, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_004401.]: Logging df_agg: row_means [20250519_004401.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004401.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_004401.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723122, 2.47760998435345)[20250519_004401.]: c(5.94911111111111, 0.84962962962963, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_004401.]: c(NA, 6.79703703703704, 19.6995555555555, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_004401.]: Entered 'hyperbolic_regression'-Function [20250519_004401.]: 'hyperbolic_regression': minmax = FALSE [20250519_004402.]: Entered 'cubic_regression'-Function [20250519_004402.]: 'cubic_regression': minmax = FALSE [20250519_004402.]: ### Starting with plotting ### [20250519_004402.]: Creating plot No. 1 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG1.png [20250519_004402.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -937.719419567728 , a = -108.56796364622 , b = -937.719419567728 , d = -232.057111323599 [20250519_004403.]: # CpG-site: CpG#1 Cubic: Using a = 6.53413423120091e-05 , b = -0.0055806968734969 , c = 0.784061853455188 , d = 1.93182659932656 [20250519_004404.]: Creating plot No. 2 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG2.png [20250519_004404.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = -2393.1426335766 , a = -141.862264327279 , b = -2393.1426335766 , d = -268.331583599043 [20250519_004404.]: # CpG-site: CpG#2 Cubic: Using a = 8.04237934904601e-06 , b = 0.00293158941798942 , c = 0.514821742825076 , d = 9.27667003367003 [20250519_004406.]: Creating plot No. 3 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG3.png [20250519_004406.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = -1477.46855437885 , a = -170.424507883158 , b = -1477.46855437885 , d = -327.190157171162 [20250519_004406.]: # CpG-site: CpG#3 Cubic: Using a = 2.30555869809204e-05 , b = -0.000797009331409346 , c = 0.62783129228796 , d = 3.88705218855218 [20250519_004408.]: Creating plot No. 4 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG4.png [20250519_004408.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = -2115.87792930847 , a = -162.169768036114 , b = -2115.87792930847 , d = -299.662268971294 [20250519_004408.]: # CpG-site: CpG#4 Cubic: Using a = 3.2166356902357e-05 , b = -0.0015913142857143 , c = 0.697398364598366 , d = 6.17055050505048 [20250519_004409.]: Creating plot No. 5 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG5.png [20250519_004409.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = -1383.32738741788 , a = -151.440067063617 , b = -1383.32738741788 , d = -304.200253800186 [20250519_004409.]: # CpG-site: CpG#5 Cubic: Using a = -4.48459708193036e-06 , b = 0.00375371236171235 , c = 0.422446384479718 , d = 5.17203872053872 [20250519_004411.]: Creating plot No. 6 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG6.png [20250519_004411.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = -1550.59307603208 , a = -125.016146072952 , b = -1550.59307603208 , d = -247.279679418643 [20250519_004411.]: # CpG-site: CpG#6 Cubic: Using a = 2.38852884399552e-05 , b = 0.000918637037037021 , c = 0.561022132435467 , d = 6.1479276094276 [20250519_004412.]: Creating plot No. 7 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG7.png [20250519_004412.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -772.844192345493 , a = -75.2724875322984 , b = -772.844192345493 , d = -226.673147125363 [20250519_004412.]: # CpG-site: CpG#7 Cubic: Using a = 4.98010505050505e-05 , b = -0.00436152150072151 , c = 0.579588917748918 , d = 1.72104545454544 [20250519_004414.]: Creating plot No. 8 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG8.png [20250519_004414.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = -3872.73616547768 , a = -278.017275815885 , b = -3872.73616547768 , d = -438.894268920994 [20250519_004414.]: # CpG-site: CpG#8 Cubic: Using a = -1.50060965207632e-05 , b = 0.00447978143338143 , c = 0.534897737694404 , d = 9.56981481481482 [20250519_004415.]: Creating plot No. 9 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG9.png [20250519_004415.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -843.925451494751 , a = -70.6796095478879 , b = -843.925451494751 , d = -184.482153986057 [20250519_004415.]: # CpG-site: CpG#9 Cubic: Using a = 7.72300426487093e-05 , b = -0.00542281173641174 , c = 0.67899229597563 , d = 2.72413468013467 [20250519_004417.]: Creating plot No. 10 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_rowmeans.png [20250519_004417.]: # CpG-site: row_means Hyperbolic: Using bias_weight = -1525.75758254501 , a = -127.67501720702 , b = -1525.75758254501 , d = -263.003030149428 [20250519_004417.]: # CpG-site: row_means Cubic: Using a = 2.88923726150392e-05 , b = -0.0006299592752926 , c = 0.600117857944524 , d = 5.17789562289563 [20250519_004418.]: Entered 'solving_equations'-Function [20250519_004418.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: -2.23222990163966 [20250519_004418.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.232 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.1698489850618 [20250519_004418.]: Samplename: 12.5 Root: 12.17 --> Root in between the borders! Added to results. Hyperbolic solved: 24.4781920312644 [20250519_004418.]: Samplename: 25 Root: 24.478 --> Root in between the borders! Added to results. Hyperbolic solved: 38.173044740918 [20250519_004418.]: Samplename: 37.5 Root: 38.173 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3349371964438 [20250519_004418.]: Samplename: 50 Root: 52.335 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4582773627666 [20250519_004418.]: Samplename: 62.5 Root: 65.458 --> Root in between the borders! Added to results. Hyperbolic solved: 75.0090795260796 [20250519_004418.]: Samplename: 75 Root: 75.009 --> Root in between the borders! Added to results. Hyperbolic solved: 81.5271920968417 [20250519_004418.]: Samplename: 87.5 Root: 81.527 --> Root in between the borders! Added to results. Hyperbolic solved: 102.400893095062 [20250519_004418.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.401 --> '100 < root < 110' --> substitute 100 [20250519_004418.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 1.13660501904968 [20250519_004418.]: Samplename: 0 Root: 1.137 --> Root in between the borders! Added to results. Hyperbolic solved: 11.4129696733689 [20250519_004418.]: Samplename: 12.5 Root: 11.413 --> Root in between the borders! Added to results. Hyperbolic solved: 26.174000526428 [20250519_004418.]: Samplename: 25 Root: 26.174 --> Root in between the borders! Added to results. Hyperbolic solved: 35.1050449117028 [20250519_004418.]: Samplename: 37.5 Root: 35.105 --> Root in between the borders! Added to results. Hyperbolic solved: 47.685500330611 [20250519_004418.]: Samplename: 50 Root: 47.686 --> Root in between the borders! Added to results. Hyperbolic solved: 67.1440494417104 [20250519_004418.]: Samplename: 62.5 Root: 67.144 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7644668894086 [20250519_004418.]: Samplename: 75 Root: 75.764 --> Root in between the borders! Added to results. Hyperbolic solved: 84.4054158616395 [20250519_004418.]: Samplename: 87.5 Root: 84.405 --> Root in between the borders! Added to results. Hyperbolic solved: 100.94827248399 [20250519_004418.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.948 --> '100 < root < 110' --> substitute 100 [20250519_004418.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 0.51235653688495 [20250519_004418.]: Samplename: 0 Root: 0.512 --> Root in between the borders! Added to results. Hyperbolic solved: 10.7523884294604 [20250519_004418.]: Samplename: 12.5 Root: 10.752 --> Root in between the borders! Added to results. Hyperbolic solved: 25.5218907947761 [20250519_004418.]: Samplename: 25 Root: 25.522 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5270462675211 [20250519_004418.]: Samplename: 37.5 Root: 36.527 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7909245028224 [20250519_004418.]: Samplename: 50 Root: 50.791 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8686317550184 [20250519_004418.]: Samplename: 62.5 Root: 64.869 --> Root in between the borders! Added to results. Hyperbolic solved: 77.5524188495235 [20250519_004418.]: Samplename: 75 Root: 77.552 --> Root in between the borders! Added to results. Hyperbolic solved: 80.4374617358174 [20250519_004418.]: Samplename: 87.5 Root: 80.437 --> Root in between the borders! Added to results. Hyperbolic solved: 102.704024900825 [20250519_004418.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.704 --> '100 < root < 110' --> substitute 100 [20250519_004418.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: -0.519503092357606 [20250519_004418.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.52 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.4934147844872 [20250519_004418.]: Samplename: 12.5 Root: 12.493 --> Root in between the borders! Added to results. Hyperbolic solved: 24.2685420024115 [20250519_004418.]: Samplename: 25 Root: 24.269 --> Root in between the borders! Added to results. Hyperbolic solved: 38.0817128465023 [20250519_004418.]: Samplename: 37.5 Root: 38.082 --> Root in between the borders! Added to results. Hyperbolic solved: 48.5843181174811 [20250519_004418.]: Samplename: 50 Root: 48.584 --> Root in between the borders! Added to results. Hyperbolic solved: 67.6722399183037 [20250519_004418.]: Samplename: 62.5 Root: 67.672 --> Root in between the borders! Added to results. Hyperbolic solved: 74.1549277799119 [20250519_004418.]: Samplename: 75 Root: 74.155 --> Root in between the borders! Added to results. Hyperbolic solved: 82.8821797890026 [20250519_004418.]: Samplename: 87.5 Root: 82.882 --> Root in between the borders! Added to results. Hyperbolic solved: 102.0791269023 [20250519_004418.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.079 --> '100 < root < 110' --> substitute 100 [20250519_004418.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 2.41558626275183 [20250519_004418.]: Samplename: 0 Root: 2.416 --> Root in between the borders! Added to results. Hyperbolic solved: 10.1649674907454 [20250519_004418.]: Samplename: 12.5 Root: 10.165 --> Root in between the borders! Added to results. Hyperbolic solved: 23.9830820412762 [20250519_004418.]: Samplename: 25 Root: 23.983 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2773619900429 [20250519_004418.]: Samplename: 37.5 Root: 37.277 --> Root in between the borders! Added to results. Hyperbolic solved: 50.8659386543864 [20250519_004418.]: Samplename: 50 Root: 50.866 --> Root in between the borders! Added to results. Hyperbolic solved: 62.4342273571069 [20250519_004418.]: Samplename: 62.5 Root: 62.434 --> Root in between the borders! Added to results. Hyperbolic solved: 76.3915260534323 [20250519_004418.]: Samplename: 75 Root: 76.392 --> Root in between the borders! Added to results. Hyperbolic solved: 86.159788778566 [20250519_004418.]: Samplename: 87.5 Root: 86.16 --> Root in between the borders! Added to results. Hyperbolic solved: 100.267759893323 [20250519_004418.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.268 --> '100 < root < 110' --> substitute 100 [20250519_004418.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 0.138163748613034 [20250519_004419.]: Samplename: 0 Root: 0.138 --> Root in between the borders! Added to results. Hyperbolic solved: 11.8635558881981 [20250519_004419.]: Samplename: 12.5 Root: 11.864 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5107449550797 [20250519_004419.]: Samplename: 25 Root: 26.511 --> Root in between the borders! Added to results. Hyperbolic solved: 35.3205073050661 [20250519_004419.]: Samplename: 37.5 Root: 35.321 --> Root in between the borders! Added to results. Hyperbolic solved: 50.0570767570666 [20250519_004419.]: Samplename: 50 Root: 50.057 --> Root in between the borders! Added to results. Hyperbolic solved: 64.9602944381018 [20250519_004419.]: Samplename: 62.5 Root: 64.96 --> Root in between the borders! Added to results. Hyperbolic solved: 73.66890571617 [20250519_004419.]: Samplename: 75 Root: 73.669 --> Root in between the borders! Added to results. Hyperbolic solved: 87.1266086585036 [20250519_004419.]: Samplename: 87.5 Root: 87.127 --> Root in between the borders! Added to results. Hyperbolic solved: 100.261637014212 [20250519_004419.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.262 --> '100 < root < 110' --> substitute 100 [20250519_004419.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: -1.37238087287012 [20250519_004419.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -1.372 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.1993162352498 [20250519_004419.]: Samplename: 12.5 Root: 10.199 --> Root in between the borders! Added to results. Hyperbolic solved: 24.595178967123 [20250519_004419.]: Samplename: 25 Root: 24.595 --> Root in between the borders! Added to results. Hyperbolic solved: 37.8310421041787 [20250519_004419.]: Samplename: 37.5 Root: 37.831 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5588739724067 [20250519_004419.]: Samplename: 50 Root: 53.559 --> Root in between the borders! Added to results. Hyperbolic solved: 65.9364947980258 [20250519_004419.]: Samplename: 62.5 Root: 65.936 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7361094434913 [20250519_004419.]: Samplename: 75 Root: 75.736 --> Root in between the borders! Added to results. Hyperbolic solved: 79.432823759854 [20250519_004419.]: Samplename: 87.5 Root: 79.433 --> Root in between the borders! Added to results. Hyperbolic solved: 103.004237013737 [20250519_004419.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 103.004 --> '100 < root < 110' --> substitute 100 [20250519_004419.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 2.80068218205093 [20250519_004419.]: Samplename: 0 Root: 2.801 --> Root in between the borders! Added to results. Hyperbolic solved: 9.27535134596596 [20250519_004419.]: Samplename: 12.5 Root: 9.275 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4762621928197 [20250519_004419.]: Samplename: 25 Root: 25.476 --> Root in between the borders! Added to results. Hyperbolic solved: 34.0122075735416 [20250519_004419.]: Samplename: 37.5 Root: 34.012 --> Root in between the borders! Added to results. Hyperbolic solved: 51.7842655662325 [20250519_004419.]: Samplename: 50 Root: 51.784 --> Root in between the borders! Added to results. Hyperbolic solved: 64.6732311906145 [20250519_004419.]: Samplename: 62.5 Root: 64.673 --> Root in between the borders! Added to results. Hyperbolic solved: 78.4326978859189 [20250519_004419.]: Samplename: 75 Root: 78.433 --> Root in between the borders! Added to results. Hyperbolic solved: 81.3427232852719 [20250519_004419.]: Samplename: 87.5 Root: 81.343 --> Root in between the borders! Added to results. Hyperbolic solved: 101.964406640583 [20250519_004419.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.964 --> '100 < root < 110' --> substitute 100 [20250519_004419.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: -2.13403721845678 [20250519_004419.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.134 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.5082192457956 [20250519_004419.]: Samplename: 12.5 Root: 10.508 --> Root in between the borders! Added to results. Hyperbolic solved: 26.9164567253388 [20250519_004419.]: Samplename: 25 Root: 26.916 --> Root in between the borders! Added to results. Hyperbolic solved: 36.8334779159501 [20250519_004419.]: Samplename: 37.5 Root: 36.833 --> Root in between the borders! Added to results. Hyperbolic solved: 52.0097895977263 [20250519_004419.]: Samplename: 50 Root: 52.01 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8930527921581 [20250519_004419.]: Samplename: 62.5 Root: 64.893 --> Root in between the borders! Added to results. Hyperbolic solved: 74.5671055499357 [20250519_004419.]: Samplename: 75 Root: 74.567 --> Root in between the borders! Added to results. Hyperbolic solved: 84.5294954832669 [20250519_004419.]: Samplename: 87.5 Root: 84.529 --> Root in between the borders! Added to results. Hyperbolic solved: 101.047146466811 [20250519_004419.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.047 --> '100 < root < 110' --> substitute 100 [20250519_004419.]: Solving hyperbolic regression for row_means Hyperbolic solved: 0.290940467580009 [20250519_004419.]: Samplename: 0 Root: 0.291 --> Root in between the borders! Added to results. Hyperbolic solved: 11.041240609297 [20250519_004419.]: Samplename: 12.5 Root: 11.041 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4081503210647 [20250519_004419.]: Samplename: 25 Root: 25.408 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5243723188613 [20250519_004419.]: Samplename: 37.5 Root: 36.524 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7348829528732 [20250519_004419.]: Samplename: 50 Root: 50.735 --> Root in between the borders! Added to results. Hyperbolic solved: 65.3135214250794 [20250519_004419.]: Samplename: 62.5 Root: 65.314 --> Root in between the borders! Added to results. Hyperbolic solved: 75.5342711952113 [20250519_004419.]: Samplename: 75 Root: 75.534 --> Root in between the borders! Added to results. Hyperbolic solved: 83.2411229564887 [20250519_004419.]: Samplename: 87.5 Root: 83.241 --> Root in between the borders! Added to results. Hyperbolic solved: 101.666942268285 [20250519_004419.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.667 --> '100 < root < 110' --> substitute 100 [20250519_004419.]: ### Starting with regression calculations ### [20250519_004419.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_004419.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.1698489850618, 24.4781920312644, 38.173044740918, 52.3349371964438, 65.4582773627666, 75.0090795260796, 81.5271920968417, 100)c(0, 0.330151014938201, 0.521807968735601, 0.673044740918002, 2.3349371964438, 2.95827736276659, 0.00907952607960283, 5.9728079031583, 0)c(NA, 2.64120811950561, 2.0872318749424, 1.79478597578134, 4.66987439288761, 4.73324378042655, 0.0121060347728038, 6.82606617503806, 0) [20250519_004419.]: Logging df_agg: CpG#1 [20250519_004419.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004419.]: c(0, 12.1698489850618, 24.4781920312644, 38.173044740918, 52.3349371964438, 65.4582773627666, 75.0090795260796, 81.5271920968417, 100)[20250519_004419.]: c(0, 0.330151014938201, 0.521807968735601, 0.673044740918002, 2.3349371964438, 2.95827736276659, 0.00907952607960283, 5.9728079031583, 0)[20250519_004419.]: c(NA, 2.64120811950561, 2.0872318749424, 1.79478597578134, 4.66987439288761, 4.73324378042655, 0.0121060347728038, 6.82606617503806, 0) [20250519_004419.]: Entered 'hyperbolic_regression'-Function [20250519_004419.]: 'hyperbolic_regression': minmax = FALSE [20250519_004419.]: Entered 'cubic_regression'-Function [20250519_004419.]: 'cubic_regression': minmax = FALSE [20250519_004419.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.13660501904968, 11.4129696733689, 26.174000526428, 35.1050449117028, 47.685500330611, 67.1440494417104, 75.7644668894086, 84.4054158616395, 100)c(1.13660501904968, 1.0870303266311, 1.174000526428, 2.3949550882972, 2.314499669389, 4.64404944171039, 0.764466889408595, 3.0945841383605, 0)c(NA, 8.6962426130488, 4.696002105712, 6.38654690212587, 4.628999338778, 7.43047910673663, 1.01928918587813, 3.53666758669772, 0) [20250519_004419.]: Logging df_agg: CpG#2 [20250519_004419.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004419.]: c(1.13660501904968, 11.4129696733689, 26.174000526428, 35.1050449117028, 47.685500330611, 67.1440494417104, 75.7644668894086, 84.4054158616395, 100)[20250519_004419.]: c(1.13660501904968, 1.0870303266311, 1.174000526428, 2.3949550882972, 2.314499669389, 4.64404944171039, 0.764466889408595, 3.0945841383605, 0)[20250519_004419.]: c(NA, 8.6962426130488, 4.696002105712, 6.38654690212587, 4.628999338778, 7.43047910673663, 1.01928918587813, 3.53666758669772, 0) [20250519_004419.]: Entered 'hyperbolic_regression'-Function [20250519_004419.]: 'hyperbolic_regression': minmax = FALSE [20250519_004419.]: Entered 'cubic_regression'-Function [20250519_004419.]: 'cubic_regression': minmax = FALSE [20250519_004419.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.51235653688495, 10.7523884294604, 25.5218907947761, 36.5270462675211, 50.7909245028224, 64.8686317550184, 77.5524188495235, 80.4374617358174, 100)c(0.51235653688495, 1.7476115705396, 0.521890794776098, 0.972953732478899, 0.790924502822399, 2.3686317550184, 2.55241884952351, 7.06253826418261, 0)c(NA, 13.9808925643168, 2.08756317910439, 2.5945432866104, 1.5818490056448, 3.78981080802944, 3.40322513269801, 8.07147230192298, 0) [20250519_004419.]: Logging df_agg: CpG#3 [20250519_004419.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004419.]: c(0.51235653688495, 10.7523884294604, 25.5218907947761, 36.5270462675211, 50.7909245028224, 64.8686317550184, 77.5524188495235, 80.4374617358174, 100)[20250519_004419.]: c(0.51235653688495, 1.7476115705396, 0.521890794776098, 0.972953732478899, 0.790924502822399, 2.3686317550184, 2.55241884952351, 7.06253826418261, 0)[20250519_004419.]: c(NA, 13.9808925643168, 2.08756317910439, 2.5945432866104, 1.5818490056448, 3.78981080802944, 3.40322513269801, 8.07147230192298, 0) [20250519_004419.]: Entered 'hyperbolic_regression'-Function [20250519_004419.]: 'hyperbolic_regression': minmax = FALSE [20250519_004420.]: Entered 'cubic_regression'-Function [20250519_004420.]: 'cubic_regression': minmax = FALSE [20250519_004420.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.4934147844872, 24.2685420024115, 38.0817128465023, 48.5843181174811, 67.6722399183037, 74.1549277799119, 82.8821797890026, 100)c(0, 0.00658521551279989, 0.7314579975885, 0.581712846502299, 1.4156818825189, 5.1722399183037, 0.845072220088099, 4.61782021099739, 0)c(NA, 0.0526817241023991, 2.925831990354, 1.55123425733946, 2.83136376503781, 8.27558386928592, 1.12676296011747, 5.27750881256845, 0) [20250519_004420.]: Logging df_agg: CpG#4 [20250519_004420.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004420.]: c(0, 12.4934147844872, 24.2685420024115, 38.0817128465023, 48.5843181174811, 67.6722399183037, 74.1549277799119, 82.8821797890026, 100)[20250519_004420.]: c(0, 0.00658521551279989, 0.7314579975885, 0.581712846502299, 1.4156818825189, 5.1722399183037, 0.845072220088099, 4.61782021099739, 0)[20250519_004420.]: c(NA, 0.0526817241023991, 2.925831990354, 1.55123425733946, 2.83136376503781, 8.27558386928592, 1.12676296011747, 5.27750881256845, 0) [20250519_004420.]: Entered 'hyperbolic_regression'-Function [20250519_004420.]: 'hyperbolic_regression': minmax = FALSE [20250519_004420.]: Entered 'cubic_regression'-Function [20250519_004420.]: 'cubic_regression': minmax = FALSE [20250519_004420.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.41558626275183, 10.1649674907454, 23.9830820412762, 37.2773619900429, 50.8659386543864, 62.4342273571069, 76.3915260534323, 86.159788778566, 100)c(2.41558626275183, 2.3350325092546, 1.0169179587238, 0.222638009957102, 0.865938654386397, 0.0657726428931014, 1.3915260534323, 1.340211221434, 0)c(NA, 18.6802600740368, 4.06767183489519, 0.593701359885604, 1.73187730877279, 0.105236228628962, 1.85536807124307, 1.53166996735315, 0) [20250519_004420.]: Logging df_agg: CpG#5 [20250519_004420.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004420.]: c(2.41558626275183, 10.1649674907454, 23.9830820412762, 37.2773619900429, 50.8659386543864, 62.4342273571069, 76.3915260534323, 86.159788778566, 100)[20250519_004420.]: c(2.41558626275183, 2.3350325092546, 1.0169179587238, 0.222638009957102, 0.865938654386397, 0.0657726428931014, 1.3915260534323, 1.340211221434, 0)[20250519_004420.]: c(NA, 18.6802600740368, 4.06767183489519, 0.593701359885604, 1.73187730877279, 0.105236228628962, 1.85536807124307, 1.53166996735315, 0) [20250519_004420.]: Entered 'hyperbolic_regression'-Function [20250519_004420.]: 'hyperbolic_regression': minmax = FALSE [20250519_004420.]: Entered 'cubic_regression'-Function [20250519_004420.]: 'cubic_regression': minmax = FALSE [20250519_004420.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.138163748613034, 11.8635558881981, 26.5107449550797, 35.3205073050661, 50.0570767570666, 64.9602944381018, 73.66890571617, 87.1266086585036, 100)c(0.138163748613034, 0.6364441118019, 1.5107449550797, 2.1794926949339, 0.0570767570666035, 2.4602944381018, 1.33109428383, 0.373391341496401, 0)c(NA, 5.0915528944152, 6.04297982031881, 5.81198051982373, 0.114153514133207, 3.93647110096288, 1.77479237844, 0.426732961710172, 0) [20250519_004420.]: Logging df_agg: CpG#6 [20250519_004420.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004420.]: c(0.138163748613034, 11.8635558881981, 26.5107449550797, 35.3205073050661, 50.0570767570666, 64.9602944381018, 73.66890571617, 87.1266086585036, 100)[20250519_004420.]: c(0.138163748613034, 0.6364441118019, 1.5107449550797, 2.1794926949339, 0.0570767570666035, 2.4602944381018, 1.33109428383, 0.373391341496401, 0)[20250519_004420.]: c(NA, 5.0915528944152, 6.04297982031881, 5.81198051982373, 0.114153514133207, 3.93647110096288, 1.77479237844, 0.426732961710172, 0) [20250519_004420.]: Entered 'hyperbolic_regression'-Function [20250519_004420.]: 'hyperbolic_regression': minmax = FALSE [20250519_004420.]: Entered 'cubic_regression'-Function [20250519_004420.]: 'cubic_regression': minmax = FALSE [20250519_004420.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.1993162352498, 24.595178967123, 37.8310421041787, 53.5588739724067, 65.9364947980258, 75.7361094434913, 79.432823759854, 100)c(0, 2.3006837647502, 0.404821032876999, 0.3310421041787, 3.5588739724067, 3.43649479802581, 0.736109443491301, 8.067176240146, 0)c(NA, 18.4054701180016, 1.619284131508, 0.882778944476532, 7.1177479448134, 5.49839167684129, 0.981479257988402, 9.21962998873828, 0) [20250519_004420.]: Logging df_agg: CpG#7 [20250519_004420.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004420.]: c(0, 10.1993162352498, 24.595178967123, 37.8310421041787, 53.5588739724067, 65.9364947980258, 75.7361094434913, 79.432823759854, 100)[20250519_004420.]: c(0, 2.3006837647502, 0.404821032876999, 0.3310421041787, 3.5588739724067, 3.43649479802581, 0.736109443491301, 8.067176240146, 0)[20250519_004420.]: c(NA, 18.4054701180016, 1.619284131508, 0.882778944476532, 7.1177479448134, 5.49839167684129, 0.981479257988402, 9.21962998873828, 0) [20250519_004420.]: Entered 'hyperbolic_regression'-Function [20250519_004420.]: 'hyperbolic_regression': minmax = FALSE [20250519_004421.]: Entered 'cubic_regression'-Function [20250519_004421.]: 'cubic_regression': minmax = FALSE [20250519_004421.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.80068218205093, 9.27535134596596, 25.4762621928197, 34.0122075735416, 51.7842655662325, 64.6732311906145, 78.4326978859189, 81.3427232852719, 100)c(2.80068218205093, 3.22464865403404, 0.4762621928197, 3.4877924264584, 1.7842655662325, 2.1732311906145, 3.4326978859189, 6.1572767147281, 0)c(NA, 25.7971892322723, 1.9050487712788, 9.30077980388906, 3.568531132465, 3.4771699049832, 4.57693051455853, 7.03688767397497, 0) [20250519_004421.]: Logging df_agg: CpG#8 [20250519_004421.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004421.]: c(2.80068218205093, 9.27535134596596, 25.4762621928197, 34.0122075735416, 51.7842655662325, 64.6732311906145, 78.4326978859189, 81.3427232852719, 100)[20250519_004421.]: c(2.80068218205093, 3.22464865403404, 0.4762621928197, 3.4877924264584, 1.7842655662325, 2.1732311906145, 3.4326978859189, 6.1572767147281, 0)[20250519_004421.]: c(NA, 25.7971892322723, 1.9050487712788, 9.30077980388906, 3.568531132465, 3.4771699049832, 4.57693051455853, 7.03688767397497, 0) [20250519_004421.]: Entered 'hyperbolic_regression'-Function [20250519_004421.]: 'hyperbolic_regression': minmax = FALSE [20250519_004421.]: Entered 'cubic_regression'-Function [20250519_004421.]: 'cubic_regression': minmax = FALSE [20250519_004421.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.5082192457956, 26.9164567253388, 36.8334779159501, 52.0097895977263, 64.8930527921581, 74.5671055499357, 84.5294954832669, 100)c(0, 1.9917807542044, 1.9164567253388, 0.666522084049902, 2.0097895977263, 2.3930527921581, 0.432894450064296, 2.9705045167331, 0)c(NA, 15.9342460336352, 7.6658269013552, 1.77739222413307, 4.01957919545259, 3.82888446745296, 0.577192600085728, 3.39486230483783, 0) [20250519_004421.]: Logging df_agg: CpG#9 [20250519_004421.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004421.]: c(0, 10.5082192457956, 26.9164567253388, 36.8334779159501, 52.0097895977263, 64.8930527921581, 74.5671055499357, 84.5294954832669, 100)[20250519_004421.]: c(0, 1.9917807542044, 1.9164567253388, 0.666522084049902, 2.0097895977263, 2.3930527921581, 0.432894450064296, 2.9705045167331, 0)[20250519_004421.]: c(NA, 15.9342460336352, 7.6658269013552, 1.77739222413307, 4.01957919545259, 3.82888446745296, 0.577192600085728, 3.39486230483783, 0) [20250519_004421.]: Entered 'hyperbolic_regression'-Function [20250519_004421.]: 'hyperbolic_regression': minmax = FALSE [20250519_004421.]: Entered 'cubic_regression'-Function [20250519_004421.]: 'cubic_regression': minmax = FALSE [20250519_004421.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.290940467580009, 11.041240609297, 25.4081503210647, 36.5243723188613, 50.7348829528732, 65.3135214250794, 75.5342711952113, 83.2411229564887, 100)c(0.290940467580009, 1.458759390703, 0.408150321064699, 0.975627681138697, 0.7348829528732, 2.8135214250794, 0.534271195211304, 4.2588770435113, 0)c(NA, 11.670075125624, 1.6326012842588, 2.60167381636986, 1.4697659057464, 4.50163428012704, 0.712361593615071, 4.8672880497272, 0) [20250519_004421.]: Logging df_agg: row_means [20250519_004421.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004421.]: c(0.290940467580009, 11.041240609297, 25.4081503210647, 36.5243723188613, 50.7348829528732, 65.3135214250794, 75.5342711952113, 83.2411229564887, 100)[20250519_004421.]: c(0.290940467580009, 1.458759390703, 0.408150321064699, 0.975627681138697, 0.7348829528732, 2.8135214250794, 0.534271195211304, 4.2588770435113, 0)[20250519_004421.]: c(NA, 11.670075125624, 1.6326012842588, 2.60167381636986, 1.4697659057464, 4.50163428012704, 0.712361593615071, 4.8672880497272, 0) [20250519_004421.]: Entered 'hyperbolic_regression'-Function [20250519_004421.]: 'hyperbolic_regression': minmax = FALSE [20250519_004421.]: Entered 'cubic_regression'-Function [20250519_004421.]: 'cubic_regression': minmax = FALSE [20250519_004422.]: ### Starting with plotting ### [20250519_004422.]: Creating BiasCorrected (hyperbolic) plot No. 1 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG1_corrected_h.png [20250519_004422.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -657.853491622191 , a = 985.267531706584 , b = -657.853491622191 , d = 903.45414536045 [20250519_004422.]: # CpG-site: CpG#1 Cubic: Using a = 5.18463232357963e-06 , b = -0.00180592068110451 , c = 1.11404020909943 , d = -0.858600112691325 [20250519_004424.]: Creating BiasCorrected (hyperbolic) plot No. 2 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG2_corrected_h.png [20250519_004424.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = 3399.55230393578 , a = 30276.0421015763 , b = 3399.55230393578 , d = 30352.4984502595 [20250519_004424.]: # CpG-site: CpG#2 Cubic: Using a = -2.58097549676629e-05 , b = 0.00383753278164476 , c = 0.8516077608074 , d = 0.957127237506517 [20250519_004425.]: Creating BiasCorrected (hyperbolic) plot No. 3 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG3_corrected_h.png [20250519_004425.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = -945.757057025882 , a = 1333.61975246108 , b = -945.757057025882 , d = 1252.95856205348 [20250519_004425.]: # CpG-site: CpG#3 Cubic: Using a = -1.13948611491254e-05 , b = 0.000937180276062016 , c = 0.998630620080636 , d = -0.383337686690576 [20250519_004427.]: Creating BiasCorrected (hyperbolic) plot No. 4 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG4_corrected_h.png [20250519_004427.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = -754.050736416394 , a = 1637.26786961468 , b = -754.050736416394 , d = 1558.27361713715 [20250519_004427.]: # CpG-site: CpG#4 Cubic: Using a = -3.25779988054422e-06 , b = -0.000129596030700936 , c = 1.03077246222439 , d = -0.370343704072989 [20250519_004428.]: Creating BiasCorrected (hyperbolic) plot No. 5 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG5_corrected_h.png [20250519_004428.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = -1917.44420974597 , a = -5621.4534701009 , b = -1917.44420974597 , d = -5734.47300613195 [20250519_004428.]: # CpG-site: CpG#5 Cubic: Using a = -3.16782834876228e-05 , b = 0.00493527189107616 , c = 0.800194601834967 , d = 1.38653015028503 [20250519_004430.]: Creating BiasCorrected (hyperbolic) plot No. 6 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG6_corrected_h.png [20250519_004430.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = -1255.67712347228 , a = 13775.5934089325 , b = -1255.67712347228 , d = 13690.4825730065 [20250519_004430.]: # CpG-site: CpG#6 Cubic: Using a = -3.70201684653088e-06 , b = 0.000482210694203131 , c = 0.985293133118367 , d = 0.0293568698433451 [20250519_004431.]: Creating BiasCorrected (hyperbolic) plot No. 7 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG7_corrected_h.png [20250519_004431.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -1065.05415478934 , a = 722.083975601617 , b = -1065.05415478934 , d = 634.402060442185 [20250519_004431.]: # CpG-site: CpG#7 Cubic: Using a = -1.04111972402046e-06 , b = -0.00130150084904922 , c = 1.12399516228677 , d = -1.5985899870939 [20250519_004433.]: Creating BiasCorrected (hyperbolic) plot No. 8 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG8_corrected_h.png [20250519_004433.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = 68.5784871630731 , a = 4784.90969361316 , b = 68.5784871630731 , d = 4739.12296431021 [20250519_004433.]: # CpG-site: CpG#8 Cubic: Using a = -4.65730502627556e-05 , b = 0.00676525570440644 , c = 0.747405744075959 , d = 1.52337158754213 [20250519_004434.]: Creating BiasCorrected (hyperbolic) plot No. 9 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG9_corrected_h.png [20250519_004434.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -1022.70169872734 , a = 1273.73039431947 , b = -1022.70169872734 , d = 1181.86496720377 [20250519_004434.]: # CpG-site: CpG#9 Cubic: Using a = 4.24790097543708e-06 , b = -0.00144169637811555 , c = 1.09837791472274 , d = -0.97898795630014 [20250519_004436.]: Creating BiasCorrected (hyperbolic) plot No. 10 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_rowmeans_corrected_h.png [20250519_004436.]: # CpG-site: row_means Hyperbolic: Using bias_weight = -1166.73858297355 , a = 1740.63784318208 , b = -1166.73858297355 , d = 1653.86071043116 [20250519_004436.]: # CpG-site: row_means Cubic: Using a = -8.71186803343537e-06 , b = 0.000715553560638339 , c = 1.00248252890793 , d = -0.420554394203132 [20250519_004437.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG1_corrected_h.png [20250519_004439.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG2_corrected_h.png [20250519_004440.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG3_corrected_h.png [20250519_004441.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG4_corrected_h.png [20250519_004443.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG5_corrected_h.png [20250519_004444.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG6_corrected_h.png [20250519_004445.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG7_corrected_h.png [20250519_004446.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG8_corrected_h.png [20250519_004448.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG9_corrected_h.png [20250519_004449.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_rowmeans_corrected_h.png [20250519_004450.]: Entered 'solving_equations'-Function [20250519_004450.]: Solving cubic regression for CpG#1 Coefficients: -1.03617340067344Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004450.]: Samplename: 0 Root: 1.334 --> Root in between the borders! Added to results. Coefficients: -8.34150673400678Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004450.]: Samplename: 12.5 Root: 11.446 --> Root in between the borders! Added to results. Coefficients: -15.3881734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004450.]: Samplename: 25 Root: 22.228 --> Root in between the borders! Added to results. Coefficients: -24.2801734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004450.]: Samplename: 37.5 Root: 36.374 --> Root in between the borders! Added to results. Coefficients: -34.9006734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004450.]: Samplename: 50 Root: 52.044 --> Root in between the borders! Added to results. Coefficients: -46.3541734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004450.]: Samplename: 62.5 Root: 66.144 --> Root in between the borders! Added to results. Coefficients: -55.8931734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004450.]: Samplename: 75 Root: 75.864 --> Root in between the borders! Added to results. Coefficients: -63.0981734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004450.]: Samplename: 87.5 Root: 82.254 --> Root in between the borders! Added to results. Coefficients: -91.0461734006735Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004450.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.877 --> '100 < root < 110' --> substitute 100 [20250519_004450.]: Solving cubic regression for CpG#2 Coefficients: -0.283329966329966Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004450.]: Samplename: 0 Root: 0.549 --> Root in between the borders! Added to results. Coefficients: -6.33999663299663Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004450.]: Samplename: 12.5 Root: 11.533 --> Root in between the borders! Added to results. Coefficients: -15.93932996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004450.]: Samplename: 25 Root: 26.628 --> Root in between the borders! Added to results. Coefficients: -22.33732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004450.]: Samplename: 37.5 Root: 35.509 --> Root in between the borders! Added to results. Coefficients: -32.22832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004450.]: Samplename: 50 Root: 47.851 --> Root in between the borders! Added to results. Coefficients: -49.96332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004450.]: Samplename: 62.5 Root: 66.893 --> Root in between the borders! Added to results. Coefficients: -58.96582996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004450.]: Samplename: 75 Root: 75.431 --> Root in between the borders! Added to results. Coefficients: -68.8366632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004450.]: Samplename: 87.5 Root: 84.118 --> Root in between the borders! Added to results. Coefficients: -90.57732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004450.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.287 --> '100 < root < 110' --> substitute 100 [20250519_004451.]: Solving cubic regression for CpG#3 Coefficients: -0.90294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004451.]: Samplename: 0 Root: 1.441 --> Root in between the borders! Added to results. Coefficients: -6.57294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004451.]: Samplename: 12.5 Root: 10.568 --> Root in between the borders! Added to results. Coefficients: -15.4289478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004451.]: Samplename: 25 Root: 24.796 --> Root in between the borders! Added to results. Coefficients: -22.6129478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004451.]: Samplename: 37.5 Root: 35.952 --> Root in between the borders! Added to results. Coefficients: -32.7754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004451.]: Samplename: 50 Root: 50.684 --> Root in between the borders! Added to results. Coefficients: -43.8889478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004451.]: Samplename: 62.5 Root: 65.142 --> Root in between the borders! Added to results. Coefficients: -54.9754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004451.]: Samplename: 75 Root: 77.905 --> Root in between the borders! Added to results. Coefficients: -57.6562811447812Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004451.]: Samplename: 87.5 Root: 80.767 --> Root in between the borders! Added to results. Coefficients: -80.6649478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004451.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.38 --> '100 < root < 110' --> substitute 100 [20250519_004451.]: Solving cubic regression for CpG#4 Coefficients: -0.597449494949524Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004451.]: Samplename: 0 Root: 0.858 --> Root in between the borders! Added to results. Coefficients: -8.25278282828286Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004451.]: Samplename: 12.5 Root: 12.086 --> Root in between the borders! Added to results. Coefficients: -15.8034494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004451.]: Samplename: 25 Root: 23.316 --> Root in between the borders! Added to results. Coefficients: -25.5274494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004451.]: Samplename: 37.5 Root: 37.383 --> Root in between the borders! Added to results. Coefficients: -33.6369494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004451.]: Samplename: 50 Root: 48.353 --> Root in between the borders! Added to results. Coefficients: -50.2554494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004451.]: Samplename: 62.5 Root: 68.082 --> Root in between the borders! Added to results. Coefficients: -56.5394494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004451.]: Samplename: 75 Root: 74.615 --> Root in between the borders! Added to results. Coefficients: -65.5927828282829Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004451.]: Samplename: 87.5 Root: 83.254 --> Root in between the borders! Added to results. Coefficients: -88.3214494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004451.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.715 --> '100 < root < 110' --> substitute 100 [20250519_004451.]: Solving cubic regression for CpG#5 Coefficients: -0.623961279461278Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004451.]: Samplename: 0 Root: 1.458 --> Root in between the borders! Added to results. Coefficients: -4.76796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004451.]: Samplename: 12.5 Root: 10.347 --> Root in between the borders! Added to results. Coefficients: -12.7259612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004451.]: Samplename: 25 Root: 24.815 --> Root in between the borders! Added to results. Coefficients: -21.1599612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004451.]: Samplename: 37.5 Root: 37.902 --> Root in between the borders! Added to results. Coefficients: -30.6954612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004451.]: Samplename: 50 Root: 50.977 --> Root in between the borders! Added to results. Coefficients: -39.6579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004451.]: Samplename: 62.5 Root: 62.126 --> Root in between the borders! Added to results. Coefficients: -51.6829612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004451.]: Samplename: 75 Root: 75.852 --> Root in between the borders! Added to results. Coefficients: -61.0146279461279Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004451.]: Samplename: 87.5 Root: 85.767 --> Root in between the borders! Added to results. Coefficients: -76.0699612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004451.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.743 --> '100 < root < 110' --> substitute 100 [20250519_004451.]: Solving cubic regression for CpG#6 Coefficients: -0.196072390572403Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004451.]: Samplename: 0 Root: 0.349 --> Root in between the borders! Added to results. Coefficients: -6.73873905723907Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004451.]: Samplename: 12.5 Root: 11.718 --> Root in between the borders! Added to results. Coefficients: -15.8880723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004451.]: Samplename: 25 Root: 26.396 --> Root in between the borders! Added to results. Coefficients: -22.0000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004451.]: Samplename: 37.5 Root: 35.301 --> Root in between the borders! Added to results. Coefficients: -33.4445723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004451.]: Samplename: 50 Root: 50.134 --> Root in between the borders! Added to results. Coefficients: -46.9000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004451.]: Samplename: 62.5 Root: 64.993 --> Root in between the borders! Added to results. Coefficients: -55.8320723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004451.]: Samplename: 75 Root: 73.639 --> Root in between the borders! Added to results. Coefficients: -71.5454057239057Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004451.]: Samplename: 87.5 Root: 87.043 --> Root in between the borders! Added to results. Coefficients: -89.6560723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004451.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.329 --> '100 < root < 110' --> substitute 100 [20250519_004451.]: Solving cubic regression for CpG#7 Coefficients: -1.21495454545456Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004451.]: Samplename: 0 Root: 2.13 --> Root in between the borders! Added to results. Coefficients: -5.39562121212123Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004451.]: Samplename: 12.5 Root: 9.973 --> Root in between the borders! Added to results. Coefficients: -11.2649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004451.]: Samplename: 25 Root: 22.206 --> Root in between the borders! Added to results. Coefficients: -17.4509545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004451.]: Samplename: 37.5 Root: 35.814 --> Root in between the borders! Added to results. Coefficients: -26.0314545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004451.]: Samplename: 50 Root: 53.28 --> Root in between the borders! Added to results. Coefficients: -33.9649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004451.]: Samplename: 62.5 Root: 66.598 --> Root in between the borders! Added to results. Coefficients: -41.1689545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004451.]: Samplename: 75 Root: 76.575 --> Root in between the borders! Added to results. Coefficients: -44.1356212121212Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004451.]: Samplename: 87.5 Root: 80.219 --> Root in between the borders! Added to results. Coefficients: -67.2229545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004451.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.506 --> '100 < root < 110' --> substitute 100 [20250519_004451.]: Solving cubic regression for CpG#8 Coefficients: -1.09618518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004451.]: Samplename: 0 Root: 2.016 --> Root in between the borders! Added to results. Coefficients: -5.44685185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004451.]: Samplename: 12.5 Root: 9.458 --> Root in between the borders! Added to results. Coefficients: -16.9301851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004451.]: Samplename: 25 Root: 26.35 --> Root in between the borders! Added to results. Coefficients: -23.3501851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004451.]: Samplename: 37.5 Root: 34.728 --> Root in between the borders! Added to results. Coefficients: -37.6251851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004451.]: Samplename: 50 Root: 51.781 --> Root in between the borders! Added to results. Coefficients: -48.8261851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004451.]: Samplename: 62.5 Root: 64.192 --> Root in between the borders! Added to results. Coefficients: -61.6676851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004451.]: Samplename: 75 Root: 77.804 --> Root in between the borders! Added to results. Coefficients: -64.5101851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004451.]: Samplename: 87.5 Root: 80.758 --> Root in between the borders! Added to results. Coefficients: -86.0601851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004451.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.834 --> '100 < root < 110' --> substitute 100 [20250519_004451.]: Solving cubic regression for CpG#9 Coefficients: -0.989865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004451.]: Samplename: 0 Root: 1.475 --> Root in between the borders! Added to results. Coefficients: -6.39586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004451.]: Samplename: 12.5 Root: 10.12 --> Root in between the borders! Added to results. Coefficients: -14.7058653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004451.]: Samplename: 25 Root: 24.844 --> Root in between the borders! Added to results. Coefficients: -20.6238653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004451.]: Samplename: 37.5 Root: 35.327 --> Root in between the borders! Added to results. Coefficients: -31.3958653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004451.]: Samplename: 50 Root: 51.855 --> Root in between the borders! Added to results. Coefficients: -42.6858653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004451.]: Samplename: 62.5 Root: 65.265 --> Root in between the borders! Added to results. Coefficients: -52.9033653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004451.]: Samplename: 75 Root: 74.915 --> Root in between the borders! Added to results. Coefficients: -65.492531986532Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004451.]: Samplename: 87.5 Root: 84.67 --> Root in between the borders! Added to results. Coefficients: -92.9898653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004451.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.082 --> '100 < root < 110' --> substitute 100 [20250519_004451.]: Solving cubic regression for row_means Coefficients: -0.771215488215478Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_004451.]: Samplename: 0 Root: 1.287 --> Root in between the borders! Added to results. Coefficients: -6.47247474747474Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_004451.]: Samplename: 12.5 Root: 10.847 --> Root in between the borders! Added to results. Coefficients: -14.8972154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_004451.]: Samplename: 25 Root: 24.737 --> Root in between the borders! Added to results. Coefficients: -22.1492154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_004451.]: Samplename: 37.5 Root: 36.02 --> Root in between the borders! Added to results. Coefficients: -32.5259932659933Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_004451.]: Samplename: 50 Root: 50.639 --> Root in between the borders! Added to results. Coefficients: -44.7218821548821Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_004451.]: Samplename: 62.5 Root: 65.497 --> Root in between the borders! Added to results. Coefficients: -54.4032154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_004451.]: Samplename: 75 Root: 75.751 --> Root in between the borders! Added to results. Coefficients: -62.4313636363636Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_004451.]: Samplename: 87.5 Root: 83.403 --> Root in between the borders! Added to results. Coefficients: -84.7343265993266Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_004451.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.573 --> '100 < root < 110' --> substitute 100 [20250519_004451.]: ### Starting with regression calculations ### [20250519_004451.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_004451.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) [20250519_004451.]: Logging df_agg: CpG#1 [20250519_004451.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004451.]: c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)[20250519_004451.]: c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)[20250519_004451.]: c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) [20250519_004451.]: Entered 'hyperbolic_regression'-Function [20250519_004451.]: 'hyperbolic_regression': minmax = FALSE [20250519_004451.]: Entered 'cubic_regression'-Function [20250519_004451.]: 'cubic_regression': minmax = FALSE [20250519_004451.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) [20250519_004451.]: Logging df_agg: CpG#2 [20250519_004451.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004451.]: c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)[20250519_004451.]: c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)[20250519_004451.]: c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) [20250519_004451.]: Entered 'hyperbolic_regression'-Function [20250519_004451.]: 'hyperbolic_regression': minmax = FALSE [20250519_004452.]: Entered 'cubic_regression'-Function [20250519_004452.]: 'cubic_regression': minmax = FALSE [20250519_004452.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) [20250519_004452.]: Logging df_agg: CpG#3 [20250519_004452.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004452.]: c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)[20250519_004452.]: c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)[20250519_004452.]: c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) [20250519_004452.]: Entered 'hyperbolic_regression'-Function [20250519_004452.]: 'hyperbolic_regression': minmax = FALSE [20250519_004452.]: Entered 'cubic_regression'-Function [20250519_004452.]: 'cubic_regression': minmax = FALSE [20250519_004452.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) [20250519_004452.]: Logging df_agg: CpG#4 [20250519_004452.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004452.]: c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)[20250519_004452.]: c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)[20250519_004452.]: c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) [20250519_004452.]: Entered 'hyperbolic_regression'-Function [20250519_004452.]: 'hyperbolic_regression': minmax = FALSE [20250519_004452.]: Entered 'cubic_regression'-Function [20250519_004452.]: 'cubic_regression': minmax = FALSE [20250519_004452.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) [20250519_004452.]: Logging df_agg: CpG#5 [20250519_004452.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004452.]: c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)[20250519_004452.]: c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)[20250519_004452.]: c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) [20250519_004452.]: Entered 'hyperbolic_regression'-Function [20250519_004452.]: 'hyperbolic_regression': minmax = FALSE [20250519_004452.]: Entered 'cubic_regression'-Function [20250519_004452.]: 'cubic_regression': minmax = FALSE [20250519_004452.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) [20250519_004452.]: Logging df_agg: CpG#6 [20250519_004452.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004452.]: c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)[20250519_004452.]: c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)[20250519_004452.]: c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) [20250519_004452.]: Entered 'hyperbolic_regression'-Function [20250519_004452.]: 'hyperbolic_regression': minmax = FALSE [20250519_004453.]: Entered 'cubic_regression'-Function [20250519_004453.]: 'cubic_regression': minmax = FALSE [20250519_004453.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) [20250519_004453.]: Logging df_agg: CpG#7 [20250519_004453.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004453.]: c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)[20250519_004453.]: c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)[20250519_004453.]: c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) [20250519_004453.]: Entered 'hyperbolic_regression'-Function [20250519_004453.]: 'hyperbolic_regression': minmax = FALSE [20250519_004453.]: Entered 'cubic_regression'-Function [20250519_004453.]: 'cubic_regression': minmax = FALSE [20250519_004453.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) [20250519_004453.]: Logging df_agg: CpG#8 [20250519_004453.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004453.]: c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)[20250519_004453.]: c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)[20250519_004453.]: c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) [20250519_004453.]: Entered 'hyperbolic_regression'-Function [20250519_004453.]: 'hyperbolic_regression': minmax = FALSE [20250519_004453.]: Entered 'cubic_regression'-Function [20250519_004453.]: 'cubic_regression': minmax = FALSE [20250519_004453.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) [20250519_004453.]: Logging df_agg: CpG#9 [20250519_004453.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004453.]: c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)[20250519_004453.]: c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)[20250519_004453.]: c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) [20250519_004453.]: Entered 'hyperbolic_regression'-Function [20250519_004453.]: 'hyperbolic_regression': minmax = FALSE [20250519_004453.]: Entered 'cubic_regression'-Function [20250519_004453.]: 'cubic_regression': minmax = FALSE [20250519_004453.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) [20250519_004453.]: Logging df_agg: row_means [20250519_004453.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004453.]: c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)[20250519_004453.]: c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)[20250519_004453.]: c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) [20250519_004453.]: Entered 'hyperbolic_regression'-Function [20250519_004453.]: 'hyperbolic_regression': minmax = FALSE [20250519_004454.]: Entered 'cubic_regression'-Function [20250519_004454.]: 'cubic_regression': minmax = FALSE [20250519_004454.]: ### Starting with plotting ### [20250519_004454.]: Creating BiasCorrected (cubic) plot No. 1 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG1_corrected_c.png [20250519_004454.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -1164.77580684101 , a = 2039.49844669959 , b = -1164.77580684101 , d = 1955.83553683067 [20250519_004454.]: # CpG-site: CpG#1 Cubic: Using a = -3.42821196240366e-05 , b = 0.00462173155013798 , c = 0.850326258865826 , d = 0.500073874211125 [20250519_004456.]: Creating BiasCorrected (cubic) plot No. 2 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG2_corrected_c.png [20250519_004456.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = -464.128853555559 , a = 4988.85258428155 , b = -464.128853555559 , d = 4925.32559808582 [20250519_004456.]: # CpG-site: CpG#2 Cubic: Using a = -1.40811796906314e-05 , b = 0.00190849431754485 , c = 0.933455870941473 , d = 0.363096210099674 [20250519_004457.]: Creating BiasCorrected (cubic) plot No. 3 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG3_corrected_c.png [20250519_004457.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = -745.611435752166 , a = 2026.53033232932 , b = -745.611435752166 , d = 1953.28002286922 [20250519_004457.]: # CpG-site: CpG#3 Cubic: Using a = -2.78835051857186e-05 , b = 0.00366863707889089 , c = 0.880665171220893 , d = 0.510779308833653 [20250519_004458.]: Creating BiasCorrected (cubic) plot No. 4 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG4_corrected_c.png [20250519_004459.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = -833.073059054674 , a = 2871.1407505286 , b = -833.073059054674 , d = 2796.8155099796 [20250519_004459.]: # CpG-site: CpG#4 Cubic: Using a = -2.30028865643e-05 , b = 0.00309017851940692 , c = 0.897073107777645 , d = 0.443455761841866 [20250519_004500.]: Creating BiasCorrected (cubic) plot No. 5 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG5_corrected_c.png [20250519_004500.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = 222.127659665624 , a = 12477.5957294815 , b = 222.127659665624 , d = 12432.2636419037 [20250519_004500.]: # CpG-site: CpG#5 Cubic: Using a = -1.28632696989134e-05 , b = 0.00184822285299014 , c = 0.931001046631176 , d = 0.437963095185599 [20250519_004501.]: Creating BiasCorrected (cubic) plot No. 6 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG6_corrected_c.png [20250519_004501.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = -700.510534379779 , a = 21299.1825568909 , b = -700.510534379779 , d = 21234.5878606004 [20250519_004501.]: # CpG-site: CpG#6 Cubic: Using a = -5.838335151375e-06 , b = 0.000828447163218526 , c = 0.970047764475166 , d = 0.158074701412251 [20250519_004503.]: Creating BiasCorrected (cubic) plot No. 7 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG7_corrected_c.png [20250519_004503.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -1144.90822837929 , a = 1477.91227480711 , b = -1144.90822837929 , d = 1394.70075909546 [20250519_004503.]: # CpG-site: CpG#7 Cubic: Using a = -4.54220535614047e-05 , b = 0.00607993130565131 , c = 0.805411493452389 , d = 0.615125382790254 [20250519_004504.]: Creating BiasCorrected (cubic) plot No. 8 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG8_corrected_c.png [20250519_004504.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = -349.378022452652 , a = 2377.37834502072 , b = -349.378022452652 , d = 2315.2055766781 [20250519_004504.]: # CpG-site: CpG#8 Cubic: Using a = -2.64622306669639e-05 , b = 0.00353621593528015 , c = 0.877916844709074 , d = 0.698684606493051 [20250519_004506.]: Creating BiasCorrected (cubic) plot No. 9 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG9_corrected_c.png [20250519_004506.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -1536.91601393605 , a = 3745.17687624221 , b = -1536.91601393605 , d = 3656.2687244524 [20250519_004506.]: # CpG-site: CpG#9 Cubic: Using a = -2.70976130611725e-05 , b = 0.00378478247182342 , c = 0.871825263051793 , d = 0.455475740974113 [20250519_004507.]: Creating BiasCorrected (cubic) plot No. 10 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_rowmeans_corrected_c.png [20250519_004507.]: # CpG-site: row_means Hyperbolic: Using bias_weight = -820.650348722241 , a = 3198.46441212347 , b = -820.650348722241 , d = 3124.20895615508 [20250519_004507.]: # CpG-site: row_means Cubic: Using a = -2.34328362100009e-05 , b = 0.00319139073176266 , c = 0.891842168032129 , d = 0.493517689787135 [20250519_004509.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG1_corrected_c.png [20250519_004510.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG2_corrected_c.png [20250519_004511.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG3_corrected_c.png [20250519_004512.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG4_corrected_c.png [20250519_004514.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG5_corrected_c.png [20250519_004515.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG6_corrected_c.png [20250519_004516.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG7_corrected_c.png [20250519_004517.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG8_corrected_c.png [20250519_004519.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG9_corrected_c.png [20250519_004520.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_rowmeans_corrected_c.png [20250519_004521.]: Entered 'solving_equations'-Function [20250519_004521.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 79.8673456895745 [20250519_004521.]: Samplename: Sample#1 Root: 79.867 --> Root in between the borders! Added to results. Hyperbolic solved: 29.7900184340805 [20250519_004521.]: Samplename: Sample#10 Root: 29.79 --> Root in between the borders! Added to results. Hyperbolic solved: 41.6525415639691 [20250519_004521.]: Samplename: Sample#2 Root: 41.653 --> Root in between the borders! Added to results. Hyperbolic solved: 57.4652090254513 [20250519_004521.]: Samplename: Sample#3 Root: 57.465 --> Root in between the borders! Added to results. Hyperbolic solved: 9.2007130627765 [20250519_004521.]: Samplename: Sample#4 Root: 9.201 --> Root in between the borders! Added to results. Hyperbolic solved: 21.8059600538131 [20250519_004521.]: Samplename: Sample#5 Root: 21.806 --> Root in between the borders! Added to results. Hyperbolic solved: 23.083796735881 [20250519_004521.]: Samplename: Sample#6 Root: 23.084 --> Root in between the borders! Added to results. Hyperbolic solved: 45.5034245569385 [20250519_004521.]: Samplename: Sample#7 Root: 45.503 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6987904075704 [20250519_004521.]: Samplename: Sample#8 Root: 85.699 --> Root in between the borders! Added to results. Hyperbolic solved: -3.66512807265101 [20250519_004521.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -3.665 --> '-10 < root < 0' --> substitute 0 [20250519_004521.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 76.7227931065881 [20250519_004521.]: Samplename: Sample#1 Root: 76.723 --> Root in between the borders! Added to results. Hyperbolic solved: 30.992427511473 [20250519_004521.]: Samplename: Sample#10 Root: 30.992 --> Root in between the borders! Added to results. Hyperbolic solved: 42.6810882780827 [20250519_004521.]: Samplename: Sample#2 Root: 42.681 --> Root in between the borders! Added to results. Hyperbolic solved: 58.9337908379889 [20250519_004521.]: Samplename: Sample#3 Root: 58.934 --> Root in between the borders! Added to results. Hyperbolic solved: 4.98020457313721 [20250519_004521.]: Samplename: Sample#4 Root: 4.98 --> Root in between the borders! Added to results. Hyperbolic solved: 20.2480263675669 [20250519_004521.]: Samplename: Sample#5 Root: 20.248 --> Root in between the borders! Added to results. Hyperbolic solved: 17.8133085643845 [20250519_004521.]: Samplename: Sample#6 Root: 17.813 --> Root in between the borders! Added to results. Hyperbolic solved: 40.9176745340012 [20250519_004521.]: Samplename: Sample#7 Root: 40.918 --> Root in between the borders! Added to results. Hyperbolic solved: 86.0450715732916 [20250519_004521.]: Samplename: Sample#8 Root: 86.045 --> Root in between the borders! Added to results. Hyperbolic solved: 3.11579770508969 [20250519_004521.]: Samplename: Sample#9 Root: 3.116 --> Root in between the borders! Added to results. [20250519_004521.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 74.9349254100163 [20250519_004521.]: Samplename: Sample#1 Root: 74.935 --> Root in between the borders! Added to results. Hyperbolic solved: 27.6844381581493 [20250519_004521.]: Samplename: Sample#10 Root: 27.684 --> Root in between the borders! Added to results. Hyperbolic solved: 41.852019114379 [20250519_004521.]: Samplename: Sample#2 Root: 41.852 --> Root in between the borders! Added to results. Hyperbolic solved: 55.8325180209418 [20250519_004521.]: Samplename: Sample#3 Root: 55.833 --> Root in between the borders! Added to results. Hyperbolic solved: 8.03519251633153 [20250519_004521.]: Samplename: Sample#4 Root: 8.035 --> Root in between the borders! Added to results. Hyperbolic solved: 24.1066315721853 [20250519_004521.]: Samplename: Sample#5 Root: 24.107 --> Root in between the borders! Added to results. Hyperbolic solved: 26.2419820027673 [20250519_004521.]: Samplename: Sample#6 Root: 26.242 --> Root in between the borders! Added to results. Hyperbolic solved: 44.0944922703422 [20250519_004521.]: Samplename: Sample#7 Root: 44.094 --> Root in between the borders! Added to results. Hyperbolic solved: 85.8279382585787 [20250519_004521.]: Samplename: Sample#8 Root: 85.828 --> Root in between the borders! Added to results. Hyperbolic solved: -0.666482392725758 [20250519_004521.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.666 --> '-10 < root < 0' --> substitute 0 [20250519_004521.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 76.3495278640236 [20250519_004521.]: Samplename: Sample#1 Root: 76.35 --> Root in between the borders! Added to results. Hyperbolic solved: 28.2568553570941 [20250519_004521.]: Samplename: Sample#10 Root: 28.257 --> Root in between the borders! Added to results. Hyperbolic solved: 43.4089839390807 [20250519_004521.]: Samplename: Sample#2 Root: 43.409 --> Root in between the borders! Added to results. Hyperbolic solved: 58.5435236860146 [20250519_004521.]: Samplename: Sample#3 Root: 58.544 --> Root in between the borders! Added to results. Hyperbolic solved: 10.3087045690571 [20250519_004521.]: Samplename: Sample#4 Root: 10.309 --> Root in between the borders! Added to results. Hyperbolic solved: 22.183045165659 [20250519_004521.]: Samplename: Sample#5 Root: 22.183 --> Root in between the borders! Added to results. Hyperbolic solved: 27.1337769553499 [20250519_004521.]: Samplename: Sample#6 Root: 27.134 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8321096080155 [20250519_004521.]: Samplename: Sample#7 Root: 41.832 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6890189074743 [20250519_004521.]: Samplename: Sample#8 Root: 85.689 --> Root in between the borders! Added to results. Hyperbolic solved: 2.42232098177269 [20250519_004521.]: Samplename: Sample#9 Root: 2.422 --> Root in between the borders! Added to results. [20250519_004521.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 72.8126914355011 [20250519_004521.]: Samplename: Sample#1 Root: 72.813 --> Root in between the borders! Added to results. Hyperbolic solved: 26.4206935815673 [20250519_004521.]: Samplename: Sample#10 Root: 26.421 --> Root in between the borders! Added to results. Hyperbolic solved: 44.2738765057261 [20250519_004521.]: Samplename: Sample#2 Root: 44.274 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5324386232341 [20250519_004521.]: Samplename: Sample#3 Root: 53.532 --> Root in between the borders! Added to results. Hyperbolic solved: 10.0373717962154 [20250519_004521.]: Samplename: Sample#4 Root: 10.037 --> Root in between the borders! Added to results. Hyperbolic solved: 22.8733115052607 [20250519_004521.]: Samplename: Sample#5 Root: 22.873 --> Root in between the borders! Added to results. Hyperbolic solved: 25.9417915784748 [20250519_004521.]: Samplename: Sample#6 Root: 25.942 --> Root in between the borders! Added to results. Hyperbolic solved: 42.7945074918604 [20250519_004521.]: Samplename: Sample#7 Root: 42.795 --> Root in between the borders! Added to results. Hyperbolic solved: 88.8741214630346 [20250519_004521.]: Samplename: Sample#8 Root: 88.874 --> Root in between the borders! Added to results. Hyperbolic solved: 2.34647529966966 [20250519_004521.]: Samplename: Sample#9 Root: 2.346 --> Root in between the borders! Added to results. [20250519_004521.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 79.2780593622711 [20250519_004521.]: Samplename: Sample#1 Root: 79.278 --> Root in between the borders! Added to results. Hyperbolic solved: 30.2012458984074 [20250519_004521.]: Samplename: Sample#10 Root: 30.201 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8474393624107 [20250519_004521.]: Samplename: Sample#2 Root: 41.847 --> Root in between the borders! Added to results. Hyperbolic solved: 56.8423517321508 [20250519_004521.]: Samplename: Sample#3 Root: 56.842 --> Root in between the borders! Added to results. Hyperbolic solved: 8.87856046118588 [20250519_004521.]: Samplename: Sample#4 Root: 8.879 --> Root in between the borders! Added to results. Hyperbolic solved: 18.69015950004 [20250519_004521.]: Samplename: Sample#5 Root: 18.69 --> Root in between the borders! Added to results. Hyperbolic solved: 29.9309263534749 [20250519_004521.]: Samplename: Sample#6 Root: 29.931 --> Root in between the borders! Added to results. Hyperbolic solved: 42.8148560027697 [20250519_004521.]: Samplename: Sample#7 Root: 42.815 --> Root in between the borders! Added to results. Hyperbolic solved: 86.7501831416152 [20250519_004521.]: Samplename: Sample#8 Root: 86.75 --> Root in between the borders! Added to results. Hyperbolic solved: 1.51516194985267 [20250519_004521.]: Samplename: Sample#9 Root: 1.515 --> Root in between the borders! Added to results. [20250519_004521.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 78.2565592569279 [20250519_004521.]: Samplename: Sample#1 Root: 78.257 --> Root in between the borders! Added to results. Hyperbolic solved: 25.488739349283 [20250519_004521.]: Samplename: Sample#10 Root: 25.489 --> Root in between the borders! Added to results. Hyperbolic solved: 47.3712258915285 [20250519_004521.]: Samplename: Sample#2 Root: 47.371 --> Root in between the borders! Added to results. Hyperbolic solved: 58.3142673189298 [20250519_004521.]: Samplename: Sample#3 Root: 58.314 --> Root in between the borders! Added to results. Hyperbolic solved: 11.7212231360573 [20250519_004521.]: Samplename: Sample#4 Root: 11.721 --> Root in between the borders! Added to results. Hyperbolic solved: 25.3797485992238 [20250519_004521.]: Samplename: Sample#5 Root: 25.38 --> Root in between the borders! Added to results. Hyperbolic solved: 29.4095133062523 [20250519_004521.]: Samplename: Sample#6 Root: 29.41 --> Root in between the borders! Added to results. Hyperbolic solved: 44.5755071469546 [20250519_004521.]: Samplename: Sample#7 Root: 44.576 --> Root in between the borders! Added to results. Hyperbolic solved: 85.9628731021447 [20250519_004521.]: Samplename: Sample#8 Root: 85.963 --> Root in between the borders! Added to results. Hyperbolic solved: -4.1645647175353 [20250519_004521.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -4.165 --> '-10 < root < 0' --> substitute 0 [20250519_004521.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 72.9696605050898 [20250519_004521.]: Samplename: Sample#1 Root: 72.97 --> Root in between the borders! Added to results. Hyperbolic solved: 27.8250956361743 [20250519_004521.]: Samplename: Sample#10 Root: 27.825 --> Root in between the borders! Added to results. Hyperbolic solved: 34.9068388175484 [20250519_004521.]: Samplename: Sample#2 Root: 34.907 --> Root in between the borders! Added to results. Hyperbolic solved: 59.163743824213 [20250519_004521.]: Samplename: Sample#3 Root: 59.164 --> Root in between the borders! Added to results. Hyperbolic solved: 9.64644413255063 [20250519_004521.]: Samplename: Sample#4 Root: 9.646 --> Root in between the borders! Added to results. Hyperbolic solved: 19.3522607837916 [20250519_004522.]: Samplename: Sample#5 Root: 19.352 --> Root in between the borders! Added to results. Hyperbolic solved: 38.5571436812761 [20250519_004522.]: Samplename: Sample#6 Root: 38.557 --> Root in between the borders! Added to results. Hyperbolic solved: 44.7759625743335 [20250519_004522.]: Samplename: Sample#7 Root: 44.776 --> Root in between the borders! Added to results. Hyperbolic solved: 84.8599671624974 [20250519_004522.]: Samplename: Sample#8 Root: 84.86 --> Root in between the borders! Added to results. Hyperbolic solved: -2.05044894070214 [20250519_004522.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.05 --> '-10 < root < 0' --> substitute 0 [20250519_004522.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 81.041667802328 [20250519_004522.]: Samplename: Sample#1 Root: 81.042 --> Root in between the borders! Added to results. Hyperbolic solved: 26.6441666289306 [20250519_004522.]: Samplename: Sample#10 Root: 26.644 --> Root in between the borders! Added to results. Hyperbolic solved: 45.7046199658262 [20250519_004522.]: Samplename: Sample#2 Root: 45.705 --> Root in between the borders! Added to results. Hyperbolic solved: 56.9767986854544 [20250519_004522.]: Samplename: Sample#3 Root: 56.977 --> Root in between the borders! Added to results. Hyperbolic solved: 6.81231222322667 [20250519_004522.]: Samplename: Sample#4 Root: 6.812 --> Root in between the borders! Added to results. Hyperbolic solved: 22.9284780361346 [20250519_004522.]: Samplename: Sample#5 Root: 22.928 --> Root in between the borders! Added to results. Hyperbolic solved: 38.8653203361426 [20250519_004522.]: Samplename: Sample#6 Root: 38.865 --> Root in between the borders! Added to results. Hyperbolic solved: 43.6855631975613 [20250519_004522.]: Samplename: Sample#7 Root: 43.686 --> Root in between the borders! Added to results. Hyperbolic solved: 88.0155364495973 [20250519_004522.]: Samplename: Sample#8 Root: 88.016 --> Root in between the borders! Added to results. Hyperbolic solved: -3.35610072407525 [20250519_004522.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -3.356 --> '-10 < root < 0' --> substitute 0 [20250519_004522.]: Solving hyperbolic regression for row_means Hyperbolic solved: 77.0692799954538 [20250519_004522.]: Samplename: Sample#1 Root: 77.069 --> Root in between the borders! Added to results. Hyperbolic solved: 28.3620043244584 [20250519_004522.]: Samplename: Sample#10 Root: 28.362 --> Root in between the borders! Added to results. Hyperbolic solved: 42.5026175468089 [20250519_004522.]: Samplename: Sample#2 Root: 42.503 --> Root in between the borders! Added to results. Hyperbolic solved: 57.2972050444248 [20250519_004522.]: Samplename: Sample#3 Root: 57.297 --> Root in between the borders! Added to results. Hyperbolic solved: 8.82704012618393 [20250519_004522.]: Samplename: Sample#4 Root: 8.827 --> Root in between the borders! Added to results. Hyperbolic solved: 21.8102592524881 [20250519_004522.]: Samplename: Sample#5 Root: 21.81 --> Root in between the borders! Added to results. Hyperbolic solved: 28.7228660045975 [20250519_004522.]: Samplename: Sample#6 Root: 28.723 --> Root in between the borders! Added to results. Hyperbolic solved: 43.410510290696 [20250519_004522.]: Samplename: Sample#7 Root: 43.411 --> Root in between the borders! Added to results. Hyperbolic solved: 86.4143551963301 [20250519_004522.]: Samplename: Sample#8 Root: 86.414 --> Root in between the borders! Added to results. Hyperbolic solved: -0.237020571201611 [20250519_004522.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.237 --> '-10 < root < 0' --> substitute 0 [20250519_004522.]: on_start: using future::plan("sequential") [20250519_004522.]: Entered 'clean_dt'-Function [20250519_004522.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_004522.]: got experimental data [20250519_004522.]: Entered 'clean_dt'-Function [20250519_004522.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_004522.]: got calibration data [20250519_004522.]: ### Starting with regression calculations ### [20250519_004522.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_004522.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_004522.]: Logging df_agg: CpG#1 [20250519_004522.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004522.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_004522.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_004522.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_004522.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_004522.]: Entered 'hyperbolic_regression'-Function [20250519_004522.]: 'hyperbolic_regression': minmax = FALSE [20250519_004522.]: Entered 'cubic_regression'-Function [20250519_004522.]: 'cubic_regression': minmax = FALSE [20250519_004522.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_004522.]: Logging df_agg: CpG#2 [20250519_004522.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004522.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_004522.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_004522.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_004522.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_004522.]: Entered 'hyperbolic_regression'-Function [20250519_004522.]: 'hyperbolic_regression': minmax = FALSE [20250519_004522.]: Entered 'cubic_regression'-Function [20250519_004522.]: 'cubic_regression': minmax = FALSE [20250519_004522.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_004522.]: Logging df_agg: CpG#3 [20250519_004522.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004522.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_004522.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_004522.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_004522.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_004522.]: Entered 'hyperbolic_regression'-Function [20250519_004522.]: 'hyperbolic_regression': minmax = FALSE [20250519_004523.]: Entered 'cubic_regression'-Function [20250519_004523.]: 'cubic_regression': minmax = FALSE [20250519_004523.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_004523.]: Logging df_agg: CpG#4 [20250519_004523.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004523.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_004523.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_004523.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_004523.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_004523.]: Entered 'hyperbolic_regression'-Function [20250519_004523.]: 'hyperbolic_regression': minmax = FALSE [20250519_004523.]: Entered 'cubic_regression'-Function [20250519_004523.]: 'cubic_regression': minmax = FALSE [20250519_004523.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_004523.]: Logging df_agg: CpG#5 [20250519_004523.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004523.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_004523.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_004523.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_004523.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_004523.]: Entered 'hyperbolic_regression'-Function [20250519_004523.]: 'hyperbolic_regression': minmax = FALSE [20250519_004523.]: Entered 'cubic_regression'-Function [20250519_004523.]: 'cubic_regression': minmax = FALSE [20250519_004523.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_004523.]: Logging df_agg: CpG#6 [20250519_004523.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004523.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_004523.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_004523.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_004523.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_004523.]: Entered 'hyperbolic_regression'-Function [20250519_004523.]: 'hyperbolic_regression': minmax = FALSE [20250519_004523.]: Entered 'cubic_regression'-Function [20250519_004523.]: 'cubic_regression': minmax = FALSE [20250519_004523.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_004523.]: Logging df_agg: CpG#7 [20250519_004523.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004523.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_004523.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_004523.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_004523.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_004523.]: Entered 'hyperbolic_regression'-Function [20250519_004523.]: 'hyperbolic_regression': minmax = FALSE [20250519_004523.]: Entered 'cubic_regression'-Function [20250519_004523.]: 'cubic_regression': minmax = FALSE [20250519_004523.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_004523.]: Logging df_agg: CpG#8 [20250519_004523.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004523.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_004523.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_004523.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_004523.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_004523.]: Entered 'hyperbolic_regression'-Function [20250519_004523.]: 'hyperbolic_regression': minmax = FALSE [20250519_004524.]: Entered 'cubic_regression'-Function [20250519_004524.]: 'cubic_regression': minmax = FALSE [20250519_004524.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_004524.]: Logging df_agg: CpG#9 [20250519_004524.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004524.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_004524.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_004524.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_004524.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_004524.]: Entered 'hyperbolic_regression'-Function [20250519_004524.]: 'hyperbolic_regression': minmax = FALSE [20250519_004524.]: Entered 'cubic_regression'-Function [20250519_004524.]: 'cubic_regression': minmax = FALSE [20250519_004524.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723122, 2.47760998435345)c(5.94911111111111, 0.84962962962963, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703704, 19.6995555555555, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_004524.]: Logging df_agg: row_means [20250519_004524.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004524.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_004524.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723122, 2.47760998435345)[20250519_004524.]: c(5.94911111111111, 0.84962962962963, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_004524.]: c(NA, 6.79703703703704, 19.6995555555555, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_004524.]: Entered 'hyperbolic_regression'-Function [20250519_004524.]: 'hyperbolic_regression': minmax = FALSE [20250519_004524.]: Entered 'cubic_regression'-Function [20250519_004524.]: 'cubic_regression': minmax = FALSE [20250519_004525.]: ### Starting with plotting ### [20250519_004525.]: Creating plot No. 1 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG1.png [20250519_004525.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -937.71833725671 , a = -108.568188547925 , b = -937.71833725671 , d = -232.05735859911 [20250519_004525.]: # CpG-site: CpG#1 Cubic: Using a = 6.53413423120091e-05 , b = -0.0055806968734969 , c = 0.784061853455188 , d = 1.93182659932656 [20250519_004526.]: Creating plot No. 2 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG2.png [20250519_004526.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = -2393.14264996499 , a = -141.862267639951 , b = -2393.14264996499 , d = -268.331587255735 [20250519_004526.]: # CpG-site: CpG#2 Cubic: Using a = 8.04237934904601e-06 , b = 0.00293158941798942 , c = 0.514821742825076 , d = 9.27667003367003 [20250519_004528.]: Creating plot No. 3 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG3.png [20250519_004528.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = -1477.46867711742 , a = -170.424279984248 , b = -1477.46867711742 , d = -327.189866698889 [20250519_004528.]: # CpG-site: CpG#3 Cubic: Using a = 2.30555869809204e-05 , b = -0.000797009331409346 , c = 0.62783129228796 , d = 3.88705218855218 [20250519_004529.]: Creating plot No. 4 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG4.png [20250519_004529.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = -2115.87814484424 , a = -162.169929539519 , b = -2115.87814484424 , d = -299.662454768746 [20250519_004529.]: # CpG-site: CpG#4 Cubic: Using a = 3.2166356902357e-05 , b = -0.0015913142857143 , c = 0.697398364598366 , d = 6.17055050505048 [20250519_004531.]: Creating plot No. 5 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG5.png [20250519_004531.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = -1383.32734884945 , a = -151.440088502132 , b = -1383.32734884945 , d = -304.200280997675 [20250519_004531.]: # CpG-site: CpG#5 Cubic: Using a = -4.48459708193036e-06 , b = 0.00375371236171235 , c = 0.422446384479718 , d = 5.17203872053872 [20250519_004532.]: Creating plot No. 6 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG6.png [20250519_004532.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = -1550.59304894212 , a = -125.016150436792 , b = -1550.59304894212 , d = -247.279683783527 [20250519_004532.]: # CpG-site: CpG#6 Cubic: Using a = 2.38852884399552e-05 , b = 0.000918637037037021 , c = 0.561022132435467 , d = 6.1479276094276 [20250519_004534.]: Creating plot No. 7 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG7.png [20250519_004534.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -772.843265948977 , a = -75.2726697123378 , b = -772.843265948977 , d = -226.67343046903 [20250519_004534.]: # CpG-site: CpG#7 Cubic: Using a = 4.98010505050505e-05 , b = -0.00436152150072151 , c = 0.579588917748918 , d = 1.72104545454544 [20250519_004535.]: Creating plot No. 8 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG8.png [20250519_004535.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = -3872.73534838154 , a = -278.017155483789 , b = -3872.73534838154 , d = -438.894126934244 [20250519_004535.]: # CpG-site: CpG#8 Cubic: Using a = -1.50060965207632e-05 , b = 0.00447978143338143 , c = 0.534897737694404 , d = 9.56981481481482 [20250519_004537.]: Creating plot No. 9 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG9.png [20250519_004537.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -843.925271611492 , a = -70.6796326247736 , b = -843.925271611492 , d = -184.482178799669 [20250519_004537.]: # CpG-site: CpG#9 Cubic: Using a = 7.72300426487093e-05 , b = -0.00542281173641174 , c = 0.67899229597563 , d = 2.72413468013467 [20250519_004538.]: Creating plot No. 10 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_rowmeans.png [20250519_004538.]: # CpG-site: row_means Hyperbolic: Using bias_weight = -1525.75751056682 , a = -127.675093193972 , b = -1525.75751056682 , d = -263.003121358972 [20250519_004538.]: # CpG-site: row_means Cubic: Using a = 2.88923726150392e-05 , b = -0.0006299592752926 , c = 0.600117857944524 , d = 5.17789562289563 [20250519_004540.]: Entered 'solving_equations'-Function [20250519_004540.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: -2.23220911684257 [20250519_004540.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.232 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.1698564372971 [20250519_004540.]: Samplename: 12.5 Root: 12.17 --> Root in between the borders! Added to results. Hyperbolic solved: 24.478190918656 [20250519_004540.]: Samplename: 25 Root: 24.478 --> Root in between the borders! Added to results. Hyperbolic solved: 38.1730371634976 [20250519_004540.]: Samplename: 37.5 Root: 38.173 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3349263290239 [20250519_004540.]: Samplename: 50 Root: 52.335 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4582665283612 [20250519_004540.]: Samplename: 62.5 Root: 65.458 --> Root in between the borders! Added to results. Hyperbolic solved: 75.0090705794847 [20250519_004540.]: Samplename: 75 Root: 75.009 --> Root in between the borders! Added to results. Hyperbolic solved: 81.5271853400487 [20250519_004540.]: Samplename: 87.5 Root: 81.527 --> Root in between the borders! Added to results. Hyperbolic solved: 102.400898271831 [20250519_004540.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.401 --> '100 < root < 110' --> substitute 100 [20250519_004540.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 1.13660511681856 [20250519_004540.]: Samplename: 0 Root: 1.137 --> Root in between the borders! Added to results. Hyperbolic solved: 11.4129696918445 [20250519_004540.]: Samplename: 12.5 Root: 11.413 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1740004612661 [20250519_004540.]: Samplename: 25 Root: 26.174 --> Root in between the borders! Added to results. Hyperbolic solved: 35.1050448132614 [20250519_004540.]: Samplename: 37.5 Root: 35.105 --> Root in between the borders! Added to results. Hyperbolic solved: 47.6855002074492 [20250519_004540.]: Samplename: 50 Root: 47.686 --> Root in between the borders! Added to results. Hyperbolic solved: 67.1440493313574 [20250519_004540.]: Samplename: 62.5 Root: 67.144 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7644668045486 [20250519_004540.]: Samplename: 75 Root: 75.764 --> Root in between the borders! Added to results. Hyperbolic solved: 84.4054158145455 [20250519_004540.]: Samplename: 87.5 Root: 84.405 --> Root in between the borders! Added to results. Hyperbolic solved: 100.948272543309 [20250519_004540.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.948 --> '100 < root < 110' --> substitute 100 [20250519_004540.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 0.512348561877098 [20250519_004540.]: Samplename: 0 Root: 0.512 --> Root in between the borders! Added to results. Hyperbolic solved: 10.7523845008661 [20250519_004540.]: Samplename: 12.5 Root: 10.752 --> Root in between the borders! Added to results. Hyperbolic solved: 25.5218912317424 [20250519_004540.]: Samplename: 25 Root: 25.522 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5270488279988 [20250519_004540.]: Samplename: 37.5 Root: 36.527 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7909283805111 [20250519_004540.]: Samplename: 50 Root: 50.791 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8686353441458 [20250519_004540.]: Samplename: 62.5 Root: 64.869 --> Root in between the borders! Added to results. Hyperbolic solved: 77.5524208271734 [20250519_004540.]: Samplename: 75 Root: 77.552 --> Root in between the borders! Added to results. Hyperbolic solved: 80.4374631680835 [20250519_004540.]: Samplename: 87.5 Root: 80.437 --> Root in between the borders! Added to results. Hyperbolic solved: 102.704019894026 [20250519_004540.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.704 --> '100 < root < 110' --> substitute 100 [20250519_004540.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: -0.519496428117613 [20250519_004540.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.519 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.4934173132007 [20250519_004540.]: Samplename: 12.5 Root: 12.493 --> Root in between the borders! Added to results. Hyperbolic solved: 24.2685417185317 [20250519_004540.]: Samplename: 25 Root: 24.269 --> Root in between the borders! Added to results. Hyperbolic solved: 38.0817103888785 [20250519_004540.]: Samplename: 37.5 Root: 38.082 --> Root in between the borders! Added to results. Hyperbolic solved: 48.5843148203973 [20250519_004540.]: Samplename: 50 Root: 48.584 --> Root in between the borders! Added to results. Hyperbolic solved: 67.672236894341 [20250519_004540.]: Samplename: 62.5 Root: 67.672 --> Root in between the borders! Added to results. Hyperbolic solved: 74.1549253766253 [20250519_004540.]: Samplename: 75 Root: 74.155 --> Root in between the borders! Added to results. Hyperbolic solved: 82.8821786440368 [20250519_004540.]: Samplename: 87.5 Root: 82.882 --> Root in between the borders! Added to results. Hyperbolic solved: 102.079130232402 [20250519_004540.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.079 --> '100 < root < 110' --> substitute 100 [20250519_004540.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 2.41558718123367 [20250519_004540.]: Samplename: 0 Root: 2.416 --> Root in between the borders! Added to results. Hyperbolic solved: 10.1649680545674 [20250519_004540.]: Samplename: 12.5 Root: 10.165 --> Root in between the borders! Added to results. Hyperbolic solved: 23.9830821073418 [20250519_004540.]: Samplename: 25 Root: 23.983 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2773617400679 [20250519_004540.]: Samplename: 37.5 Root: 37.277 --> Root in between the borders! Added to results. Hyperbolic solved: 50.8659382464167 [20250519_004540.]: Samplename: 50 Root: 50.866 --> Root in between the borders! Added to results. Hyperbolic solved: 62.4342269461158 [20250519_004540.]: Samplename: 62.5 Root: 62.434 --> Root in between the borders! Added to results. Hyperbolic solved: 76.3915257997575 [20250519_004540.]: Samplename: 75 Root: 76.392 --> Root in between the borders! Added to results. Hyperbolic solved: 86.1597887396996 [20250519_004540.]: Samplename: 87.5 Root: 86.16 --> Root in between the borders! Added to results. Hyperbolic solved: 100.267760316884 [20250519_004540.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.268 --> '100 < root < 110' --> substitute 100 [20250519_004540.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 0.138164161050506 [20250519_004540.]: Samplename: 0 Root: 0.138 --> Root in between the borders! Added to results. Hyperbolic solved: 11.8635561171135 [20250519_004540.]: Samplename: 12.5 Root: 11.864 --> Root in between the borders! Added to results. Hyperbolic solved: 26.510745006666 [20250519_004540.]: Samplename: 25 Root: 26.511 --> Root in between the borders! Added to results. Hyperbolic solved: 35.3205072777727 [20250519_004540.]: Samplename: 37.5 Root: 35.321 --> Root in between the borders! Added to results. Hyperbolic solved: 50.0570766444694 [20250519_004540.]: Samplename: 50 Root: 50.057 --> Root in between the borders! Added to results. Hyperbolic solved: 64.9602942986125 [20250519_004540.]: Samplename: 62.5 Root: 64.96 --> Root in between the borders! Added to results. Hyperbolic solved: 73.6689055886064 [20250519_004540.]: Samplename: 75 Root: 73.669 --> Root in between the borders! Added to results. Hyperbolic solved: 87.1266085894673 [20250519_004540.]: Samplename: 87.5 Root: 87.127 --> Root in between the borders! Added to results. Hyperbolic solved: 100.261637049252 [20250519_004540.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.262 --> '100 < root < 110' --> substitute 100 [20250519_004540.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: -1.37235519395329 [20250519_004540.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -1.372 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.1993294013604 [20250519_004540.]: Samplename: 12.5 Root: 10.199 --> Root in between the borders! Added to results. Hyperbolic solved: 24.5951803850766 [20250519_004540.]: Samplename: 25 Root: 24.595 --> Root in between the borders! Added to results. Hyperbolic solved: 37.8310364564335 [20250519_004540.]: Samplename: 37.5 Root: 37.831 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5588645818264 [20250519_004540.]: Samplename: 50 Root: 53.559 --> Root in between the borders! Added to results. Hyperbolic solved: 65.9364860153449 [20250519_004540.]: Samplename: 62.5 Root: 65.936 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7361033620359 [20250519_004540.]: Samplename: 75 Root: 75.736 --> Root in between the borders! Added to results. Hyperbolic solved: 79.4328192070151 [20250519_004540.]: Samplename: 87.5 Root: 79.433 --> Root in between the borders! Added to results. Hyperbolic solved: 103.004248773309 [20250519_004540.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 103.004 --> '100 < root < 110' --> substitute 100 [20250519_004540.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 2.80068093389159 [20250519_004540.]: Samplename: 0 Root: 2.801 --> Root in between the borders! Added to results. Hyperbolic solved: 9.27535066703723 [20250519_004540.]: Samplename: 12.5 Root: 9.275 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4762625870809 [20250519_004540.]: Samplename: 25 Root: 25.476 --> Root in between the borders! Added to results. Hyperbolic solved: 34.0122083314201 [20250519_004540.]: Samplename: 37.5 Root: 34.012 --> Root in between the borders! Added to results. Hyperbolic solved: 51.7842666342759 [20250519_004540.]: Samplename: 50 Root: 51.784 --> Root in between the borders! Added to results. Hyperbolic solved: 64.6732321058682 [20250519_004540.]: Samplename: 62.5 Root: 64.673 --> Root in between the borders! Added to results. Hyperbolic solved: 78.4326982875914 [20250519_004540.]: Samplename: 75 Root: 78.433 --> Root in between the borders! Added to results. Hyperbolic solved: 81.3427235319597 [20250519_004540.]: Samplename: 87.5 Root: 81.343 --> Root in between the borders! Added to results. Hyperbolic solved: 101.964405325155 [20250519_004540.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.964 --> '100 < root < 110' --> substitute 100 [20250519_004540.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: -2.1340328997008 [20250519_004540.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.134 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.5082212970134 [20250519_004540.]: Samplename: 12.5 Root: 10.508 --> Root in between the borders! Added to results. Hyperbolic solved: 26.9164566258777 [20250519_004540.]: Samplename: 25 Root: 26.916 --> Root in between the borders! Added to results. Hyperbolic solved: 36.8334769505904 [20250519_004540.]: Samplename: 37.5 Root: 36.833 --> Root in between the borders! Added to results. Hyperbolic solved: 52.0097879402751 [20250519_004540.]: Samplename: 50 Root: 52.01 --> Root in between the borders! Added to results. Hyperbolic solved: 64.893051148082 [20250519_004540.]: Samplename: 62.5 Root: 64.893 --> Root in between the borders! Added to results. Hyperbolic solved: 74.5671042786367 [20250519_004540.]: Samplename: 75 Root: 74.567 --> Root in between the borders! Added to results. Hyperbolic solved: 84.5294949210319 [20250519_004540.]: Samplename: 87.5 Root: 84.529 --> Root in between the borders! Added to results. Hyperbolic solved: 101.047147807246 [20250519_004540.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.047 --> '100 < root < 110' --> substitute 100 [20250519_004540.]: Solving hyperbolic regression for row_means Hyperbolic solved: 0.290944901553815 [20250519_004540.]: Samplename: 0 Root: 0.291 --> Root in between the borders! Added to results. Hyperbolic solved: 11.0412427310408 [20250519_004540.]: Samplename: 12.5 Root: 11.041 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4081501338029 [20250519_004540.]: Samplename: 25 Root: 25.408 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5243709582085 [20250519_004540.]: Samplename: 37.5 Root: 36.524 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7348808712519 [20250519_004540.]: Samplename: 50 Root: 50.735 --> Root in between the borders! Added to results. Hyperbolic solved: 65.3135195123017 [20250519_004540.]: Samplename: 62.5 Root: 65.314 --> Root in between the borders! Added to results. Hyperbolic solved: 75.5342699494589 [20250519_004540.]: Samplename: 75 Root: 75.534 --> Root in between the borders! Added to results. Hyperbolic solved: 83.2411225127701 [20250519_004540.]: Samplename: 87.5 Root: 83.241 --> Root in between the borders! Added to results. Hyperbolic solved: 101.666944784378 [20250519_004540.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.667 --> '100 < root < 110' --> substitute 100 [20250519_004540.]: ### Starting with regression calculations ### [20250519_004540.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_004540.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.1698564372971, 24.478190918656, 38.1730371634976, 52.3349263290239, 65.4582665283612, 75.0090705794847, 81.5271853400487, 100)c(0, 0.330143562702901, 0.521809081343999, 0.673037163497597, 2.3349263290239, 2.95826652836119, 0.00907057948469969, 5.9728146599513, 0)c(NA, 2.64114850162321, 2.087236325376, 1.79476576932692, 4.6698526580478, 4.73322644537791, 0.0120941059795996, 6.8260738970872, 0) [20250519_004540.]: Logging df_agg: CpG#1 [20250519_004540.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004540.]: c(0, 12.1698564372971, 24.478190918656, 38.1730371634976, 52.3349263290239, 65.4582665283612, 75.0090705794847, 81.5271853400487, 100)[20250519_004540.]: c(0, 0.330143562702901, 0.521809081343999, 0.673037163497597, 2.3349263290239, 2.95826652836119, 0.00907057948469969, 5.9728146599513, 0)[20250519_004540.]: c(NA, 2.64114850162321, 2.087236325376, 1.79476576932692, 4.6698526580478, 4.73322644537791, 0.0120941059795996, 6.8260738970872, 0) [20250519_004540.]: Entered 'hyperbolic_regression'-Function [20250519_004540.]: 'hyperbolic_regression': minmax = FALSE [20250519_004541.]: Entered 'cubic_regression'-Function [20250519_004541.]: 'cubic_regression': minmax = FALSE [20250519_004541.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.13660511681856, 11.4129696918445, 26.1740004612661, 35.1050448132614, 47.6855002074492, 67.1440493313574, 75.7644668045486, 84.4054158145455, 100)c(1.13660511681856, 1.0870303081555, 1.1740004612661, 2.3949551867386, 2.3144997925508, 4.64404933135739, 0.764466804548604, 3.09458418545449, 0)c(NA, 8.696242465244, 4.6960018450644, 6.38654716463626, 4.6289995851016, 7.43047893017183, 1.01928907273147, 3.53666764051942, 0) [20250519_004541.]: Logging df_agg: CpG#2 [20250519_004541.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004541.]: c(1.13660511681856, 11.4129696918445, 26.1740004612661, 35.1050448132614, 47.6855002074492, 67.1440493313574, 75.7644668045486, 84.4054158145455, 100)[20250519_004541.]: c(1.13660511681856, 1.0870303081555, 1.1740004612661, 2.3949551867386, 2.3144997925508, 4.64404933135739, 0.764466804548604, 3.09458418545449, 0)[20250519_004541.]: c(NA, 8.696242465244, 4.6960018450644, 6.38654716463626, 4.6289995851016, 7.43047893017183, 1.01928907273147, 3.53666764051942, 0) [20250519_004541.]: Entered 'hyperbolic_regression'-Function [20250519_004541.]: 'hyperbolic_regression': minmax = FALSE [20250519_004541.]: Entered 'cubic_regression'-Function [20250519_004541.]: 'cubic_regression': minmax = FALSE [20250519_004541.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.512348561877098, 10.7523845008661, 25.5218912317424, 36.5270488279988, 50.7909283805111, 64.8686353441458, 77.5524208271734, 80.4374631680835, 100)c(0.512348561877098, 1.7476154991339, 0.521891231742401, 0.972951172001203, 0.790928380511097, 2.3686353441458, 2.55242082717341, 7.0625368319165, 0)c(NA, 13.9809239930712, 2.08756492696961, 2.59453645866987, 1.58185676102219, 3.78981655063328, 3.40322776956454, 8.07147066504743, 0) [20250519_004541.]: Logging df_agg: CpG#3 [20250519_004541.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004541.]: c(0.512348561877098, 10.7523845008661, 25.5218912317424, 36.5270488279988, 50.7909283805111, 64.8686353441458, 77.5524208271734, 80.4374631680835, 100)[20250519_004541.]: c(0.512348561877098, 1.7476154991339, 0.521891231742401, 0.972951172001203, 0.790928380511097, 2.3686353441458, 2.55242082717341, 7.0625368319165, 0)[20250519_004541.]: c(NA, 13.9809239930712, 2.08756492696961, 2.59453645866987, 1.58185676102219, 3.78981655063328, 3.40322776956454, 8.07147066504743, 0) [20250519_004541.]: Entered 'hyperbolic_regression'-Function [20250519_004541.]: 'hyperbolic_regression': minmax = FALSE [20250519_004541.]: Entered 'cubic_regression'-Function [20250519_004541.]: 'cubic_regression': minmax = FALSE [20250519_004541.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.4934173132007, 24.2685417185317, 38.0817103888785, 48.5843148203973, 67.672236894341, 74.1549253766253, 82.8821786440368, 100)c(0, 0.00658268679929996, 0.731458281468299, 0.581710388878498, 1.4156851796027, 5.172236894341, 0.8450746233747, 4.6178213559632, 0)c(NA, 0.0526614943943997, 2.9258331258732, 1.551227703676, 2.83137035920539, 8.27557903094559, 1.1267661644996, 5.2775101211008, 0) [20250519_004541.]: Logging df_agg: CpG#4 [20250519_004541.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004541.]: c(0, 12.4934173132007, 24.2685417185317, 38.0817103888785, 48.5843148203973, 67.672236894341, 74.1549253766253, 82.8821786440368, 100)[20250519_004541.]: c(0, 0.00658268679929996, 0.731458281468299, 0.581710388878498, 1.4156851796027, 5.172236894341, 0.8450746233747, 4.6178213559632, 0)[20250519_004541.]: c(NA, 0.0526614943943997, 2.9258331258732, 1.551227703676, 2.83137035920539, 8.27557903094559, 1.1267661644996, 5.2775101211008, 0) [20250519_004541.]: Entered 'hyperbolic_regression'-Function [20250519_004541.]: 'hyperbolic_regression': minmax = FALSE [20250519_004541.]: Entered 'cubic_regression'-Function [20250519_004541.]: 'cubic_regression': minmax = FALSE [20250519_004541.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.41558718123367, 10.1649680545674, 23.9830821073418, 37.2773617400679, 50.8659382464167, 62.4342269461158, 76.3915257997575, 86.1597887396996, 100)c(2.41558718123367, 2.3350319454326, 1.0169178926582, 0.222638259932097, 0.865938246416697, 0.0657730538842003, 1.3915257997575, 1.34021126030041, 0)c(NA, 18.6802555634608, 4.0676715706328, 0.593702026485592, 1.73187649283339, 0.10523688621472, 1.85536773301, 1.53167001177189, 0) [20250519_004541.]: Logging df_agg: CpG#5 [20250519_004541.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004541.]: c(2.41558718123367, 10.1649680545674, 23.9830821073418, 37.2773617400679, 50.8659382464167, 62.4342269461158, 76.3915257997575, 86.1597887396996, 100)[20250519_004541.]: c(2.41558718123367, 2.3350319454326, 1.0169178926582, 0.222638259932097, 0.865938246416697, 0.0657730538842003, 1.3915257997575, 1.34021126030041, 0)[20250519_004541.]: c(NA, 18.6802555634608, 4.0676715706328, 0.593702026485592, 1.73187649283339, 0.10523688621472, 1.85536773301, 1.53167001177189, 0) [20250519_004541.]: Entered 'hyperbolic_regression'-Function [20250519_004541.]: 'hyperbolic_regression': minmax = FALSE [20250519_004542.]: Entered 'cubic_regression'-Function [20250519_004542.]: 'cubic_regression': minmax = FALSE [20250519_004542.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.138164161050506, 11.8635561171135, 26.510745006666, 35.3205072777727, 50.0570766444694, 64.9602942986125, 73.6689055886064, 87.1266085894673, 100)c(0.138164161050506, 0.6364438828865, 1.510745006666, 2.1794927222273, 0.0570766444693973, 2.4602942986125, 1.33109441139359, 0.373391410532705, 0)c(NA, 5.091551063092, 6.042980026664, 5.81198059260614, 0.114153288938795, 3.93647087777999, 1.77479254852479, 0.426733040608805, 0) [20250519_004542.]: Logging df_agg: CpG#6 [20250519_004542.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004542.]: c(0.138164161050506, 11.8635561171135, 26.510745006666, 35.3205072777727, 50.0570766444694, 64.9602942986125, 73.6689055886064, 87.1266085894673, 100)[20250519_004542.]: c(0.138164161050506, 0.6364438828865, 1.510745006666, 2.1794927222273, 0.0570766444693973, 2.4602942986125, 1.33109441139359, 0.373391410532705, 0)[20250519_004542.]: c(NA, 5.091551063092, 6.042980026664, 5.81198059260614, 0.114153288938795, 3.93647087777999, 1.77479254852479, 0.426733040608805, 0) [20250519_004542.]: Entered 'hyperbolic_regression'-Function [20250519_004542.]: 'hyperbolic_regression': minmax = FALSE [20250519_004542.]: Entered 'cubic_regression'-Function [20250519_004542.]: 'cubic_regression': minmax = FALSE [20250519_004542.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.1993294013604, 24.5951803850766, 37.8310364564335, 53.5588645818264, 65.9364860153449, 75.7361033620359, 79.4328192070151, 100)c(0, 2.3006705986396, 0.404819614923401, 0.331036456433502, 3.5588645818264, 3.4364860153449, 0.736103362035905, 8.0671807929849, 0)c(NA, 18.4053647891168, 1.6192784596936, 0.882763883822671, 7.1177291636528, 5.49837762455184, 0.981471149381207, 9.21963519198274, 0) [20250519_004542.]: Logging df_agg: CpG#7 [20250519_004542.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004542.]: c(0, 10.1993294013604, 24.5951803850766, 37.8310364564335, 53.5588645818264, 65.9364860153449, 75.7361033620359, 79.4328192070151, 100)[20250519_004542.]: c(0, 2.3006705986396, 0.404819614923401, 0.331036456433502, 3.5588645818264, 3.4364860153449, 0.736103362035905, 8.0671807929849, 0)[20250519_004542.]: c(NA, 18.4053647891168, 1.6192784596936, 0.882763883822671, 7.1177291636528, 5.49837762455184, 0.981471149381207, 9.21963519198274, 0) [20250519_004542.]: Entered 'hyperbolic_regression'-Function [20250519_004542.]: 'hyperbolic_regression': minmax = FALSE [20250519_004542.]: Entered 'cubic_regression'-Function [20250519_004542.]: 'cubic_regression': minmax = FALSE [20250519_004542.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.80068093389159, 9.27535066703723, 25.4762625870809, 34.0122083314201, 51.7842666342759, 64.6732321058682, 78.4326982875914, 81.3427235319597, 100)c(2.80068093389159, 3.22464933296277, 0.4762625870809, 3.4877916685799, 1.7842666342759, 2.17323210586819, 3.43269828759141, 6.15727646804029, 0)c(NA, 25.7971946637022, 1.9050503483236, 9.30077778287974, 3.5685332685518, 3.47717136938911, 4.57693105012188, 7.03688739204605, 0) [20250519_004542.]: Logging df_agg: CpG#8 [20250519_004542.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004542.]: c(2.80068093389159, 9.27535066703723, 25.4762625870809, 34.0122083314201, 51.7842666342759, 64.6732321058682, 78.4326982875914, 81.3427235319597, 100)[20250519_004542.]: c(2.80068093389159, 3.22464933296277, 0.4762625870809, 3.4877916685799, 1.7842666342759, 2.17323210586819, 3.43269828759141, 6.15727646804029, 0)[20250519_004542.]: c(NA, 25.7971946637022, 1.9050503483236, 9.30077778287974, 3.5685332685518, 3.47717136938911, 4.57693105012188, 7.03688739204605, 0) [20250519_004542.]: Entered 'hyperbolic_regression'-Function [20250519_004542.]: 'hyperbolic_regression': minmax = FALSE [20250519_004542.]: Entered 'cubic_regression'-Function [20250519_004542.]: 'cubic_regression': minmax = FALSE [20250519_004542.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.5082212970134, 26.9164566258777, 36.8334769505904, 52.0097879402751, 64.893051148082, 74.5671042786367, 84.5294949210319, 100)c(0, 1.9917787029866, 1.9164566258777, 0.666523049409598, 2.0097879402751, 2.39305114808199, 0.432895721363295, 2.9705050789681, 0)c(NA, 15.9342296238928, 7.6658265035108, 1.77739479842559, 4.01957588055021, 3.82888183693119, 0.57719429515106, 3.39486294739212, 0) [20250519_004542.]: Logging df_agg: CpG#9 [20250519_004542.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004542.]: c(0, 10.5082212970134, 26.9164566258777, 36.8334769505904, 52.0097879402751, 64.893051148082, 74.5671042786367, 84.5294949210319, 100)[20250519_004542.]: c(0, 1.9917787029866, 1.9164566258777, 0.666523049409598, 2.0097879402751, 2.39305114808199, 0.432895721363295, 2.9705050789681, 0)[20250519_004542.]: c(NA, 15.9342296238928, 7.6658265035108, 1.77739479842559, 4.01957588055021, 3.82888183693119, 0.57719429515106, 3.39486294739212, 0) [20250519_004542.]: Entered 'hyperbolic_regression'-Function [20250519_004542.]: 'hyperbolic_regression': minmax = FALSE [20250519_004543.]: Entered 'cubic_regression'-Function [20250519_004543.]: 'cubic_regression': minmax = FALSE [20250519_004543.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.290944901553815, 11.0412427310408, 25.4081501338029, 36.5243709582085, 50.7348808712519, 65.3135195123017, 75.5342699494589, 83.2411225127701, 100)c(0.290944901553815, 1.4587572689592, 0.408150133802899, 0.9756290417915, 0.734880871251903, 2.8135195123017, 0.534269949458903, 4.2588774872299, 0)c(NA, 11.6700581516736, 1.63260053521159, 2.60167744477733, 1.46976174250381, 4.50163121968271, 0.71235993261187, 4.86728855683417, 0) [20250519_004543.]: Logging df_agg: row_means [20250519_004543.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004543.]: c(0.290944901553815, 11.0412427310408, 25.4081501338029, 36.5243709582085, 50.7348808712519, 65.3135195123017, 75.5342699494589, 83.2411225127701, 100)[20250519_004543.]: c(0.290944901553815, 1.4587572689592, 0.408150133802899, 0.9756290417915, 0.734880871251903, 2.8135195123017, 0.534269949458903, 4.2588774872299, 0)[20250519_004543.]: c(NA, 11.6700581516736, 1.63260053521159, 2.60167744477733, 1.46976174250381, 4.50163121968271, 0.71235993261187, 4.86728855683417, 0) [20250519_004543.]: Entered 'hyperbolic_regression'-Function [20250519_004543.]: 'hyperbolic_regression': minmax = FALSE [20250519_004543.]: Entered 'cubic_regression'-Function [20250519_004543.]: 'cubic_regression': minmax = FALSE [20250519_004543.]: ### Starting with plotting ### [20250519_004544.]: Creating BiasCorrected (hyperbolic) plot No. 1 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG1_corrected_h.png [20250519_004544.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -657.85070009309 , a = 985.271206705952 , b = -657.85070009309 , d = 903.457967400565 [20250519_004544.]: # CpG-site: CpG#1 Cubic: Using a = 5.1847317144537e-06 , b = -0.00180593132520811 , c = 1.11404026275692 , d = -0.858597345049636 [20250519_004545.]: Creating BiasCorrected (hyperbolic) plot No. 2 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG2_corrected_h.png [20250519_004545.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = 3399.63077807263 , a = 30276.8974003894 , b = 3399.63077807263 , d = 30353.3579426379 [20250519_004545.]: # CpG-site: CpG#2 Cubic: Using a = -2.58097552031906e-05 , b = 0.00383753288450718 , c = 0.851607751869747 , d = 0.95712734045721 [20250519_004546.]: Creating BiasCorrected (hyperbolic) plot No. 3 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG3_corrected_h.png [20250519_004546.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = -945.763900328712 , a = 1333.61328542459 , b = -945.763900328712 , d = 1252.95190710086 [20250519_004546.]: # CpG-site: CpG#3 Cubic: Using a = -1.13948418252831e-05 , b = 0.000937174314708123 , c = 0.998631105115713 , d = -0.383346041194064 [20250519_004548.]: Creating BiasCorrected (hyperbolic) plot No. 4 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG4_corrected_h.png [20250519_004548.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = -754.050808709341 , a = 1637.26986382636 , b = -754.050808709341 , d = 1558.27566152171 [20250519_004548.]: # CpG-site: CpG#4 Cubic: Using a = -3.25777472656452e-06 , b = -0.000129598485618069 , c = 1.03077245158202 , d = -0.370342672245111 [20250519_004549.]: Creating BiasCorrected (hyperbolic) plot No. 5 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG5_corrected_h.png [20250519_004549.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = -1917.44470782279 , a = -5621.43051220384 , b = -1917.44470782279 , d = -5734.45004174505 [20250519_004549.]: # CpG-site: CpG#5 Cubic: Using a = -3.16782847952728e-05 , b = 0.00493527243554485 , c = 0.800194551229801 , d = 1.38653114075915 [20250519_004551.]: Creating BiasCorrected (hyperbolic) plot No. 6 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG6_corrected_h.png [20250519_004551.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = -1255.66539296107 , a = 13775.5132875201 , b = -1255.66539296107 , d = 13690.4025642689 [20250519_004551.]: # CpG-site: CpG#6 Cubic: Using a = -3.70201699726522e-06 , b = 0.000482210843999232 , c = 0.98529311552838 , d = 0.0293572853154747 [20250519_004552.]: Creating BiasCorrected (hyperbolic) plot No. 7 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG7_corrected_h.png [20250519_004552.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -1065.05219601462 , a = 722.085443879134 , b = -1065.05219601462 , d = 634.403611801521 [20250519_004552.]: # CpG-site: CpG#7 Cubic: Using a = -1.04101209223167e-06 , b = -0.00130151331776721 , c = 1.1239953084199 , d = -1.59858561306358 [20250519_004554.]: Creating BiasCorrected (hyperbolic) plot No. 8 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG8_corrected_h.png [20250519_004554.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = 68.5259802694693 , a = 4784.81913306904 , b = 68.5259802694693 , d = 4739.03101269703 [20250519_004554.]: # CpG-site: CpG#8 Cubic: Using a = -4.65730461761547e-05 , b = 0.00676525443932464 , c = 0.747405842988874 , d = 1.5233701793389 [20250519_004555.]: Creating BiasCorrected (hyperbolic) plot No. 9 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG9_corrected_h.png [20250519_004555.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -1022.70071430348 , a = 1273.73160369723 , b = -1022.70071430348 , d = 1181.86620562781 [20250519_004555.]: # CpG-site: CpG#9 Cubic: Using a = 4.24791805783875e-06 , b = -0.00144169825546529 , c = 1.09837792836035 , d = -0.978987254622136 [20250519_004557.]: Creating BiasCorrected (hyperbolic) plot No. 10 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_rowmeans_corrected_h.png [20250519_004557.]: # CpG-site: row_means Hyperbolic: Using bias_weight = -1166.73385465972 , a = 1740.64188131784 , b = -1166.73385465972 , d = 1653.86486365297 [20250519_004557.]: # CpG-site: row_means Cubic: Using a = -8.71187897501959e-06 , b = 0.000715556896368103 , c = 1.00248226010187 , d = -0.420549765740683 [20250519_004558.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG1_corrected_h.png [20250519_004600.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG2_corrected_h.png [20250519_004601.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG3_corrected_h.png [20250519_004602.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG4_corrected_h.png [20250519_004603.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG5_corrected_h.png [20250519_004605.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG6_corrected_h.png [20250519_004606.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG7_corrected_h.png [20250519_004607.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG8_corrected_h.png [20250519_004609.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG9_corrected_h.png [20250519_004610.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_rowmeans_corrected_h.png [20250519_004611.]: Entered 'solving_equations'-Function [20250519_004611.]: Solving cubic regression for CpG#1 Coefficients: -1.03617340067344Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004611.]: Samplename: 0 Root: 1.334 --> Root in between the borders! Added to results. Coefficients: -8.34150673400678Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004611.]: Samplename: 12.5 Root: 11.446 --> Root in between the borders! Added to results. Coefficients: -15.3881734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004611.]: Samplename: 25 Root: 22.228 --> Root in between the borders! Added to results. Coefficients: -24.2801734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004611.]: Samplename: 37.5 Root: 36.374 --> Root in between the borders! Added to results. Coefficients: -34.9006734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004611.]: Samplename: 50 Root: 52.044 --> Root in between the borders! Added to results. Coefficients: -46.3541734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004611.]: Samplename: 62.5 Root: 66.144 --> Root in between the borders! Added to results. Coefficients: -55.8931734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004611.]: Samplename: 75 Root: 75.864 --> Root in between the borders! Added to results. Coefficients: -63.0981734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004611.]: Samplename: 87.5 Root: 82.254 --> Root in between the borders! Added to results. Coefficients: -91.0461734006735Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004611.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.877 --> '100 < root < 110' --> substitute 100 [20250519_004611.]: Solving cubic regression for CpG#2 Coefficients: -0.283329966329966Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004611.]: Samplename: 0 Root: 0.549 --> Root in between the borders! Added to results. Coefficients: -6.33999663299663Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004611.]: Samplename: 12.5 Root: 11.533 --> Root in between the borders! Added to results. Coefficients: -15.93932996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004611.]: Samplename: 25 Root: 26.628 --> Root in between the borders! Added to results. Coefficients: -22.33732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004611.]: Samplename: 37.5 Root: 35.509 --> Root in between the borders! Added to results. Coefficients: -32.22832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004611.]: Samplename: 50 Root: 47.851 --> Root in between the borders! Added to results. Coefficients: -49.96332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004611.]: Samplename: 62.5 Root: 66.893 --> Root in between the borders! Added to results. Coefficients: -58.96582996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004611.]: Samplename: 75 Root: 75.431 --> Root in between the borders! Added to results. Coefficients: -68.8366632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004611.]: Samplename: 87.5 Root: 84.118 --> Root in between the borders! Added to results. Coefficients: -90.57732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004611.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.287 --> '100 < root < 110' --> substitute 100 [20250519_004611.]: Solving cubic regression for CpG#3 Coefficients: -0.90294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004611.]: Samplename: 0 Root: 1.441 --> Root in between the borders! Added to results. Coefficients: -6.57294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004611.]: Samplename: 12.5 Root: 10.568 --> Root in between the borders! Added to results. Coefficients: -15.4289478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004611.]: Samplename: 25 Root: 24.796 --> Root in between the borders! Added to results. Coefficients: -22.6129478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004611.]: Samplename: 37.5 Root: 35.952 --> Root in between the borders! Added to results. Coefficients: -32.7754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004611.]: Samplename: 50 Root: 50.684 --> Root in between the borders! Added to results. Coefficients: -43.8889478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004611.]: Samplename: 62.5 Root: 65.142 --> Root in between the borders! Added to results. Coefficients: -54.9754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004611.]: Samplename: 75 Root: 77.905 --> Root in between the borders! Added to results. Coefficients: -57.6562811447812Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004611.]: Samplename: 87.5 Root: 80.767 --> Root in between the borders! Added to results. Coefficients: -80.6649478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004611.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.38 --> '100 < root < 110' --> substitute 100 [20250519_004611.]: Solving cubic regression for CpG#4 Coefficients: -0.597449494949524Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004611.]: Samplename: 0 Root: 0.858 --> Root in between the borders! Added to results. Coefficients: -8.25278282828286Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004611.]: Samplename: 12.5 Root: 12.086 --> Root in between the borders! Added to results. Coefficients: -15.8034494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004611.]: Samplename: 25 Root: 23.316 --> Root in between the borders! Added to results. Coefficients: -25.5274494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004611.]: Samplename: 37.5 Root: 37.383 --> Root in between the borders! Added to results. Coefficients: -33.6369494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004611.]: Samplename: 50 Root: 48.353 --> Root in between the borders! Added to results. Coefficients: -50.2554494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004611.]: Samplename: 62.5 Root: 68.082 --> Root in between the borders! Added to results. Coefficients: -56.5394494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004611.]: Samplename: 75 Root: 74.615 --> Root in between the borders! Added to results. Coefficients: -65.5927828282829Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004611.]: Samplename: 87.5 Root: 83.254 --> Root in between the borders! Added to results. Coefficients: -88.3214494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004611.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.715 --> '100 < root < 110' --> substitute 100 [20250519_004611.]: Solving cubic regression for CpG#5 Coefficients: -0.623961279461278Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004611.]: Samplename: 0 Root: 1.458 --> Root in between the borders! Added to results. Coefficients: -4.76796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004611.]: Samplename: 12.5 Root: 10.347 --> Root in between the borders! Added to results. Coefficients: -12.7259612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004611.]: Samplename: 25 Root: 24.815 --> Root in between the borders! Added to results. Coefficients: -21.1599612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004611.]: Samplename: 37.5 Root: 37.902 --> Root in between the borders! Added to results. Coefficients: -30.6954612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004611.]: Samplename: 50 Root: 50.977 --> Root in between the borders! Added to results. Coefficients: -39.6579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004611.]: Samplename: 62.5 Root: 62.126 --> Root in between the borders! Added to results. Coefficients: -51.6829612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004611.]: Samplename: 75 Root: 75.852 --> Root in between the borders! Added to results. Coefficients: -61.0146279461279Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004611.]: Samplename: 87.5 Root: 85.767 --> Root in between the borders! Added to results. Coefficients: -76.0699612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004611.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.743 --> '100 < root < 110' --> substitute 100 [20250519_004611.]: Solving cubic regression for CpG#6 Coefficients: -0.196072390572403Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004611.]: Samplename: 0 Root: 0.349 --> Root in between the borders! Added to results. Coefficients: -6.73873905723907Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004611.]: Samplename: 12.5 Root: 11.718 --> Root in between the borders! Added to results. Coefficients: -15.8880723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004611.]: Samplename: 25 Root: 26.396 --> Root in between the borders! Added to results. Coefficients: -22.0000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004611.]: Samplename: 37.5 Root: 35.301 --> Root in between the borders! Added to results. Coefficients: -33.4445723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004611.]: Samplename: 50 Root: 50.134 --> Root in between the borders! Added to results. Coefficients: -46.9000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004611.]: Samplename: 62.5 Root: 64.993 --> Root in between the borders! Added to results. Coefficients: -55.8320723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004611.]: Samplename: 75 Root: 73.639 --> Root in between the borders! Added to results. Coefficients: -71.5454057239057Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004611.]: Samplename: 87.5 Root: 87.043 --> Root in between the borders! Added to results. Coefficients: -89.6560723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004611.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.329 --> '100 < root < 110' --> substitute 100 [20250519_004611.]: Solving cubic regression for CpG#7 Coefficients: -1.21495454545456Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004611.]: Samplename: 0 Root: 2.13 --> Root in between the borders! Added to results. Coefficients: -5.39562121212123Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004611.]: Samplename: 12.5 Root: 9.973 --> Root in between the borders! Added to results. Coefficients: -11.2649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004611.]: Samplename: 25 Root: 22.206 --> Root in between the borders! Added to results. Coefficients: -17.4509545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004611.]: Samplename: 37.5 Root: 35.814 --> Root in between the borders! Added to results. Coefficients: -26.0314545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004611.]: Samplename: 50 Root: 53.28 --> Root in between the borders! Added to results. Coefficients: -33.9649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004611.]: Samplename: 62.5 Root: 66.598 --> Root in between the borders! Added to results. Coefficients: -41.1689545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004611.]: Samplename: 75 Root: 76.575 --> Root in between the borders! Added to results. Coefficients: -44.1356212121212Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004611.]: Samplename: 87.5 Root: 80.219 --> Root in between the borders! Added to results. Coefficients: -67.2229545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004611.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.506 --> '100 < root < 110' --> substitute 100 [20250519_004611.]: Solving cubic regression for CpG#8 Coefficients: -1.09618518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004611.]: Samplename: 0 Root: 2.016 --> Root in between the borders! Added to results. Coefficients: -5.44685185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004611.]: Samplename: 12.5 Root: 9.458 --> Root in between the borders! Added to results. Coefficients: -16.9301851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004611.]: Samplename: 25 Root: 26.35 --> Root in between the borders! Added to results. Coefficients: -23.3501851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004611.]: Samplename: 37.5 Root: 34.728 --> Root in between the borders! Added to results. Coefficients: -37.6251851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004611.]: Samplename: 50 Root: 51.781 --> Root in between the borders! Added to results. Coefficients: -48.8261851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004611.]: Samplename: 62.5 Root: 64.192 --> Root in between the borders! Added to results. Coefficients: -61.6676851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004611.]: Samplename: 75 Root: 77.804 --> Root in between the borders! Added to results. Coefficients: -64.5101851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004611.]: Samplename: 87.5 Root: 80.758 --> Root in between the borders! Added to results. Coefficients: -86.0601851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004611.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.834 --> '100 < root < 110' --> substitute 100 [20250519_004611.]: Solving cubic regression for CpG#9 Coefficients: -0.989865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004611.]: Samplename: 0 Root: 1.475 --> Root in between the borders! Added to results. Coefficients: -6.39586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004611.]: Samplename: 12.5 Root: 10.12 --> Root in between the borders! Added to results. Coefficients: -14.7058653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004611.]: Samplename: 25 Root: 24.844 --> Root in between the borders! Added to results. Coefficients: -20.6238653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004611.]: Samplename: 37.5 Root: 35.327 --> Root in between the borders! Added to results. Coefficients: -31.3958653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004611.]: Samplename: 50 Root: 51.855 --> Root in between the borders! Added to results. Coefficients: -42.6858653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004611.]: Samplename: 62.5 Root: 65.265 --> Root in between the borders! Added to results. Coefficients: -52.9033653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004611.]: Samplename: 75 Root: 74.915 --> Root in between the borders! Added to results. Coefficients: -65.492531986532Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004611.]: Samplename: 87.5 Root: 84.67 --> Root in between the borders! Added to results. Coefficients: -92.9898653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004611.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.082 --> '100 < root < 110' --> substitute 100 [20250519_004611.]: Solving cubic regression for row_means Coefficients: -0.771215488215478Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_004611.]: Samplename: 0 Root: 1.287 --> Root in between the borders! Added to results. Coefficients: -6.47247474747474Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_004611.]: Samplename: 12.5 Root: 10.847 --> Root in between the borders! Added to results. Coefficients: -14.8972154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_004611.]: Samplename: 25 Root: 24.737 --> Root in between the borders! Added to results. Coefficients: -22.1492154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_004611.]: Samplename: 37.5 Root: 36.02 --> Root in between the borders! Added to results. Coefficients: -32.5259932659933Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_004611.]: Samplename: 50 Root: 50.639 --> Root in between the borders! Added to results. Coefficients: -44.7218821548821Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_004611.]: Samplename: 62.5 Root: 65.497 --> Root in between the borders! Added to results. Coefficients: -54.4032154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_004611.]: Samplename: 75 Root: 75.751 --> Root in between the borders! Added to results. Coefficients: -62.4313636363636Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_004611.]: Samplename: 87.5 Root: 83.403 --> Root in between the borders! Added to results. Coefficients: -84.7343265993266Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_004611.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.573 --> '100 < root < 110' --> substitute 100 [20250519_004611.]: ### Starting with regression calculations ### [20250519_004611.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_004612.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) [20250519_004612.]: Logging df_agg: CpG#1 [20250519_004612.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004612.]: c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)[20250519_004612.]: c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)[20250519_004612.]: c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) [20250519_004612.]: Entered 'hyperbolic_regression'-Function [20250519_004612.]: 'hyperbolic_regression': minmax = FALSE [20250519_004612.]: Entered 'cubic_regression'-Function [20250519_004612.]: 'cubic_regression': minmax = FALSE [20250519_004612.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) [20250519_004612.]: Logging df_agg: CpG#2 [20250519_004612.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004612.]: c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)[20250519_004612.]: c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)[20250519_004612.]: c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) [20250519_004612.]: Entered 'hyperbolic_regression'-Function [20250519_004612.]: 'hyperbolic_regression': minmax = FALSE [20250519_004612.]: Entered 'cubic_regression'-Function [20250519_004612.]: 'cubic_regression': minmax = FALSE [20250519_004612.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) [20250519_004612.]: Logging df_agg: CpG#3 [20250519_004612.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004612.]: c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)[20250519_004612.]: c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)[20250519_004612.]: c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) [20250519_004612.]: Entered 'hyperbolic_regression'-Function [20250519_004612.]: 'hyperbolic_regression': minmax = FALSE [20250519_004612.]: Entered 'cubic_regression'-Function [20250519_004612.]: 'cubic_regression': minmax = FALSE [20250519_004612.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) [20250519_004612.]: Logging df_agg: CpG#4 [20250519_004612.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004612.]: c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)[20250519_004612.]: c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)[20250519_004612.]: c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) [20250519_004612.]: Entered 'hyperbolic_regression'-Function [20250519_004612.]: 'hyperbolic_regression': minmax = FALSE [20250519_004613.]: Entered 'cubic_regression'-Function [20250519_004613.]: 'cubic_regression': minmax = FALSE [20250519_004613.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) [20250519_004613.]: Logging df_agg: CpG#5 [20250519_004613.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004613.]: c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)[20250519_004613.]: c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)[20250519_004613.]: c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) [20250519_004613.]: Entered 'hyperbolic_regression'-Function [20250519_004613.]: 'hyperbolic_regression': minmax = FALSE [20250519_004613.]: Entered 'cubic_regression'-Function [20250519_004613.]: 'cubic_regression': minmax = FALSE [20250519_004613.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) [20250519_004613.]: Logging df_agg: CpG#6 [20250519_004613.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004613.]: c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)[20250519_004613.]: c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)[20250519_004613.]: c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) [20250519_004613.]: Entered 'hyperbolic_regression'-Function [20250519_004613.]: 'hyperbolic_regression': minmax = FALSE [20250519_004613.]: Entered 'cubic_regression'-Function [20250519_004613.]: 'cubic_regression': minmax = FALSE [20250519_004613.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) [20250519_004613.]: Logging df_agg: CpG#7 [20250519_004613.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004613.]: c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)[20250519_004613.]: c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)[20250519_004613.]: c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) [20250519_004613.]: Entered 'hyperbolic_regression'-Function [20250519_004613.]: 'hyperbolic_regression': minmax = FALSE [20250519_004613.]: Entered 'cubic_regression'-Function [20250519_004613.]: 'cubic_regression': minmax = FALSE [20250519_004613.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) [20250519_004613.]: Logging df_agg: CpG#8 [20250519_004613.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004613.]: c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)[20250519_004613.]: c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)[20250519_004613.]: c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) [20250519_004613.]: Entered 'hyperbolic_regression'-Function [20250519_004613.]: 'hyperbolic_regression': minmax = FALSE [20250519_004614.]: Entered 'cubic_regression'-Function [20250519_004614.]: 'cubic_regression': minmax = FALSE [20250519_004614.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) [20250519_004614.]: Logging df_agg: CpG#9 [20250519_004614.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004614.]: c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)[20250519_004614.]: c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)[20250519_004614.]: c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) [20250519_004614.]: Entered 'hyperbolic_regression'-Function [20250519_004614.]: 'hyperbolic_regression': minmax = FALSE [20250519_004614.]: Entered 'cubic_regression'-Function [20250519_004614.]: 'cubic_regression': minmax = FALSE [20250519_004614.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) [20250519_004614.]: Logging df_agg: row_means [20250519_004614.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004614.]: c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)[20250519_004614.]: c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)[20250519_004614.]: c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) [20250519_004614.]: Entered 'hyperbolic_regression'-Function [20250519_004614.]: 'hyperbolic_regression': minmax = FALSE [20250519_004614.]: Entered 'cubic_regression'-Function [20250519_004614.]: 'cubic_regression': minmax = FALSE [20250519_004615.]: ### Starting with plotting ### [20250519_004615.]: Creating BiasCorrected (cubic) plot No. 1 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG1_corrected_c.png [20250519_004615.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -1164.78037112998 , a = 2039.49073397227 , b = -1164.78037112998 , d = 1955.827713817 [20250519_004615.]: # CpG-site: CpG#1 Cubic: Using a = -3.42821196240366e-05 , b = 0.00462173155013798 , c = 0.850326258865826 , d = 0.500073874211125 [20250519_004616.]: Creating BiasCorrected (cubic) plot No. 2 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG2_corrected_c.png [20250519_004616.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = -464.14479041434 , a = 4988.85430873436 , b = -464.14479041434 , d = 4925.32709283236 [20250519_004616.]: # CpG-site: CpG#2 Cubic: Using a = -1.40811796906314e-05 , b = 0.00190849431754485 , c = 0.933455870941473 , d = 0.363096210099674 [20250519_004618.]: Creating BiasCorrected (cubic) plot No. 3 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG3_corrected_c.png [20250519_004618.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = -745.616753604673 , a = 2026.5285182947 , b = -745.616753604673 , d = 1953.27811759735 [20250519_004618.]: # CpG-site: CpG#3 Cubic: Using a = -2.78835051857186e-05 , b = 0.00366863707889089 , c = 0.880665171220893 , d = 0.510779308833653 [20250519_004619.]: Creating BiasCorrected (cubic) plot No. 4 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG4_corrected_c.png [20250519_004619.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = -833.077868829112 , a = 2871.13688289992 , b = -833.077868829112 , d = 2796.8115417971 [20250519_004619.]: # CpG-site: CpG#4 Cubic: Using a = -2.30028865643e-05 , b = 0.00309017851940692 , c = 0.897073107777645 , d = 0.443455761841866 [20250519_004621.]: Creating BiasCorrected (cubic) plot No. 5 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG5_corrected_c.png [20250519_004621.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = 222.093251203934 , a = 12477.4109611091 , b = 222.093251203934 , d = 12432.0779293841 [20250519_004621.]: # CpG-site: CpG#5 Cubic: Using a = -1.28632696989134e-05 , b = 0.00184822285299014 , c = 0.931001046631176 , d = 0.437963095185599 [20250519_004622.]: Creating BiasCorrected (cubic) plot No. 6 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG6_corrected_c.png [20250519_004622.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = -700.460688613084 , a = 21299.059407053 , b = -700.460688613084 , d = 21234.4652853188 [20250519_004622.]: # CpG-site: CpG#6 Cubic: Using a = -5.838335151375e-06 , b = 0.000828447163218526 , c = 0.970047764475166 , d = 0.158074701412251 [20250519_004624.]: Creating BiasCorrected (cubic) plot No. 7 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG7_corrected_c.png [20250519_004624.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -1144.91191228206 , a = 1477.91098401837 , b = -1144.91191228206 , d = 1394.69940178972 [20250519_004624.]: # CpG-site: CpG#7 Cubic: Using a = -4.54220535614047e-05 , b = 0.00607993130565131 , c = 0.805411493452389 , d = 0.615125382790254 [20250519_004625.]: Creating BiasCorrected (cubic) plot No. 8 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG8_corrected_c.png [20250519_004625.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = -349.37367288786 , a = 2377.38756952122 , b = -349.37367288786 , d = 2315.21494778767 [20250519_004625.]: # CpG-site: CpG#8 Cubic: Using a = -2.64622306669639e-05 , b = 0.00353621593528015 , c = 0.877916844709074 , d = 0.698684606493051 [20250519_004626.]: Creating BiasCorrected (cubic) plot No. 9 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG9_corrected_c.png [20250519_004626.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -1536.91699417279 , a = 3745.17619112231 , b = -1536.91699417279 , d = 3656.26801968932 [20250519_004626.]: # CpG-site: CpG#9 Cubic: Using a = -2.70976130611725e-05 , b = 0.00378478247182342 , c = 0.871825263051793 , d = 0.455475740974113 [20250519_004628.]: Creating BiasCorrected (cubic) plot No. 10 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_rowmeans_corrected_c.png [20250519_004628.]: # CpG-site: row_means Hyperbolic: Using bias_weight = -820.643805549713 , a = 3198.46619445775 , b = -820.643805549713 , d = 3124.21083989374 [20250519_004628.]: # CpG-site: row_means Cubic: Using a = -2.34328362100009e-05 , b = 0.00319139073176266 , c = 0.891842168032129 , d = 0.493517689787135 [20250519_004630.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG1_corrected_c.png [20250519_004631.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG2_corrected_c.png [20250519_004632.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG3_corrected_c.png [20250519_004633.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG4_corrected_c.png [20250519_004635.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG5_corrected_c.png [20250519_004636.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG6_corrected_c.png [20250519_004637.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG7_corrected_c.png [20250519_004639.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG8_corrected_c.png [20250519_004640.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG9_corrected_c.png [20250519_004641.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_rowmeans_corrected_c.png [20250519_004642.]: Entered 'solving_equations'-Function [20250519_004642.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 79.867338305743 [20250519_004642.]: Samplename: Sample#1 Root: 79.867 --> Root in between the borders! Added to results. Hyperbolic solved: 29.7900144307253 [20250519_004642.]: Samplename: Sample#10 Root: 29.79 --> Root in between the borders! Added to results. Hyperbolic solved: 41.6525328583179 [20250519_004642.]: Samplename: Sample#2 Root: 41.653 --> Root in between the borders! Added to results. Hyperbolic solved: 57.4651978180159 [20250519_004642.]: Samplename: Sample#3 Root: 57.465 --> Root in between the borders! Added to results. Hyperbolic solved: 9.20072297150156 [20250519_004642.]: Samplename: Sample#4 Root: 9.201 --> Root in between the borders! Added to results. Hyperbolic solved: 21.8059605790798 [20250519_004642.]: Samplename: Sample#5 Root: 21.806 --> Root in between the borders! Added to results. Hyperbolic solved: 23.0837964625959 [20250519_004642.]: Samplename: Sample#6 Root: 23.084 --> Root in between the borders! Added to results. Hyperbolic solved: 45.5034148455809 [20250519_004642.]: Samplename: Sample#7 Root: 45.503 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6987854360424 [20250519_004642.]: Samplename: Sample#8 Root: 85.699 --> Root in between the borders! Added to results. Hyperbolic solved: -3.66510576608508 [20250519_004642.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -3.665 --> '-10 < root < 0' --> substitute 0 [20250519_004643.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 76.7227930253137 [20250519_004643.]: Samplename: Sample#1 Root: 76.723 --> Root in between the borders! Added to results. Hyperbolic solved: 30.9924274267338 [20250519_004643.]: Samplename: Sample#10 Root: 30.992 --> Root in between the borders! Added to results. Hyperbolic solved: 42.6810881616502 [20250519_004643.]: Samplename: Sample#2 Root: 42.681 --> Root in between the borders! Added to results. Hyperbolic solved: 58.93379071467 [20250519_004643.]: Samplename: Sample#3 Root: 58.934 --> Root in between the borders! Added to results. Hyperbolic solved: 4.98020463922415 [20250519_004643.]: Samplename: Sample#4 Root: 4.98 --> Root in between the borders! Added to results. Hyperbolic solved: 20.2480263316954 [20250519_004643.]: Samplename: Sample#5 Root: 20.248 --> Root in between the borders! Added to results. Hyperbolic solved: 17.8133085422138 [20250519_004643.]: Samplename: Sample#6 Root: 17.813 --> Root in between the borders! Added to results. Hyperbolic solved: 40.917674420917 [20250519_004643.]: Samplename: Sample#7 Root: 40.918 --> Root in between the borders! Added to results. Hyperbolic solved: 86.045071534744 [20250519_004643.]: Samplename: Sample#8 Root: 86.045 --> Root in between the borders! Added to results. Hyperbolic solved: 3.1157977862424 [20250519_004643.]: Samplename: Sample#9 Root: 3.116 --> Root in between the borders! Added to results. [20250519_004643.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 74.9349278251272 [20250519_004643.]: Samplename: Sample#1 Root: 74.935 --> Root in between the borders! Added to results. Hyperbolic solved: 27.6844390885298 [20250519_004643.]: Samplename: Sample#10 Root: 27.684 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8520223561161 [20250519_004643.]: Samplename: Sample#2 Root: 41.852 --> Root in between the borders! Added to results. Hyperbolic solved: 55.8325219766746 [20250519_004643.]: Samplename: Sample#3 Root: 55.833 --> Root in between the borders! Added to results. Hyperbolic solved: 8.03518759540723 [20250519_004643.]: Samplename: Sample#4 Root: 8.035 --> Root in between the borders! Added to results. Hyperbolic solved: 24.1066316660805 [20250519_004643.]: Samplename: Sample#5 Root: 24.107 --> Root in between the borders! Added to results. Hyperbolic solved: 26.2419826081677 [20250519_004643.]: Samplename: Sample#6 Root: 26.242 --> Root in between the borders! Added to results. Hyperbolic solved: 44.0944957314077 [20250519_004643.]: Samplename: Sample#7 Root: 44.094 --> Root in between the borders! Added to results. Hyperbolic solved: 85.8279384942248 [20250519_004643.]: Samplename: Sample#8 Root: 85.828 --> Root in between the borders! Added to results. Hyperbolic solved: -0.66649088715512 [20250519_004643.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.666 --> '-10 < root < 0' --> substitute 0 [20250519_004643.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 76.349525731503 [20250519_004643.]: Samplename: Sample#1 Root: 76.35 --> Root in between the borders! Added to results. Hyperbolic solved: 28.2568543207918 [20250519_004643.]: Samplename: Sample#10 Root: 28.257 --> Root in between the borders! Added to results. Hyperbolic solved: 43.408980967851 [20250519_004643.]: Samplename: Sample#2 Root: 43.409 --> Root in between the borders! Added to results. Hyperbolic solved: 58.5435202418955 [20250519_004643.]: Samplename: Sample#3 Root: 58.544 --> Root in between the borders! Added to results. Hyperbolic solved: 10.308707716736 [20250519_004643.]: Samplename: Sample#4 Root: 10.309 --> Root in between the borders! Added to results. Hyperbolic solved: 22.1830453155629 [20250519_004643.]: Samplename: Sample#5 Root: 22.183 --> Root in between the borders! Added to results. Hyperbolic solved: 27.133776120676 [20250519_004643.]: Samplename: Sample#6 Root: 27.134 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8321067699793 [20250519_004643.]: Samplename: Sample#7 Root: 41.832 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6890182703099 [20250519_004643.]: Samplename: Sample#8 Root: 85.689 --> Root in between the borders! Added to results. Hyperbolic solved: 2.42232661674155 [20250519_004643.]: Samplename: Sample#9 Root: 2.422 --> Root in between the borders! Added to results. [20250519_004643.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 72.8126911247073 [20250519_004643.]: Samplename: Sample#1 Root: 72.813 --> Root in between the borders! Added to results. Hyperbolic solved: 26.4206935777278 [20250519_004643.]: Samplename: Sample#10 Root: 26.421 --> Root in between the borders! Added to results. Hyperbolic solved: 44.2738761535648 [20250519_004643.]: Samplename: Sample#2 Root: 44.274 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5324382038438 [20250519_004643.]: Samplename: Sample#3 Root: 53.532 --> Root in between the borders! Added to results. Hyperbolic solved: 10.0373723654377 [20250519_004643.]: Samplename: Sample#4 Root: 10.037 --> Root in between the borders! Added to results. Hyperbolic solved: 22.8733116049306 [20250519_004643.]: Samplename: Sample#5 Root: 22.873 --> Root in between the borders! Added to results. Hyperbolic solved: 25.9417915879453 [20250519_004643.]: Samplename: Sample#6 Root: 25.942 --> Root in between the borders! Added to results. Hyperbolic solved: 42.7945071576182 [20250519_004643.]: Samplename: Sample#7 Root: 42.795 --> Root in between the borders! Added to results. Hyperbolic solved: 88.8741214991654 [20250519_004643.]: Samplename: Sample#8 Root: 88.874 --> Root in between the borders! Added to results. Hyperbolic solved: 2.34647622155857 [20250519_004643.]: Samplename: Sample#9 Root: 2.346 --> Root in between the borders! Added to results. [20250519_004643.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 79.278059253184 [20250519_004643.]: Samplename: Sample#1 Root: 79.278 --> Root in between the borders! Added to results. Hyperbolic solved: 30.2012459144106 [20250519_004643.]: Samplename: Sample#10 Root: 30.201 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8474392901329 [20250519_004643.]: Samplename: Sample#2 Root: 41.847 --> Root in between the borders! Added to results. Hyperbolic solved: 56.8423515999057 [20250519_004643.]: Samplename: Sample#3 Root: 56.842 --> Root in between the borders! Added to results. Hyperbolic solved: 8.87856073331453 [20250519_004643.]: Samplename: Sample#4 Root: 8.879 --> Root in between the borders! Added to results. Hyperbolic solved: 18.6901596391315 [20250519_004643.]: Samplename: Sample#5 Root: 18.69 --> Root in between the borders! Added to results. Hyperbolic solved: 29.9309263719602 [20250519_004643.]: Samplename: Sample#6 Root: 29.931 --> Root in between the borders! Added to results. Hyperbolic solved: 42.814855924799 [20250519_004643.]: Samplename: Sample#7 Root: 42.815 --> Root in between the borders! Added to results. Hyperbolic solved: 86.75018307028 [20250519_004643.]: Samplename: Sample#8 Root: 86.75 --> Root in between the borders! Added to results. Hyperbolic solved: 1.51516233882239 [20250519_004643.]: Samplename: Sample#9 Root: 1.515 --> Root in between the borders! Added to results. [20250519_004643.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 78.2565541874121 [20250519_004643.]: Samplename: Sample#1 Root: 78.257 --> Root in between the borders! Added to results. Hyperbolic solved: 25.488740177574 [20250519_004643.]: Samplename: Sample#10 Root: 25.489 --> Root in between the borders! Added to results. Hyperbolic solved: 47.3712173704701 [20250519_004643.]: Samplename: Sample#2 Root: 47.371 --> Root in between the borders! Added to results. Hyperbolic solved: 58.3142577916519 [20250519_004643.]: Samplename: Sample#3 Root: 58.314 --> Root in between the borders! Added to results. Hyperbolic solved: 11.7212348600366 [20250519_004643.]: Samplename: Sample#4 Root: 11.721 --> Root in between the borders! Added to results. Hyperbolic solved: 25.3797494985646 [20250519_004643.]: Samplename: Sample#5 Root: 25.38 --> Root in between the borders! Added to results. Hyperbolic solved: 29.4095117400265 [20250519_004643.]: Samplename: Sample#6 Root: 29.41 --> Root in between the borders! Added to results. Hyperbolic solved: 44.5754992753069 [20250519_004643.]: Samplename: Sample#7 Root: 44.575 --> Root in between the borders! Added to results. Hyperbolic solved: 85.9628719316752 [20250519_004643.]: Samplename: Sample#8 Root: 85.963 --> Root in between the borders! Added to results. Hyperbolic solved: -4.16453560967523 [20250519_004643.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -4.165 --> '-10 < root < 0' --> substitute 0 [20250519_004643.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 72.9696611539955 [20250519_004643.]: Samplename: Sample#1 Root: 72.97 --> Root in between the borders! Added to results. Hyperbolic solved: 27.8250961443825 [20250519_004643.]: Samplename: Sample#10 Root: 27.825 --> Root in between the borders! Added to results. Hyperbolic solved: 34.9068396054724 [20250519_004643.]: Samplename: Sample#2 Root: 34.907 --> Root in between the borders! Added to results. Hyperbolic solved: 59.163744843639 [20250519_004643.]: Samplename: Sample#3 Root: 59.164 --> Root in between the borders! Added to results. Hyperbolic solved: 9.64644348381889 [20250519_004643.]: Samplename: Sample#4 Root: 9.646 --> Root in between the borders! Added to results. Hyperbolic solved: 19.3522608313684 [20250519_004643.]: Samplename: Sample#5 Root: 19.352 --> Root in between the borders! Added to results. Hyperbolic solved: 38.5571445759356 [20250519_004643.]: Samplename: Sample#6 Root: 38.557 --> Root in between the borders! Added to results. Hyperbolic solved: 44.7759635921689 [20250519_004643.]: Samplename: Sample#7 Root: 44.776 --> Root in between the borders! Added to results. Hyperbolic solved: 84.8599672002532 [20250519_004643.]: Samplename: Sample#8 Root: 84.86 --> Root in between the borders! Added to results. Hyperbolic solved: -2.05045066787228 [20250519_004643.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.05 --> '-10 < root < 0' --> substitute 0 [20250519_004643.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 81.0416669543132 [20250519_004643.]: Samplename: Sample#1 Root: 81.042 --> Root in between the borders! Added to results. Hyperbolic solved: 26.6441665578561 [20250519_004643.]: Samplename: Sample#10 Root: 26.644 --> Root in between the borders! Added to results. Hyperbolic solved: 45.7046185029368 [20250519_004643.]: Samplename: Sample#2 Root: 45.705 --> Root in between the borders! Added to results. Hyperbolic solved: 56.9767969678006 [20250519_004643.]: Samplename: Sample#3 Root: 56.977 --> Root in between the borders! Added to results. Hyperbolic solved: 6.81231488238752 [20250519_004643.]: Samplename: Sample#4 Root: 6.812 --> Root in between the borders! Added to results. Hyperbolic solved: 22.928478377057 [20250519_004643.]: Samplename: Sample#5 Root: 22.928 --> Root in between the borders! Added to results. Hyperbolic solved: 38.8653192337298 [20250519_004643.]: Samplename: Sample#6 Root: 38.865 --> Root in between the borders! Added to results. Hyperbolic solved: 43.6855618249121 [20250519_004643.]: Samplename: Sample#7 Root: 43.686 --> Root in between the borders! Added to results. Hyperbolic solved: 88.0155362134065 [20250519_004643.]: Samplename: Sample#8 Root: 88.016 --> Root in between the borders! Added to results. Hyperbolic solved: -3.35609615796437 [20250519_004643.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -3.356 --> '-10 < root < 0' --> substitute 0 [20250519_004643.]: Solving hyperbolic regression for row_means Hyperbolic solved: 77.0692788889392 [20250519_004643.]: Samplename: Sample#1 Root: 77.069 --> Root in between the borders! Added to results. Hyperbolic solved: 28.3620037732087 [20250519_004643.]: Samplename: Sample#10 Root: 28.362 --> Root in between the borders! Added to results. Hyperbolic solved: 42.5026157763218 [20250519_004643.]: Samplename: Sample#2 Root: 42.503 --> Root in between the borders! Added to results. Hyperbolic solved: 57.2972029249363 [20250519_004643.]: Samplename: Sample#3 Root: 57.297 --> Root in between the borders! Added to results. Hyperbolic solved: 8.82704268325745 [20250519_004643.]: Samplename: Sample#4 Root: 8.827 --> Root in between the borders! Added to results. Hyperbolic solved: 21.8102595595999 [20250519_004643.]: Samplename: Sample#5 Root: 21.81 --> Root in between the borders! Added to results. Hyperbolic solved: 28.7228654114699 [20250519_004643.]: Samplename: Sample#6 Root: 28.723 --> Root in between the borders! Added to results. Hyperbolic solved: 43.4105084715017 [20250519_004643.]: Samplename: Sample#7 Root: 43.411 --> Root in between the borders! Added to results. Hyperbolic solved: 86.4143551575745 [20250519_004643.]: Samplename: Sample#8 Root: 86.414 --> Root in between the borders! Added to results. Hyperbolic solved: -0.237016010782264 [20250519_004643.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.237 --> '-10 < root < 0' --> substitute 0 [20250519_004643.]: on_start: using future::plan("sequential") [20250519_004643.]: Entered 'clean_dt'-Function [20250519_004643.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_004643.]: got experimental data [20250519_004643.]: Entered 'clean_dt'-Function [20250519_004643.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_004643.]: got calibration data [20250519_004643.]: ### Starting with regression calculations ### [20250519_004643.]: Entered 'regression_type1'-Function [20250519_004643.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_004643.]: Logging df_agg: CpG#1 [20250519_004643.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004643.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_004643.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_004643.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_004643.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_004643.]: Entered 'hyperbolic_regression'-Function [20250519_004643.]: 'hyperbolic_regression': minmax = FALSE [20250519_004643.]: Entered 'cubic_regression'-Function [20250519_004643.]: 'cubic_regression': minmax = FALSE [20250519_004643.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_004643.]: Logging df_agg: CpG#2 [20250519_004643.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004643.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_004643.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_004643.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_004643.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_004643.]: Entered 'hyperbolic_regression'-Function [20250519_004643.]: 'hyperbolic_regression': minmax = FALSE [20250519_004643.]: Entered 'cubic_regression'-Function [20250519_004643.]: 'cubic_regression': minmax = FALSE [20250519_004643.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_004643.]: Logging df_agg: CpG#3 [20250519_004643.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004643.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_004643.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_004643.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_004643.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_004643.]: Entered 'hyperbolic_regression'-Function [20250519_004643.]: 'hyperbolic_regression': minmax = FALSE [20250519_004643.]: Entered 'cubic_regression'-Function [20250519_004643.]: 'cubic_regression': minmax = FALSE [20250519_004643.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_004643.]: Logging df_agg: CpG#4 [20250519_004643.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004643.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_004643.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_004643.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_004643.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_004643.]: Entered 'hyperbolic_regression'-Function [20250519_004643.]: 'hyperbolic_regression': minmax = FALSE [20250519_004643.]: Entered 'cubic_regression'-Function [20250519_004643.]: 'cubic_regression': minmax = FALSE [20250519_004643.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_004643.]: Logging df_agg: CpG#5 [20250519_004643.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004643.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_004643.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_004643.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_004643.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_004643.]: Entered 'hyperbolic_regression'-Function [20250519_004643.]: 'hyperbolic_regression': minmax = FALSE [20250519_004643.]: Entered 'cubic_regression'-Function [20250519_004643.]: 'cubic_regression': minmax = FALSE [20250519_004643.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_004643.]: Logging df_agg: CpG#6 [20250519_004643.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004643.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_004643.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_004643.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_004643.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_004643.]: Entered 'hyperbolic_regression'-Function [20250519_004643.]: 'hyperbolic_regression': minmax = FALSE [20250519_004643.]: Entered 'cubic_regression'-Function [20250519_004643.]: 'cubic_regression': minmax = FALSE [20250519_004643.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_004643.]: Logging df_agg: CpG#7 [20250519_004643.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004643.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_004643.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_004643.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_004643.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_004643.]: Entered 'hyperbolic_regression'-Function [20250519_004643.]: 'hyperbolic_regression': minmax = FALSE [20250519_004643.]: Entered 'cubic_regression'-Function [20250519_004643.]: 'cubic_regression': minmax = FALSE [20250519_004643.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_004643.]: Logging df_agg: CpG#8 [20250519_004643.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004643.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_004643.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_004643.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_004643.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_004643.]: Entered 'hyperbolic_regression'-Function [20250519_004643.]: 'hyperbolic_regression': minmax = FALSE [20250519_004643.]: Entered 'cubic_regression'-Function [20250519_004644.]: 'cubic_regression': minmax = FALSE [20250519_004644.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_004644.]: Logging df_agg: CpG#9 [20250519_004644.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004644.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_004644.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_004644.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_004644.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_004644.]: Entered 'hyperbolic_regression'-Function [20250519_004644.]: 'hyperbolic_regression': minmax = FALSE [20250519_004644.]: Entered 'cubic_regression'-Function [20250519_004644.]: 'cubic_regression': minmax = FALSE [20250519_004644.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723122, 2.47760998435345)c(5.94911111111111, 0.84962962962963, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703704, 19.6995555555555, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_004644.]: Logging df_agg: row_means [20250519_004644.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004644.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_004644.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723122, 2.47760998435345)[20250519_004644.]: c(5.94911111111111, 0.84962962962963, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_004644.]: c(NA, 6.79703703703704, 19.6995555555555, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_004644.]: Entered 'hyperbolic_regression'-Function [20250519_004644.]: 'hyperbolic_regression': minmax = FALSE [20250519_004644.]: Entered 'cubic_regression'-Function [20250519_004644.]: 'cubic_regression': minmax = FALSE [20250519_004644.]: ### Starting with plotting ### [20250519_004645.]: Creating plot No. 1 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG1.png [20250519_004645.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -123249135.40483 , a = 49589952.0989949 , b = -123249135.40483 , d = 59280789.0121927 [20250519_004645.]: # CpG-site: CpG#1 Cubic: Using a = 6.53413423120091e-05 , b = -0.0055806968734969 , c = 0.784061853455188 , d = 1.93182659932656 [20250519_004646.]: Creating plot No. 2 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG2.png [20250519_004646.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = 167929575.171327 , a = 42299444.0962795 , b = 167929575.171327 , d = 47897274.2220611 [20250519_004646.]: # CpG-site: CpG#2 Cubic: Using a = 8.04237934904601e-06 , b = 0.00293158941798942 , c = 0.514821742825076 , d = 9.27667003367003 [20250519_004647.]: Creating plot No. 3 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG3.png [20250519_004647.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = 31004745.3756238 , a = 31050253.3779659 , b = 31004745.3756238 , d = 40665231.758814 [20250519_004647.]: # CpG-site: CpG#3 Cubic: Using a = 2.30555869809204e-05 , b = -0.000797009331409346 , c = 0.62783129228796 , d = 3.88705218855218 [20250519_004649.]: Creating plot No. 4 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG4.png [20250519_004649.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = 128022258.276155 , a = 42779497.5538086 , b = 128022258.276155 , d = 50999439.9227065 [20250519_004649.]: # CpG-site: CpG#4 Cubic: Using a = 3.2166356902357e-05 , b = -0.0015913142857143 , c = 0.697398364598366 , d = 6.17055050505048 [20250519_004650.]: Creating plot No. 5 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG5.png [20250519_004650.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = 29447513.6184781 , a = 41864399.8673718 , b = 29447513.6184781 , d = 55382346.8470806 [20250519_004650.]: # CpG-site: CpG#5 Cubic: Using a = -4.48459708193036e-06 , b = 0.00375371236171235 , c = 0.422446384479718 , d = 5.17203872053872 [20250519_004652.]: Creating plot No. 6 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG6.png [20250519_004652.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = 17150080.5960938 , a = 40932834.1078748 , b = 17150080.5960938 , d = 46723471.6686748 [20250519_004652.]: # CpG-site: CpG#6 Cubic: Using a = 2.38852884399552e-05 , b = 0.000918637037037021 , c = 0.561022132435467 , d = 6.1479276094276 [20250519_004653.]: Creating plot No. 7 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG7.png [20250519_004653.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -57204506.2862076 , a = 29554652.6579632 , b = -57204506.2862076 , d = 48548425.6483363 [20250519_004653.]: # CpG-site: CpG#7 Cubic: Using a = 4.98010505050505e-05 , b = -0.00436152150072151 , c = 0.579588917748918 , d = 1.72104545454544 [20250519_004655.]: Creating plot No. 8 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG8.png [20250519_004655.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = 263871870.982016 , a = 38159683.1948542 , b = 263871870.982016 , d = 45284547.6704654 [20250519_004655.]: # CpG-site: CpG#8 Cubic: Using a = -1.50060965207632e-05 , b = 0.00447978143338143 , c = 0.534897737694404 , d = 9.56981481481482 [20250519_004656.]: Creating plot No. 9 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG9.png [20250519_004656.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -224404013.660658 , a = 51672526.9851193 , b = -224404013.660658 , d = 60201205.3472543 [20250519_004656.]: # CpG-site: CpG#9 Cubic: Using a = 7.72300426487093e-05 , b = -0.00542281173641174 , c = 0.67899229597563 , d = 2.72413468013467 [20250519_004658.]: Creating plot No. 10 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_rowmeans.png [20250519_004658.]: # CpG-site: row_means Hyperbolic: Using bias_weight = 40333655.1288316 , a = 44937623.5310126 , b = 40333655.1288316 , d = 55678831.3245349 [20250519_004658.]: # CpG-site: row_means Cubic: Using a = 2.88923726150392e-05 , b = -0.0006299592752926 , c = 0.600117857944524 , d = 5.17789562289563 [20250519_004659.]: Entered 'solving_equations'-Function [20250519_004659.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 6.03337011705089 [20250519_004659.]: Samplename: 0 Root: 6.033 --> Root in between the borders! Added to results. Hyperbolic solved: 14.7663097441095 [20250519_004659.]: Samplename: 12.5 Root: 14.766 --> Root in between the borders! Added to results. Hyperbolic solved: 23.1900365713551 [20250519_004659.]: Samplename: 25 Root: 23.19 --> Root in between the borders! Added to results. Hyperbolic solved: 33.8197153352119 [20250519_004659.]: Samplename: 37.5 Root: 33.82 --> Root in between the borders! Added to results. Hyperbolic solved: 46.5156833662696 [20250519_004659.]: Samplename: 50 Root: 46.516 --> Root in between the borders! Added to results. Hyperbolic solved: 60.2074431300908 [20250519_004659.]: Samplename: 62.5 Root: 60.207 --> Root in between the borders! Added to results. Hyperbolic solved: 71.6105733239506 [20250519_004659.]: Samplename: 75 Root: 71.611 --> Root in between the borders! Added to results. Hyperbolic solved: 80.2235915426204 [20250519_004659.]: Samplename: 87.5 Root: 80.224 --> Root in between the borders! Added to results. Hyperbolic solved: 113.633279788745 [20250519_004659.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20250519_004659.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 6.85513576175294 [20250519_004659.]: Samplename: 0 Root: 6.855 --> Root in between the borders! Added to results. Hyperbolic solved: 13.7133339963707 [20250519_004659.]: Samplename: 12.5 Root: 13.713 --> Root in between the borders! Added to results. Hyperbolic solved: 24.5830349243156 [20250519_004659.]: Samplename: 25 Root: 24.583 --> Root in between the borders! Added to results. Hyperbolic solved: 31.8277435330554 [20250519_004659.]: Samplename: 37.5 Root: 31.828 --> Root in between the borders! Added to results. Hyperbolic solved: 43.0277185945539 [20250519_004659.]: Samplename: 50 Root: 43.028 --> Root in between the borders! Added to results. Hyperbolic solved: 63.1097818281222 [20250519_004659.]: Samplename: 62.5 Root: 63.11 --> Root in between the borders! Added to results. Hyperbolic solved: 73.3036858899805 [20250519_004659.]: Samplename: 75 Root: 73.304 --> Root in between the borders! Added to results. Hyperbolic solved: 84.4808447799272 [20250519_004659.]: Samplename: 87.5 Root: 84.481 --> Root in between the borders! Added to results. Hyperbolic solved: 109.0987325611 [20250519_004659.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 109.099 --> '100 < root < 110' --> substitute 100 [20250519_004659.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 5.27473119209009 [20250519_004659.]: Samplename: 0 Root: 5.275 --> Root in between the borders! Added to results. Hyperbolic solved: 12.7004989900866 [20250519_004659.]: Samplename: 12.5 Root: 12.7 --> Root in between the borders! Added to results. Hyperbolic solved: 24.2988465005425 [20250519_004659.]: Samplename: 25 Root: 24.299 --> Root in between the borders! Added to results. Hyperbolic solved: 33.7074476249853 [20250519_004659.]: Samplename: 37.5 Root: 33.707 --> Root in between the borders! Added to results. Hyperbolic solved: 47.0168800224423 [20250519_004659.]: Samplename: 50 Root: 47.017 --> Root in between the borders! Added to results. Hyperbolic solved: 61.5718102366584 [20250519_004659.]: Samplename: 62.5 Root: 61.572 --> Root in between the borders! Added to results. Hyperbolic solved: 76.091389954119 [20250519_004659.]: Samplename: 75 Root: 76.091 --> Root in between the borders! Added to results. Hyperbolic solved: 79.6023799465703 [20250519_004659.]: Samplename: 87.5 Root: 79.602 --> Root in between the borders! Added to results. Hyperbolic solved: 109.736019452694 [20250519_004659.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 109.736 --> '100 < root < 110' --> substitute 100 [20250519_004659.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 5.07584235183922 [20250519_004659.]: Samplename: 0 Root: 5.076 --> Root in between the borders! Added to results. Hyperbolic solved: 14.2021272601476 [20250519_004659.]: Samplename: 12.5 Root: 14.202 --> Root in between the borders! Added to results. Hyperbolic solved: 23.203637284743 [20250519_004659.]: Samplename: 25 Root: 23.204 --> Root in between the borders! Added to results. Hyperbolic solved: 34.7960863609446 [20250519_004659.]: Samplename: 37.5 Root: 34.796 --> Root in between the borders! Added to results. Hyperbolic solved: 44.4638162018573 [20250519_004659.]: Samplename: 50 Root: 44.464 --> Root in between the borders! Added to results. Hyperbolic solved: 64.2755507391393 [20250519_004659.]: Samplename: 62.5 Root: 64.276 --> Root in between the borders! Added to results. Hyperbolic solved: 71.7670214785718 [20250519_004659.]: Samplename: 75 Root: 71.767 --> Root in between the borders! Added to results. Hyperbolic solved: 82.5599568822802 [20250519_004659.]: Samplename: 87.5 Root: 82.56 --> Root in between the borders! Added to results. Hyperbolic solved: 109.655967198106 [20250519_004659.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 109.656 --> '100 < root < 110' --> substitute 100 [20250519_004659.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 6.96411772281142 [20250519_004659.]: Samplename: 0 Root: 6.964 --> Root in between the borders! Added to results. Hyperbolic solved: 12.4462106087166 [20250519_004659.]: Samplename: 12.5 Root: 12.446 --> Root in between the borders! Added to results. Hyperbolic solved: 22.9738427992134 [20250519_004659.]: Samplename: 25 Root: 22.974 --> Root in between the borders! Added to results. Hyperbolic solved: 34.1311793989123 [20250519_004659.]: Samplename: 37.5 Root: 34.131 --> Root in between the borders! Added to results. Hyperbolic solved: 46.7456955019114 [20250519_004659.]: Samplename: 50 Root: 46.746 --> Root in between the borders! Added to results. Hyperbolic solved: 58.6021949543013 [20250519_004659.]: Samplename: 62.5 Root: 58.602 --> Root in between the borders! Added to results. Hyperbolic solved: 74.5100864344263 [20250519_004659.]: Samplename: 75 Root: 74.51 --> Root in between the borders! Added to results. Hyperbolic solved: 86.8549692943716 [20250519_004659.]: Samplename: 87.5 Root: 86.855 --> Root in between the borders! Added to results. Hyperbolic solved: 106.771715286513 [20250519_004659.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 106.772 --> '100 < root < 110' --> substitute 100 [20250519_004659.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 6.82248549455073 [20250519_004659.]: Samplename: 0 Root: 6.822 --> Root in between the borders! Added to results. Hyperbolic solved: 14.2907258003629 [20250519_004659.]: Samplename: 12.5 Root: 14.291 --> Root in between the borders! Added to results. Hyperbolic solved: 24.7343949718638 [20250519_004659.]: Samplename: 25 Root: 24.734 --> Root in between the borders! Added to results. Hyperbolic solved: 31.7110486196861 [20250519_004659.]: Samplename: 37.5 Root: 31.711 --> Root in between the borders! Added to results. Hyperbolic solved: 44.7745870553137 [20250519_004659.]: Samplename: 50 Root: 44.775 --> Root in between the borders! Added to results. Hyperbolic solved: 60.1336283719223 [20250519_004659.]: Samplename: 62.5 Root: 60.134 --> Root in between the borders! Added to results. Hyperbolic solved: 70.3292384995337 [20250519_004659.]: Samplename: 75 Root: 70.329 --> Root in between the borders! Added to results. Hyperbolic solved: 88.2655480529845 [20250519_004659.]: Samplename: 87.5 Root: 88.266 --> Root in between the borders! Added to results. Hyperbolic solved: 108.938360440145 [20250519_004659.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 108.938 --> '100 < root < 110' --> substitute 100 [20250519_004659.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 6.75841824784972 [20250519_004659.]: Samplename: 0 Root: 6.758 --> Root in between the borders! Added to results. Hyperbolic solved: 13.6258602138932 [20250519_004659.]: Samplename: 12.5 Root: 13.626 --> Root in between the borders! Added to results. Hyperbolic solved: 23.2672219789518 [20250519_004659.]: Samplename: 25 Root: 23.267 --> Root in between the borders! Added to results. Hyperbolic solved: 33.4287658578254 [20250519_004659.]: Samplename: 37.5 Root: 33.429 --> Root in between the borders! Added to results. Hyperbolic solved: 47.5236851621158 [20250519_004659.]: Samplename: 50 Root: 47.524 --> Root in between the borders! Added to results. Hyperbolic solved: 60.5558050598499 [20250519_004659.]: Samplename: 62.5 Root: 60.556 --> Root in between the borders! Added to results. Hyperbolic solved: 72.3896034879297 [20250519_004659.]: Samplename: 75 Root: 72.39 --> Root in between the borders! Added to results. Hyperbolic solved: 77.2628610417933 [20250519_004659.]: Samplename: 87.5 Root: 77.263 --> Root in between the borders! Added to results. Hyperbolic solved: 115.187789384315 [20250519_004659.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20250519_004659.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 5.74253131510683 [20250519_004659.]: Samplename: 0 Root: 5.743 --> Root in between the borders! Added to results. Hyperbolic solved: 10.9055216254216 [20250519_004659.]: Samplename: 12.5 Root: 10.906 --> Root in between the borders! Added to results. Hyperbolic solved: 24.5329419436721 [20250519_004659.]: Samplename: 25 Root: 24.533 --> Root in between the borders! Added to results. Hyperbolic solved: 32.1516426249373 [20250519_004659.]: Samplename: 37.5 Root: 32.152 --> Root in between the borders! Added to results. Hyperbolic solved: 49.0919870496865 [20250519_004659.]: Samplename: 50 Root: 49.092 --> Root in between the borders! Added to results. Hyperbolic solved: 62.3843810658735 [20250519_004700.]: Samplename: 62.5 Root: 62.384 --> Root in between the borders! Added to results. Hyperbolic solved: 77.6235905386846 [20250519_004700.]: Samplename: 75 Root: 77.624 --> Root in between the borders! Added to results. Hyperbolic solved: 80.9968312605936 [20250519_004700.]: Samplename: 87.5 Root: 80.997 --> Root in between the borders! Added to results. Hyperbolic solved: 106.570581137258 [20250519_004700.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 106.571 --> '100 < root < 110' --> substitute 100 [20250519_004700.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 8.66981641228949 [20250519_004700.]: Samplename: 0 Root: 8.67 --> Root in between the borders! Added to results. Hyperbolic solved: 14.9680920996829 [20250519_004700.]: Samplename: 12.5 Root: 14.968 --> Root in between the borders! Added to results. Hyperbolic solved: 24.6496838227806 [20250519_004700.]: Samplename: 25 Root: 24.65 --> Root in between the borders! Added to results. Hyperbolic solved: 31.5444702961411 [20250519_004700.]: Samplename: 37.5 Root: 31.544 --> Root in between the borders! Added to results. Hyperbolic solved: 44.0944303975514 [20250519_004700.]: Samplename: 50 Root: 44.094 --> Root in between the borders! Added to results. Hyperbolic solved: 57.2478940106457 [20250519_004700.]: Samplename: 62.5 Root: 57.248 --> Root in between the borders! Added to results. Hyperbolic solved: 69.1518417879612 [20250519_004700.]: Samplename: 75 Root: 69.152 --> Root in between the borders! Added to results. Hyperbolic solved: 83.8189177560508 [20250519_004700.]: Samplename: 87.5 Root: 83.819 --> Root in between the borders! Added to results. Hyperbolic solved: 115.854857900091 [20250519_004700.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20250519_004700.]: Solving hyperbolic regression for row_means Hyperbolic solved: 6.47354965864506 [20250519_004700.]: Samplename: 0 Root: 6.474 --> Root in between the borders! Added to results. Hyperbolic solved: 13.5375540918194 [20250519_004700.]: Samplename: 12.5 Root: 13.538 --> Root in between the borders! Added to results. Hyperbolic solved: 23.976024251167 [20250519_004700.]: Samplename: 25 Root: 23.976 --> Root in between the borders! Added to results. Hyperbolic solved: 32.9614416466039 [20250519_004700.]: Samplename: 37.5 Root: 32.961 --> Root in between the borders! Added to results. Hyperbolic solved: 45.8185453601259 [20250519_004700.]: Samplename: 50 Root: 45.819 --> Root in between the borders! Added to results. Hyperbolic solved: 60.9295837234236 [20250519_004700.]: Samplename: 62.5 Root: 60.93 --> Root in between the borders! Added to results. Hyperbolic solved: 72.9250251350637 [20250519_004700.]: Samplename: 75 Root: 72.925 --> Root in between the borders! Added to results. Hyperbolic solved: 82.8721280175692 [20250519_004700.]: Samplename: 87.5 Root: 82.872 --> Root in between the borders! Added to results. Hyperbolic solved: 110.506149316569 [20250519_004700.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20250519_004700.]: ### Starting with regression calculations ### [20250519_004700.]: Entered 'regression_type1'-Function [20250519_004700.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)c(6.03337011705089, 14.7663097441095, 23.1900365713551, 33.8197153352119, 46.5156833662696, 60.2074431300908, 71.6105733239506, 80.2235915426204)c(6.03337011705089, 2.2663097441095, 1.8099634286449, 3.6802846647881, 3.4843166337304, 2.2925568699092, 3.3894266760494, 7.2764084573796)c(NA, 18.130477952876, 7.2398537145796, 9.81409243943492, 6.9686332674608, 3.66809099185472, 4.51923556806586, 8.3158953798624) [20250519_004700.]: Logging df_agg: CpG#1 [20250519_004700.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)[20250519_004700.]: c(6.03337011705089, 14.7663097441095, 23.1900365713551, 33.8197153352119, 46.5156833662696, 60.2074431300908, 71.6105733239506, 80.2235915426204)[20250519_004700.]: c(6.03337011705089, 2.2663097441095, 1.8099634286449, 3.6802846647881, 3.4843166337304, 2.2925568699092, 3.3894266760494, 7.2764084573796)[20250519_004700.]: c(NA, 18.130477952876, 7.2398537145796, 9.81409243943492, 6.9686332674608, 3.66809099185472, 4.51923556806586, 8.3158953798624) [20250519_004700.]: Entered 'hyperbolic_regression'-Function [20250519_004700.]: 'hyperbolic_regression': minmax = FALSE [20250519_004700.]: Entered 'cubic_regression'-Function [20250519_004700.]: 'cubic_regression': minmax = FALSE [20250519_004700.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.85513576175294, 13.7133339963707, 24.5830349243156, 31.8277435330554, 43.0277185945539, 63.1097818281222, 73.3036858899805, 84.4808447799272, 100)c(6.85513576175294, 1.2133339963707, 0.416965075684399, 5.6722564669446, 6.9722814054461, 0.609781828122202, 1.6963141100195, 3.0191552200728, 0)c(NA, 9.7066719709656, 1.66786030273759, 15.1260172451856, 13.9445628108922, 0.975650924995523, 2.26175214669267, 3.45046310865463, 0) [20250519_004700.]: Logging df_agg: CpG#2 [20250519_004700.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004700.]: c(6.85513576175294, 13.7133339963707, 24.5830349243156, 31.8277435330554, 43.0277185945539, 63.1097818281222, 73.3036858899805, 84.4808447799272, 100)[20250519_004700.]: c(6.85513576175294, 1.2133339963707, 0.416965075684399, 5.6722564669446, 6.9722814054461, 0.609781828122202, 1.6963141100195, 3.0191552200728, 0)[20250519_004700.]: c(NA, 9.7066719709656, 1.66786030273759, 15.1260172451856, 13.9445628108922, 0.975650924995523, 2.26175214669267, 3.45046310865463, 0) [20250519_004700.]: Entered 'hyperbolic_regression'-Function [20250519_004700.]: 'hyperbolic_regression': minmax = FALSE [20250519_004700.]: Entered 'cubic_regression'-Function [20250519_004700.]: 'cubic_regression': minmax = FALSE [20250519_004700.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.27473119209009, 12.7004989900866, 24.2988465005425, 33.7074476249853, 47.0168800224423, 61.5718102366584, 76.091389954119, 79.6023799465703, 100)c(5.27473119209009, 0.200498990086601, 0.701153499457501, 3.7925523750147, 2.9831199775577, 0.928189763341599, 1.09138995411899, 7.8976200534297, 0)c(NA, 1.60399192069281, 2.80461399783, 10.1134730000392, 5.9662399551154, 1.48510362134656, 1.45518660549199, 9.02585148963395, 0) [20250519_004700.]: Logging df_agg: CpG#3 [20250519_004700.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004700.]: c(5.27473119209009, 12.7004989900866, 24.2988465005425, 33.7074476249853, 47.0168800224423, 61.5718102366584, 76.091389954119, 79.6023799465703, 100)[20250519_004700.]: c(5.27473119209009, 0.200498990086601, 0.701153499457501, 3.7925523750147, 2.9831199775577, 0.928189763341599, 1.09138995411899, 7.8976200534297, 0)[20250519_004700.]: c(NA, 1.60399192069281, 2.80461399783, 10.1134730000392, 5.9662399551154, 1.48510362134656, 1.45518660549199, 9.02585148963395, 0) [20250519_004700.]: Entered 'hyperbolic_regression'-Function [20250519_004700.]: 'hyperbolic_regression': minmax = FALSE [20250519_004700.]: Entered 'cubic_regression'-Function [20250519_004700.]: 'cubic_regression': minmax = FALSE [20250519_004700.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.07584235183922, 14.2021272601476, 23.203637284743, 34.7960863609446, 44.4638162018573, 64.2755507391393, 71.7670214785718, 82.5599568822802, 100)c(5.07584235183922, 1.7021272601476, 1.796362715257, 2.7039136390554, 5.5361837981427, 1.77555073913931, 3.23297852142819, 4.9400431177198, 0)c(NA, 13.6170180811808, 7.185450861028, 7.2104363708144, 11.0723675962854, 2.84088118262289, 4.31063802857093, 5.64576356310834, 0) [20250519_004700.]: Logging df_agg: CpG#4 [20250519_004700.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004700.]: c(5.07584235183922, 14.2021272601476, 23.203637284743, 34.7960863609446, 44.4638162018573, 64.2755507391393, 71.7670214785718, 82.5599568822802, 100)[20250519_004700.]: c(5.07584235183922, 1.7021272601476, 1.796362715257, 2.7039136390554, 5.5361837981427, 1.77555073913931, 3.23297852142819, 4.9400431177198, 0)[20250519_004700.]: c(NA, 13.6170180811808, 7.185450861028, 7.2104363708144, 11.0723675962854, 2.84088118262289, 4.31063802857093, 5.64576356310834, 0) [20250519_004700.]: Entered 'hyperbolic_regression'-Function [20250519_004700.]: 'hyperbolic_regression': minmax = FALSE [20250519_004700.]: Entered 'cubic_regression'-Function [20250519_004700.]: 'cubic_regression': minmax = FALSE [20250519_004700.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.96411772281142, 12.4462106087166, 22.9738427992134, 34.1311793989123, 46.7456955019114, 58.6021949543013, 74.5100864344263, 86.8549692943716, 100)c(6.96411772281142, 0.0537893912834004, 2.0261572007866, 3.3688206010877, 3.2543044980886, 3.8978050456987, 0.489913565573701, 0.6450307056284, 0)c(NA, 0.430315130267203, 8.1046288031464, 8.98352160290053, 6.5086089961772, 6.23648807311793, 0.653218087431602, 0.7371779492896, 0) [20250519_004700.]: Logging df_agg: CpG#5 [20250519_004700.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004700.]: c(6.96411772281142, 12.4462106087166, 22.9738427992134, 34.1311793989123, 46.7456955019114, 58.6021949543013, 74.5100864344263, 86.8549692943716, 100)[20250519_004700.]: c(6.96411772281142, 0.0537893912834004, 2.0261572007866, 3.3688206010877, 3.2543044980886, 3.8978050456987, 0.489913565573701, 0.6450307056284, 0)[20250519_004700.]: c(NA, 0.430315130267203, 8.1046288031464, 8.98352160290053, 6.5086089961772, 6.23648807311793, 0.653218087431602, 0.7371779492896, 0) [20250519_004700.]: Entered 'hyperbolic_regression'-Function [20250519_004700.]: 'hyperbolic_regression': minmax = FALSE [20250519_004700.]: Entered 'cubic_regression'-Function [20250519_004700.]: 'cubic_regression': minmax = FALSE [20250519_004700.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.82248549455073, 14.2907258003629, 24.7343949718638, 31.7110486196861, 44.7745870553137, 60.1336283719223, 70.3292384995337, 88.2655480529845, 100)c(6.82248549455073, 1.7907258003629, 0.2656050281362, 5.7889513803139, 5.2254129446863, 2.3663716280777, 4.6707615004663, 0.765548052984499, 0)c(NA, 14.3258064029032, 1.0624201125448, 15.4372036808371, 10.4508258893726, 3.78619460492432, 6.22768200062173, 0.874912060553714, 0) [20250519_004700.]: Logging df_agg: CpG#6 [20250519_004700.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004700.]: c(6.82248549455073, 14.2907258003629, 24.7343949718638, 31.7110486196861, 44.7745870553137, 60.1336283719223, 70.3292384995337, 88.2655480529845, 100)[20250519_004700.]: c(6.82248549455073, 1.7907258003629, 0.2656050281362, 5.7889513803139, 5.2254129446863, 2.3663716280777, 4.6707615004663, 0.765548052984499, 0)[20250519_004700.]: c(NA, 14.3258064029032, 1.0624201125448, 15.4372036808371, 10.4508258893726, 3.78619460492432, 6.22768200062173, 0.874912060553714, 0) [20250519_004700.]: Entered 'hyperbolic_regression'-Function [20250519_004700.]: 'hyperbolic_regression': minmax = FALSE [20250519_004700.]: Entered 'cubic_regression'-Function [20250519_004700.]: 'cubic_regression': minmax = FALSE [20250519_004700.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)c(6.75841824784972, 13.6258602138932, 23.2672219789518, 33.4287658578254, 47.5236851621158, 60.5558050598499, 72.3896034879297, 77.2628610417933)c(6.75841824784972, 1.1258602138932, 1.7327780210482, 4.0712341421746, 2.4763148378842, 1.9441949401501, 2.6103965120703, 10.2371389582067)c(NA, 9.0068817111456, 6.9311120841928, 10.8566243791323, 4.95262967576841, 3.11071190424016, 3.48052868276039, 11.6995873808077) [20250519_004700.]: Logging df_agg: CpG#7 [20250519_004700.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)[20250519_004700.]: c(6.75841824784972, 13.6258602138932, 23.2672219789518, 33.4287658578254, 47.5236851621158, 60.5558050598499, 72.3896034879297, 77.2628610417933)[20250519_004700.]: c(6.75841824784972, 1.1258602138932, 1.7327780210482, 4.0712341421746, 2.4763148378842, 1.9441949401501, 2.6103965120703, 10.2371389582067)[20250519_004700.]: c(NA, 9.0068817111456, 6.9311120841928, 10.8566243791323, 4.95262967576841, 3.11071190424016, 3.48052868276039, 11.6995873808077) [20250519_004700.]: Entered 'hyperbolic_regression'-Function [20250519_004700.]: 'hyperbolic_regression': minmax = FALSE [20250519_004700.]: Entered 'cubic_regression'-Function [20250519_004700.]: 'cubic_regression': minmax = FALSE [20250519_004700.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.74253131510683, 10.9055216254216, 24.5329419436721, 32.1516426249373, 49.0919870496865, 62.3843810658735, 77.6235905386846, 80.9968312605936, 100)c(5.74253131510683, 1.5944783745784, 0.4670580563279, 5.3483573750627, 0.908012950313498, 0.115618934126502, 2.6235905386846, 6.50316873940641, 0)c(NA, 12.7558269966272, 1.8682322253116, 14.2622863335005, 1.816025900627, 0.184990294602403, 3.49812071824613, 7.43219284503589, 0) [20250519_004700.]: Logging df_agg: CpG#8 [20250519_004700.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004700.]: c(5.74253131510683, 10.9055216254216, 24.5329419436721, 32.1516426249373, 49.0919870496865, 62.3843810658735, 77.6235905386846, 80.9968312605936, 100)[20250519_004700.]: c(5.74253131510683, 1.5944783745784, 0.4670580563279, 5.3483573750627, 0.908012950313498, 0.115618934126502, 2.6235905386846, 6.50316873940641, 0)[20250519_004700.]: c(NA, 12.7558269966272, 1.8682322253116, 14.2622863335005, 1.816025900627, 0.184990294602403, 3.49812071824613, 7.43219284503589, 0) [20250519_004700.]: Entered 'hyperbolic_regression'-Function [20250519_004700.]: 'hyperbolic_regression': minmax = FALSE [20250519_004700.]: Entered 'cubic_regression'-Function [20250519_004700.]: 'cubic_regression': minmax = FALSE [20250519_004700.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)c(8.66981641228949, 14.9680920996829, 24.6496838227806, 31.5444702961411, 44.0944303975514, 57.2478940106457, 69.1518417879612, 83.8189177560508)c(8.66981641228949, 2.4680920996829, 0.350316177219401, 5.9555297038589, 5.9055696024486, 5.2521059893543, 5.8481582120388, 3.68108224394921)c(NA, 19.7447367974632, 1.4012647088776, 15.8814125436237, 11.8111392048972, 8.40336958296688, 7.7975442827184, 4.20695113594195) [20250519_004700.]: Logging df_agg: CpG#9 [20250519_004700.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)[20250519_004700.]: c(8.66981641228949, 14.9680920996829, 24.6496838227806, 31.5444702961411, 44.0944303975514, 57.2478940106457, 69.1518417879612, 83.8189177560508)[20250519_004700.]: c(8.66981641228949, 2.4680920996829, 0.350316177219401, 5.9555297038589, 5.9055696024486, 5.2521059893543, 5.8481582120388, 3.68108224394921)[20250519_004700.]: c(NA, 19.7447367974632, 1.4012647088776, 15.8814125436237, 11.8111392048972, 8.40336958296688, 7.7975442827184, 4.20695113594195) [20250519_004700.]: Entered 'hyperbolic_regression'-Function [20250519_004700.]: 'hyperbolic_regression': minmax = FALSE [20250519_004700.]: Entered 'cubic_regression'-Function [20250519_004700.]: 'cubic_regression': minmax = FALSE [20250519_004700.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)c(6.47354965864506, 13.5375540918194, 23.976024251167, 32.9614416466039, 45.8185453601259, 60.9295837234236, 72.9250251350637, 82.8721280175692)c(6.47354965864506, 1.0375540918194, 1.023975748833, 4.5385583533961, 4.1814546398741, 1.5704162765764, 2.07497486493629, 4.6278719824308)c(NA, 8.3004327345552, 4.095902995332, 12.1028222757229, 8.36290927974819, 2.51266604252224, 2.76663315324839, 5.28899655134949) [20250519_004700.]: Logging df_agg: row_means [20250519_004700.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)[20250519_004700.]: c(6.47354965864506, 13.5375540918194, 23.976024251167, 32.9614416466039, 45.8185453601259, 60.9295837234236, 72.9250251350637, 82.8721280175692)[20250519_004700.]: c(6.47354965864506, 1.0375540918194, 1.023975748833, 4.5385583533961, 4.1814546398741, 1.5704162765764, 2.07497486493629, 4.6278719824308)[20250519_004700.]: c(NA, 8.3004327345552, 4.095902995332, 12.1028222757229, 8.36290927974819, 2.51266604252224, 2.76663315324839, 5.28899655134949) [20250519_004700.]: Entered 'hyperbolic_regression'-Function [20250519_004700.]: 'hyperbolic_regression': minmax = FALSE [20250519_004700.]: Entered 'cubic_regression'-Function [20250519_004700.]: 'cubic_regression': minmax = FALSE [20250519_004701.]: ### Starting with plotting ### [20250519_004701.]: Creating BiasCorrected (hyperbolic) plot No. 1 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG1_corrected_h.png [20250519_004701.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = 170413234.729972 , a = 44157086.707734 , b = 170413234.729972 , d = 49999790.9017601 [20250519_004701.]: # CpG-site: CpG#1 Cubic: Using a = -8.02902858014277e-05 , b = 0.0119973831380665 , c = 0.410459267079284 , d = 6.65096545514267 [20250519_004702.]: Creating BiasCorrected (hyperbolic) plot No. 2 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG2_corrected_h.png [20250519_004702.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = 63519198.3583519 , a = 42702123.5062977 , b = 63519198.3583519 , d = 44879882.4102916 [20250519_004702.]: # CpG-site: CpG#2 Cubic: Using a = -4.57917988885898e-05 , b = 0.00978975326683099 , c = 0.400365566888902 , d = 7.17765330717532 [20250519_004704.]: Creating BiasCorrected (hyperbolic) plot No. 3 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG3_corrected_h.png [20250519_004704.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = 60249784.2121432 , a = 37717764.1245422 , b = 60249784.2121432 , d = 39783429.2823954 [20250519_004704.]: # CpG-site: CpG#3 Cubic: Using a = -2.8548625146914e-05 , b = 0.00587956303749884 , c = 0.626869802626483 , d = 4.78057876315863 [20250519_004705.]: Creating BiasCorrected (hyperbolic) plot No. 4 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG4_corrected_h.png [20250519_004705.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = 66787089.0359508 , a = 42175722.8129271 , b = 66787089.0359508 , d = 44465523.7860812 [20250519_004705.]: # CpG-site: CpG#4 Cubic: Using a = -1.99135996570308e-05 , b = 0.00496936895290719 , c = 0.637632604471187 , d = 5.04633670060351 [20250519_004707.]: Creating BiasCorrected (hyperbolic) plot No. 5 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG5_corrected_h.png [20250519_004707.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = 57501729.1236441 , a = 52618093.853152 , b = 57501729.1236441 , d = 54589535.3828564 [20250519_004707.]: # CpG-site: CpG#5 Cubic: Using a = -4.67907948499236e-05 , b = 0.00978122250124343 , c = 0.42296364991001 , d = 6.61748738798833 [20250519_004708.]: Creating BiasCorrected (hyperbolic) plot No. 6 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG6_corrected_h.png [20250519_004708.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = 62067256.5784193 , a = 42512767.1502548 , b = 62067256.5784193 , d = 44640742.1604705 [20250519_004708.]: # CpG-site: CpG#6 Cubic: Using a = -2.66665530276184e-05 , b = 0.00740474965950072 , c = 0.461019694351388 , d = 7.23067904029629 [20250519_004710.]: Creating BiasCorrected (hyperbolic) plot No. 7 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG7_corrected_h.png [20250519_004710.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = 162158036.04427 , a = 37148305.7394653 , b = 162158036.04427 , d = 42707972.2066412 [20250519_004710.]: # CpG-site: CpG#7 Cubic: Using a = -0.000133937721266082 , b = 0.0182624909642956 , c = 0.234528921247686 , d = 7.29090555727988 [20250519_004711.]: Creating BiasCorrected (hyperbolic) plot No. 8 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG8_corrected_h.png [20250519_004711.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = 54899408.7962443 , a = 51832403.6152701 , b = 54899408.7962443 , d = 53714624.7099072 [20250519_004711.]: # CpG-site: CpG#8 Cubic: Using a = -5.74524696829791e-05 , b = 0.00998862677056794 , c = 0.502915955578275 , d = 4.90625922077111 [20250519_004712.]: Creating BiasCorrected (hyperbolic) plot No. 9 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG9_corrected_h.png [20250519_004712.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = 170227220.836014 , a = 37110575.5394583 , b = 170227220.836014 , d = 42946902.7624502 [20250519_004712.]: # CpG-site: CpG#9 Cubic: Using a = -1.38503636743099e-05 , b = 0.00591895875024497 , c = 0.445742549227169 , d = 8.73330013353405 [20250519_004714.]: Creating BiasCorrected (hyperbolic) plot No. 10 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_rowmeans_corrected_h.png [20250519_004714.]: # CpG-site: row_means Hyperbolic: Using bias_weight = 131846991.813373 , a = 45678337.0783695 , b = 131846991.813373 , d = 50198768.4229237 [20250519_004714.]: # CpG-site: row_means Cubic: Using a = -7.67916401132796e-05 , b = 0.0124890194887903 , c = 0.369098504101218 , d = 6.83740913189534 [20250519_004715.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG1_corrected_h.png [20250519_004717.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG2_corrected_h.png [20250519_004718.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG3_corrected_h.png [20250519_004719.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG4_corrected_h.png [20250519_004720.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG5_corrected_h.png [20250519_004722.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG6_corrected_h.png [20250519_004723.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG7_corrected_h.png [20250519_004724.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG8_corrected_h.png [20250519_004726.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG9_corrected_h.png [20250519_004727.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_rowmeans_corrected_h.png [20250519_004728.]: Entered 'solving_equations'-Function [20250519_004728.]: Solving cubic regression for CpG#1 Coefficients: -1.03617340067344Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004728.]: Samplename: 0 Root: 1.334 --> Root in between the borders! Added to results. Coefficients: -8.34150673400678Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004728.]: Samplename: 12.5 Root: 11.446 --> Root in between the borders! Added to results. Coefficients: -15.3881734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004728.]: Samplename: 25 Root: 22.228 --> Root in between the borders! Added to results. Coefficients: -24.2801734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004728.]: Samplename: 37.5 Root: 36.374 --> Root in between the borders! Added to results. Coefficients: -34.9006734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004728.]: Samplename: 50 Root: 52.044 --> Root in between the borders! Added to results. Coefficients: -46.3541734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004728.]: Samplename: 62.5 Root: 66.144 --> Root in between the borders! Added to results. Coefficients: -55.8931734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004728.]: Samplename: 75 Root: 75.864 --> Root in between the borders! Added to results. Coefficients: -63.0981734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004728.]: Samplename: 87.5 Root: 82.254 --> Root in between the borders! Added to results. Coefficients: -91.0461734006735Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004728.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.877 --> '100 < root < 110' --> substitute 100 [20250519_004728.]: Solving cubic regression for CpG#2 Coefficients: -0.283329966329966Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004728.]: Samplename: 0 Root: 0.549 --> Root in between the borders! Added to results. Coefficients: -6.33999663299663Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004728.]: Samplename: 12.5 Root: 11.533 --> Root in between the borders! Added to results. Coefficients: -15.93932996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004728.]: Samplename: 25 Root: 26.628 --> Root in between the borders! Added to results. Coefficients: -22.33732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004728.]: Samplename: 37.5 Root: 35.509 --> Root in between the borders! Added to results. Coefficients: -32.22832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004728.]: Samplename: 50 Root: 47.851 --> Root in between the borders! Added to results. Coefficients: -49.96332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004728.]: Samplename: 62.5 Root: 66.893 --> Root in between the borders! Added to results. Coefficients: -58.96582996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004728.]: Samplename: 75 Root: 75.431 --> Root in between the borders! Added to results. Coefficients: -68.8366632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004728.]: Samplename: 87.5 Root: 84.118 --> Root in between the borders! Added to results. Coefficients: -90.57732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004728.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.287 --> '100 < root < 110' --> substitute 100 [20250519_004728.]: Solving cubic regression for CpG#3 Coefficients: -0.90294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004728.]: Samplename: 0 Root: 1.441 --> Root in between the borders! Added to results. Coefficients: -6.57294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004728.]: Samplename: 12.5 Root: 10.568 --> Root in between the borders! Added to results. Coefficients: -15.4289478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004728.]: Samplename: 25 Root: 24.796 --> Root in between the borders! Added to results. Coefficients: -22.6129478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004728.]: Samplename: 37.5 Root: 35.952 --> Root in between the borders! Added to results. Coefficients: -32.7754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004728.]: Samplename: 50 Root: 50.684 --> Root in between the borders! Added to results. Coefficients: -43.8889478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004728.]: Samplename: 62.5 Root: 65.142 --> Root in between the borders! Added to results. Coefficients: -54.9754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004728.]: Samplename: 75 Root: 77.905 --> Root in between the borders! Added to results. Coefficients: -57.6562811447812Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004728.]: Samplename: 87.5 Root: 80.767 --> Root in between the borders! Added to results. Coefficients: -80.6649478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004728.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.38 --> '100 < root < 110' --> substitute 100 [20250519_004728.]: Solving cubic regression for CpG#4 Coefficients: -0.597449494949524Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004728.]: Samplename: 0 Root: 0.858 --> Root in between the borders! Added to results. Coefficients: -8.25278282828286Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004728.]: Samplename: 12.5 Root: 12.086 --> Root in between the borders! Added to results. Coefficients: -15.8034494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004728.]: Samplename: 25 Root: 23.316 --> Root in between the borders! Added to results. Coefficients: -25.5274494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004728.]: Samplename: 37.5 Root: 37.383 --> Root in between the borders! Added to results. Coefficients: -33.6369494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004728.]: Samplename: 50 Root: 48.353 --> Root in between the borders! Added to results. Coefficients: -50.2554494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004728.]: Samplename: 62.5 Root: 68.082 --> Root in between the borders! Added to results. Coefficients: -56.5394494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004728.]: Samplename: 75 Root: 74.615 --> Root in between the borders! Added to results. Coefficients: -65.5927828282829Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004728.]: Samplename: 87.5 Root: 83.254 --> Root in between the borders! Added to results. Coefficients: -88.3214494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004728.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.715 --> '100 < root < 110' --> substitute 100 [20250519_004728.]: Solving cubic regression for CpG#5 Coefficients: -0.623961279461278Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004728.]: Samplename: 0 Root: 1.458 --> Root in between the borders! Added to results. Coefficients: -4.76796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004728.]: Samplename: 12.5 Root: 10.347 --> Root in between the borders! Added to results. Coefficients: -12.7259612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004728.]: Samplename: 25 Root: 24.815 --> Root in between the borders! Added to results. Coefficients: -21.1599612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004728.]: Samplename: 37.5 Root: 37.902 --> Root in between the borders! Added to results. Coefficients: -30.6954612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004728.]: Samplename: 50 Root: 50.977 --> Root in between the borders! Added to results. Coefficients: -39.6579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004728.]: Samplename: 62.5 Root: 62.126 --> Root in between the borders! Added to results. Coefficients: -51.6829612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004728.]: Samplename: 75 Root: 75.852 --> Root in between the borders! Added to results. Coefficients: -61.0146279461279Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004728.]: Samplename: 87.5 Root: 85.767 --> Root in between the borders! Added to results. Coefficients: -76.0699612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004728.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.743 --> '100 < root < 110' --> substitute 100 [20250519_004728.]: Solving cubic regression for CpG#6 Coefficients: -0.196072390572403Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004728.]: Samplename: 0 Root: 0.349 --> Root in between the borders! Added to results. Coefficients: -6.73873905723907Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004728.]: Samplename: 12.5 Root: 11.718 --> Root in between the borders! Added to results. Coefficients: -15.8880723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004728.]: Samplename: 25 Root: 26.396 --> Root in between the borders! Added to results. Coefficients: -22.0000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004728.]: Samplename: 37.5 Root: 35.301 --> Root in between the borders! Added to results. Coefficients: -33.4445723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004728.]: Samplename: 50 Root: 50.134 --> Root in between the borders! Added to results. Coefficients: -46.9000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004728.]: Samplename: 62.5 Root: 64.993 --> Root in between the borders! Added to results. Coefficients: -55.8320723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004728.]: Samplename: 75 Root: 73.639 --> Root in between the borders! Added to results. Coefficients: -71.5454057239057Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004728.]: Samplename: 87.5 Root: 87.043 --> Root in between the borders! Added to results. Coefficients: -89.6560723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004728.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.329 --> '100 < root < 110' --> substitute 100 [20250519_004728.]: Solving cubic regression for CpG#7 Coefficients: -1.21495454545456Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004728.]: Samplename: 0 Root: 2.13 --> Root in between the borders! Added to results. Coefficients: -5.39562121212123Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004728.]: Samplename: 12.5 Root: 9.973 --> Root in between the borders! Added to results. Coefficients: -11.2649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004728.]: Samplename: 25 Root: 22.206 --> Root in between the borders! Added to results. Coefficients: -17.4509545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004728.]: Samplename: 37.5 Root: 35.814 --> Root in between the borders! Added to results. Coefficients: -26.0314545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004728.]: Samplename: 50 Root: 53.28 --> Root in between the borders! Added to results. Coefficients: -33.9649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004728.]: Samplename: 62.5 Root: 66.598 --> Root in between the borders! Added to results. Coefficients: -41.1689545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004728.]: Samplename: 75 Root: 76.575 --> Root in between the borders! Added to results. Coefficients: -44.1356212121212Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004728.]: Samplename: 87.5 Root: 80.219 --> Root in between the borders! Added to results. Coefficients: -67.2229545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004728.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.506 --> '100 < root < 110' --> substitute 100 [20250519_004728.]: Solving cubic regression for CpG#8 Coefficients: -1.09618518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004728.]: Samplename: 0 Root: 2.016 --> Root in between the borders! Added to results. Coefficients: -5.44685185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004728.]: Samplename: 12.5 Root: 9.458 --> Root in between the borders! Added to results. Coefficients: -16.9301851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004728.]: Samplename: 25 Root: 26.35 --> Root in between the borders! Added to results. Coefficients: -23.3501851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004728.]: Samplename: 37.5 Root: 34.728 --> Root in between the borders! Added to results. Coefficients: -37.6251851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004728.]: Samplename: 50 Root: 51.781 --> Root in between the borders! Added to results. Coefficients: -48.8261851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004728.]: Samplename: 62.5 Root: 64.192 --> Root in between the borders! Added to results. Coefficients: -61.6676851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004728.]: Samplename: 75 Root: 77.804 --> Root in between the borders! Added to results. Coefficients: -64.5101851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004728.]: Samplename: 87.5 Root: 80.758 --> Root in between the borders! Added to results. Coefficients: -86.0601851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004728.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.834 --> '100 < root < 110' --> substitute 100 [20250519_004728.]: Solving cubic regression for CpG#9 Coefficients: -0.989865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004728.]: Samplename: 0 Root: 1.475 --> Root in between the borders! Added to results. Coefficients: -6.39586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004728.]: Samplename: 12.5 Root: 10.12 --> Root in between the borders! Added to results. Coefficients: -14.7058653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004728.]: Samplename: 25 Root: 24.844 --> Root in between the borders! Added to results. Coefficients: -20.6238653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004728.]: Samplename: 37.5 Root: 35.327 --> Root in between the borders! Added to results. Coefficients: -31.3958653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004728.]: Samplename: 50 Root: 51.855 --> Root in between the borders! Added to results. Coefficients: -42.6858653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004728.]: Samplename: 62.5 Root: 65.265 --> Root in between the borders! Added to results. Coefficients: -52.9033653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004728.]: Samplename: 75 Root: 74.915 --> Root in between the borders! Added to results. Coefficients: -65.492531986532Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004728.]: Samplename: 87.5 Root: 84.67 --> Root in between the borders! Added to results. Coefficients: -92.9898653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004728.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.082 --> '100 < root < 110' --> substitute 100 [20250519_004728.]: Solving cubic regression for row_means Coefficients: -0.771215488215478Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_004728.]: Samplename: 0 Root: 1.287 --> Root in between the borders! Added to results. Coefficients: -6.47247474747474Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_004728.]: Samplename: 12.5 Root: 10.847 --> Root in between the borders! Added to results. Coefficients: -14.8972154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_004728.]: Samplename: 25 Root: 24.737 --> Root in between the borders! Added to results. Coefficients: -22.1492154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_004728.]: Samplename: 37.5 Root: 36.02 --> Root in between the borders! Added to results. Coefficients: -32.5259932659933Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_004728.]: Samplename: 50 Root: 50.639 --> Root in between the borders! Added to results. Coefficients: -44.7218821548821Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_004728.]: Samplename: 62.5 Root: 65.497 --> Root in between the borders! Added to results. Coefficients: -54.4032154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_004728.]: Samplename: 75 Root: 75.751 --> Root in between the borders! Added to results. Coefficients: -62.4313636363636Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_004728.]: Samplename: 87.5 Root: 83.403 --> Root in between the borders! Added to results. Coefficients: -84.7343265993266Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_004728.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.573 --> '100 < root < 110' --> substitute 100 [20250519_004728.]: ### Starting with regression calculations ### [20250519_004728.]: Entered 'regression_type1'-Function [20250519_004729.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) [20250519_004729.]: Logging df_agg: CpG#1 [20250519_004729.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004729.]: c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)[20250519_004729.]: c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)[20250519_004729.]: c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) [20250519_004729.]: Entered 'hyperbolic_regression'-Function [20250519_004729.]: 'hyperbolic_regression': minmax = FALSE [20250519_004729.]: Entered 'cubic_regression'-Function [20250519_004729.]: 'cubic_regression': minmax = FALSE [20250519_004729.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) [20250519_004729.]: Logging df_agg: CpG#2 [20250519_004729.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004729.]: c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)[20250519_004729.]: c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)[20250519_004729.]: c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) [20250519_004729.]: Entered 'hyperbolic_regression'-Function [20250519_004729.]: 'hyperbolic_regression': minmax = FALSE [20250519_004729.]: Entered 'cubic_regression'-Function [20250519_004729.]: 'cubic_regression': minmax = FALSE [20250519_004729.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) [20250519_004729.]: Logging df_agg: CpG#3 [20250519_004729.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004729.]: c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)[20250519_004729.]: c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)[20250519_004729.]: c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) [20250519_004729.]: Entered 'hyperbolic_regression'-Function [20250519_004729.]: 'hyperbolic_regression': minmax = FALSE [20250519_004729.]: Entered 'cubic_regression'-Function [20250519_004729.]: 'cubic_regression': minmax = FALSE [20250519_004729.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) [20250519_004729.]: Logging df_agg: CpG#4 [20250519_004729.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004729.]: c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)[20250519_004729.]: c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)[20250519_004729.]: c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) [20250519_004729.]: Entered 'hyperbolic_regression'-Function [20250519_004729.]: 'hyperbolic_regression': minmax = FALSE [20250519_004729.]: Entered 'cubic_regression'-Function [20250519_004729.]: 'cubic_regression': minmax = FALSE [20250519_004729.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) [20250519_004729.]: Logging df_agg: CpG#5 [20250519_004729.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004729.]: c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)[20250519_004729.]: c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)[20250519_004729.]: c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) [20250519_004729.]: Entered 'hyperbolic_regression'-Function [20250519_004729.]: 'hyperbolic_regression': minmax = FALSE [20250519_004729.]: Entered 'cubic_regression'-Function [20250519_004729.]: 'cubic_regression': minmax = FALSE [20250519_004729.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) [20250519_004729.]: Logging df_agg: CpG#6 [20250519_004729.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004729.]: c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)[20250519_004729.]: c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)[20250519_004729.]: c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) [20250519_004729.]: Entered 'hyperbolic_regression'-Function [20250519_004729.]: 'hyperbolic_regression': minmax = FALSE [20250519_004729.]: Entered 'cubic_regression'-Function [20250519_004729.]: 'cubic_regression': minmax = FALSE [20250519_004729.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) [20250519_004729.]: Logging df_agg: CpG#7 [20250519_004729.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004729.]: c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)[20250519_004729.]: c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)[20250519_004729.]: c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) [20250519_004729.]: Entered 'hyperbolic_regression'-Function [20250519_004729.]: 'hyperbolic_regression': minmax = FALSE [20250519_004729.]: Entered 'cubic_regression'-Function [20250519_004729.]: 'cubic_regression': minmax = FALSE [20250519_004729.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) [20250519_004729.]: Logging df_agg: CpG#8 [20250519_004729.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004729.]: c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)[20250519_004729.]: c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)[20250519_004729.]: c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) [20250519_004729.]: Entered 'hyperbolic_regression'-Function [20250519_004729.]: 'hyperbolic_regression': minmax = FALSE [20250519_004729.]: Entered 'cubic_regression'-Function [20250519_004729.]: 'cubic_regression': minmax = FALSE [20250519_004729.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) [20250519_004729.]: Logging df_agg: CpG#9 [20250519_004729.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004729.]: c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)[20250519_004729.]: c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)[20250519_004729.]: c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) [20250519_004729.]: Entered 'hyperbolic_regression'-Function [20250519_004729.]: 'hyperbolic_regression': minmax = FALSE [20250519_004729.]: Entered 'cubic_regression'-Function [20250519_004729.]: 'cubic_regression': minmax = FALSE [20250519_004729.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) [20250519_004729.]: Logging df_agg: row_means [20250519_004729.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004729.]: c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)[20250519_004729.]: c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)[20250519_004729.]: c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) [20250519_004729.]: Entered 'hyperbolic_regression'-Function [20250519_004729.]: 'hyperbolic_regression': minmax = FALSE [20250519_004729.]: Entered 'cubic_regression'-Function [20250519_004729.]: 'cubic_regression': minmax = FALSE [20250519_004729.]: ### Starting with plotting ### [20250519_004730.]: Creating BiasCorrected (cubic) plot No. 1 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG1_corrected_c.png [20250519_004730.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -1164.7742156272 , a = 2039.5018241568 , b = -1164.7742156272 , d = 1955.83895682798 [20250519_004730.]: # CpG-site: CpG#1 Cubic: Using a = -3.42821196240366e-05 , b = 0.00462173155013798 , c = 0.850326258865826 , d = 0.500073874211125 [20250519_004731.]: Creating BiasCorrected (cubic) plot No. 2 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG2_corrected_c.png [20250519_004731.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = -464.155645361511 , a = 4988.81163391504 , b = -464.155645361511 , d = 4925.28408403839 [20250519_004731.]: # CpG-site: CpG#2 Cubic: Using a = -1.40811796906314e-05 , b = 0.00190849431754485 , c = 0.933455870941473 , d = 0.363096210099674 [20250519_004733.]: Creating BiasCorrected (cubic) plot No. 3 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG3_corrected_c.png [20250519_004733.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = -745.602352161431 , a = 2026.54103071808 , b = -745.602352161431 , d = 1953.2909386646 [20250519_004733.]: # CpG-site: CpG#3 Cubic: Using a = -2.78835051857186e-05 , b = 0.00366863707889089 , c = 0.880665171220893 , d = 0.510779308833653 [20250519_004734.]: Creating BiasCorrected (cubic) plot No. 4 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG4_corrected_c.png [20250519_004734.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = -833.06710731958 , a = 2871.12609903594 , b = -833.06710731958 , d = 2796.80085807845 [20250519_004734.]: # CpG-site: CpG#4 Cubic: Using a = -2.30028865643e-05 , b = 0.00309017851940692 , c = 0.897073107777645 , d = 0.443455761841866 [20250519_004735.]: Creating BiasCorrected (cubic) plot No. 5 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG5_corrected_c.png [20250519_004735.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = 221.879400146024 , a = 12476.5710927987 , b = 221.879400146024 , d = 12431.2329555778 [20250519_004736.]: # CpG-site: CpG#5 Cubic: Using a = -1.28632696989134e-05 , b = 0.00184822285299014 , c = 0.931001046631176 , d = 0.437963095185599 [20250519_004737.]: Creating BiasCorrected (cubic) plot No. 6 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG6_corrected_c.png [20250519_004737.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = -700.537394936109 , a = 21298.052707184 , b = -700.537394936109 , d = 21233.4563350159 [20250519_004737.]: # CpG-site: CpG#6 Cubic: Using a = -5.838335151375e-06 , b = 0.000828447163218526 , c = 0.970047764475166 , d = 0.158074701412251 [20250519_004738.]: Creating BiasCorrected (cubic) plot No. 7 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG7_corrected_c.png [20250519_004738.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -1144.91075475684 , a = 1477.92088633595 , b = -1144.91075475684 , d = 1394.70940380214 [20250519_004738.]: # CpG-site: CpG#7 Cubic: Using a = -4.54220535614047e-05 , b = 0.00607993130565131 , c = 0.805411493452389 , d = 0.615125382790254 [20250519_004740.]: Creating BiasCorrected (cubic) plot No. 8 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG8_corrected_c.png [20250519_004740.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = -349.322022819744 , a = 2377.42555416743 , b = -349.322022819744 , d = 2315.25402084122 [20250519_004740.]: # CpG-site: CpG#8 Cubic: Using a = -2.64622306669639e-05 , b = 0.00353621593528015 , c = 0.877916844709074 , d = 0.698684606493051 [20250519_004741.]: Creating BiasCorrected (cubic) plot No. 9 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG9_corrected_c.png [20250519_004741.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -1536.91807795025 , a = 3745.12601816225 , b = -1536.91807795025 , d = 3656.21766932252 [20250519_004741.]: # CpG-site: CpG#9 Cubic: Using a = -2.70976130611725e-05 , b = 0.00378478247182342 , c = 0.871825263051793 , d = 0.455475740974113 [20250519_004743.]: Creating BiasCorrected (cubic) plot No. 10 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_rowmeans_corrected_c.png [20250519_004743.]: # CpG-site: row_means Hyperbolic: Using bias_weight = -820.655299479511 , a = 3198.47118291101 , b = -820.655299479511 , d = 3124.21569118411 [20250519_004743.]: # CpG-site: row_means Cubic: Using a = -2.34328362100009e-05 , b = 0.00319139073176266 , c = 0.891842168032129 , d = 0.493517689787135 [20250519_004744.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG1_corrected_c.png [20250519_004746.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG2_corrected_c.png [20250519_004747.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG3_corrected_c.png [20250519_004748.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG4_corrected_c.png [20250519_004749.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG5_corrected_c.png [20250519_004751.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG6_corrected_c.png [20250519_004752.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG7_corrected_c.png [20250519_004753.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG8_corrected_c.png [20250519_004755.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG9_corrected_c.png [20250519_004756.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_rowmeans_corrected_c.png [20250519_004757.]: Entered 'solving_equations'-Function [20250519_004757.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 77.9602582684049 [20250519_004757.]: Samplename: Sample#1 Root: 77.96 --> Root in between the borders! Added to results. Hyperbolic solved: 27.1420961794087 [20250519_004757.]: Samplename: Sample#10 Root: 27.142 --> Root in between the borders! Added to results. Hyperbolic solved: 36.7640366116659 [20250519_004757.]: Samplename: Sample#2 Root: 36.764 --> Root in between the borders! Added to results. Hyperbolic solved: 51.6231208412737 [20250519_004757.]: Samplename: Sample#3 Root: 51.623 --> Root in between the borders! Added to results. Hyperbolic solved: 12.873561614208 [20250519_004757.]: Samplename: Sample#4 Root: 12.874 --> Root in between the borders! Added to results. Hyperbolic solved: 21.2773642339947 [20250519_004757.]: Samplename: Sample#5 Root: 21.277 --> Root in between the borders! Added to results. Hyperbolic solved: 22.1858835788516 [20250519_004757.]: Samplename: Sample#6 Root: 22.186 --> Root in between the borders! Added to results. Hyperbolic solved: 40.1506639226714 [20250519_004757.]: Samplename: Sample#7 Root: 40.151 --> Root in between the borders! Added to results. Hyperbolic solved: 86.1385429083643 [20250519_004757.]: Samplename: Sample#8 Root: 86.139 --> Root in between the borders! Added to results. Hyperbolic solved: 5.22287566816154 [20250519_004757.]: Samplename: Sample#9 Root: 5.223 --> Root in between the borders! Added to results. [20250519_004757.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 74.4935883316308 [20250519_004757.]: Samplename: Sample#1 Root: 74.494 --> Root in between the borders! Added to results. Hyperbolic solved: 28.4239304939459 [20250519_004757.]: Samplename: Sample#10 Root: 28.424 --> Root in between the borders! Added to results. Hyperbolic solved: 38.422868577211 [20250519_004757.]: Samplename: Sample#2 Root: 38.423 --> Root in between the borders! Added to results. Hyperbolic solved: 54.1812729208768 [20250519_004757.]: Samplename: Sample#3 Root: 54.181 --> Root in between the borders! Added to results. Hyperbolic solved: 9.35760412501846 [20250519_004757.]: Samplename: Sample#4 Root: 9.358 --> Root in between the borders! Added to results. Hyperbolic solved: 20.0638687855045 [20250519_004757.]: Samplename: Sample#5 Root: 20.064 --> Root in between the borders! Added to results. Hyperbolic solved: 18.2691112345675 [20250519_004757.]: Samplename: Sample#6 Root: 18.269 --> Root in between the borders! Added to results. Hyperbolic solved: 36.8485384997942 [20250519_004757.]: Samplename: Sample#7 Root: 36.849 --> Root in between the borders! Added to results. Hyperbolic solved: 86.7213727244426 [20250519_004757.]: Samplename: Sample#8 Root: 86.721 --> Root in between the borders! Added to results. Hyperbolic solved: 8.13467837636389 [20250519_004757.]: Samplename: Sample#9 Root: 8.135 --> Root in between the borders! Added to results. [20250519_004757.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 72.9754823399013 [20250519_004757.]: Samplename: Sample#1 Root: 72.975 --> Root in between the borders! Added to results. Hyperbolic solved: 26.09308106721 [20250519_004757.]: Samplename: Sample#10 Root: 26.093 --> Root in between the borders! Added to results. Hyperbolic solved: 38.5204516929959 [20250519_004757.]: Samplename: Sample#2 Root: 38.52 --> Root in between the borders! Added to results. Hyperbolic solved: 52.0558039343263 [20250519_004757.]: Samplename: Sample#3 Root: 52.056 --> Root in between the borders! Added to results. Hyperbolic solved: 10.6836235174275 [20250519_004757.]: Samplename: Sample#4 Root: 10.684 --> Root in between the borders! Added to results. Hyperbolic solved: 23.1384875877206 [20250519_004757.]: Samplename: Sample#5 Root: 23.138 --> Root in between the borders! Added to results. Hyperbolic solved: 24.8934322445107 [20250519_004757.]: Samplename: Sample#6 Root: 24.893 --> Root in between the borders! Added to results. Hyperbolic solved: 40.6015033285401 [20250519_004757.]: Samplename: Sample#7 Root: 40.602 --> Root in between the borders! Added to results. Hyperbolic solved: 86.3873125270529 [20250519_004757.]: Samplename: Sample#8 Root: 86.387 --> Root in between the borders! Added to results. Hyperbolic solved: 4.44964604860886 [20250519_004757.]: Samplename: Sample#9 Root: 4.45 --> Root in between the borders! Added to results. [20250519_004757.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 74.4016735507272 [20250519_004757.]: Samplename: Sample#1 Root: 74.402 --> Root in between the borders! Added to results. Hyperbolic solved: 26.4295897789912 [20250519_004757.]: Samplename: Sample#10 Root: 26.43 --> Root in between the borders! Added to results. Hyperbolic solved: 39.6008425259518 [20250519_004757.]: Samplename: Sample#2 Root: 39.601 --> Root in between the borders! Added to results. Hyperbolic solved: 54.4093205134833 [20250519_004757.]: Samplename: Sample#3 Root: 54.409 --> Root in between the borders! Added to results. Hyperbolic solved: 12.6125973016001 [20250519_004757.]: Samplename: Sample#4 Root: 12.613 --> Root in between the borders! Added to results. Hyperbolic solved: 21.5537046072918 [20250519_004757.]: Samplename: Sample#5 Root: 21.554 --> Root in between the borders! Added to results. Hyperbolic solved: 25.5116357756709 [20250519_004757.]: Samplename: Sample#6 Root: 25.512 --> Root in between the borders! Added to results. Hyperbolic solved: 38.1579452647512 [20250519_004757.]: Samplename: Sample#7 Root: 38.158 --> Root in between the borders! Added to results. Hyperbolic solved: 86.2182702652395 [20250519_004757.]: Samplename: Sample#8 Root: 86.218 --> Root in between the borders! Added to results. Hyperbolic solved: 7.06911234650237 [20250519_004757.]: Samplename: Sample#9 Root: 7.069 --> Root in between the borders! Added to results. [20250519_004757.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 70.2481376198381 [20250519_004757.]: Samplename: Sample#1 Root: 70.248 --> Root in between the borders! Added to results. Hyperbolic solved: 24.9396715205194 [20250519_004757.]: Samplename: Sample#10 Root: 24.94 --> Root in between the borders! Added to results. Hyperbolic solved: 40.4614457485807 [20250519_004757.]: Samplename: Sample#2 Root: 40.461 --> Root in between the borders! Added to results. Hyperbolic solved: 49.3815751365795 [20250519_004757.]: Samplename: Sample#3 Root: 49.382 --> Root in between the borders! Added to results. Hyperbolic solved: 12.353607679335 [20250519_004757.]: Samplename: Sample#4 Root: 12.354 --> Root in between the borders! Added to results. Hyperbolic solved: 22.0901459497739 [20250519_004757.]: Samplename: Sample#5 Root: 22.09 --> Root in between the borders! Added to results. Hyperbolic solved: 24.5507390437226 [20250519_004757.]: Samplename: Sample#6 Root: 24.551 --> Root in between the borders! Added to results. Hyperbolic solved: 39.0947007538418 [20250519_004757.]: Samplename: Sample#7 Root: 39.095 --> Root in between the borders! Added to results. Hyperbolic solved: 90.4841321892294 [20250519_004757.]: Samplename: Sample#8 Root: 90.484 --> Root in between the borders! Added to results. Hyperbolic solved: 6.91649336851909 [20250519_004757.]: Samplename: Sample#9 Root: 6.916 --> Root in between the borders! Added to results. [20250519_004757.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 77.4558189028663 [20250519_004757.]: Samplename: Sample#1 Root: 77.456 --> Root in between the borders! Added to results. Hyperbolic solved: 27.5880652239037 [20250519_004757.]: Samplename: Sample#10 Root: 27.588 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2657605119474 [20250519_004757.]: Samplename: Sample#2 Root: 37.266 --> Root in between the borders! Added to results. Hyperbolic solved: 51.469303759821 [20250519_004757.]: Samplename: Sample#3 Root: 51.469 --> Root in between the borders! Added to results. Hyperbolic solved: 12.3197919642261 [20250519_004757.]: Samplename: Sample#4 Root: 12.32 --> Root in between the borders! Added to results. Hyperbolic solved: 18.9916700132306 [20250519_004757.]: Samplename: Sample#5 Root: 18.992 --> Root in between the borders! Added to results. Hyperbolic solved: 27.3757521451495 [20250519_004757.]: Samplename: Sample#6 Root: 27.376 --> Root in between the borders! Added to results. Hyperbolic solved: 38.1192529388954 [20250519_004757.]: Samplename: Sample#7 Root: 38.119 --> Root in between the borders! Added to results. Hyperbolic solved: 87.7229687796537 [20250519_004757.]: Samplename: Sample#8 Root: 87.723 --> Root in between the borders! Added to results. Hyperbolic solved: 7.66260533640959 [20250519_004757.]: Samplename: Sample#9 Root: 7.663 --> Root in between the borders! Added to results. [20250519_004757.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 75.6858966925732 [20250519_004757.]: Samplename: Sample#1 Root: 75.686 --> Root in between the borders! Added to results. Hyperbolic solved: 23.9111476853231 [20250519_004757.]: Samplename: Sample#10 Root: 23.911 --> Root in between the borders! Added to results. Hyperbolic solved: 41.6834100561109 [20250519_004757.]: Samplename: Sample#2 Root: 41.683 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3038531152796 [20250519_004757.]: Samplename: Sample#3 Root: 52.304 --> Root in between the borders! Added to results. Hyperbolic solved: 14.584082616843 [20250519_004757.]: Samplename: Sample#4 Root: 14.584 --> Root in between the borders! Added to results. Hyperbolic solved: 23.8322996387272 [20250519_004757.]: Samplename: Sample#5 Root: 23.832 --> Root in between the borders! Added to results. Hyperbolic solved: 26.8055282397054 [20250519_004757.]: Samplename: Sample#6 Root: 26.806 --> Root in between the borders! Added to results. Hyperbolic solved: 39.1748201958722 [20250519_004757.]: Samplename: Sample#7 Root: 39.175 --> Root in between the borders! Added to results. Hyperbolic solved: 86.4968654540458 [20250519_004757.]: Samplename: Sample#8 Root: 86.497 --> Root in between the borders! Added to results. Hyperbolic solved: 5.20445580813888 [20250519_004757.]: Samplename: Sample#9 Root: 5.204 --> Root in between the borders! Added to results. [20250519_004757.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 71.4358559327679 [20250519_004757.]: Samplename: Sample#1 Root: 71.436 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5978231834828 [20250519_004757.]: Samplename: Sample#10 Root: 26.598 --> Root in between the borders! Added to results. Hyperbolic solved: 32.9687797883659 [20250519_004757.]: Samplename: Sample#2 Root: 32.969 --> Root in between the borders! Added to results. Hyperbolic solved: 56.5920244960613 [20250519_004757.]: Samplename: Sample#3 Root: 56.592 --> Root in between the borders! Added to results. Hyperbolic solved: 11.2061554830111 [20250519_004757.]: Samplename: Sample#4 Root: 11.206 --> Root in between the borders! Added to results. Hyperbolic solved: 19.2580019302788 [20250519_004757.]: Samplename: Sample#5 Root: 19.258 --> Root in between the borders! Added to results. Hyperbolic solved: 36.3407423784248 [20250519_004757.]: Samplename: Sample#6 Root: 36.341 --> Root in between the borders! Added to results. Hyperbolic solved: 42.2292176199449 [20250519_004757.]: Samplename: Sample#7 Root: 42.229 --> Root in between the borders! Added to results. Hyperbolic solved: 85.1479651236815 [20250519_004757.]: Samplename: Sample#8 Root: 85.148 --> Root in between the borders! Added to results. Hyperbolic solved: 1.97353330914679 [20250519_004757.]: Samplename: Sample#9 Root: 1.974 --> Root in between the borders! Added to results. [20250519_004757.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 78.362574368733 [20250519_004757.]: Samplename: Sample#1 Root: 78.363 --> Root in between the borders! Added to results. Hyperbolic solved: 24.4725957151698 [20250519_004757.]: Samplename: Sample#10 Root: 24.473 --> Root in between the borders! Added to results. Hyperbolic solved: 38.5471090507298 [20250519_004757.]: Samplename: Sample#2 Root: 38.547 --> Root in between the borders! Added to results. Hyperbolic solved: 48.850764118569 [20250519_004757.]: Samplename: Sample#3 Root: 48.851 --> Root in between the borders! Added to results. Hyperbolic solved: 13.0341042345297 [20250519_004757.]: Samplename: Sample#4 Root: 13.034 --> Root in between the borders! Added to results. Hyperbolic solved: 22.1156929084775 [20250519_004757.]: Samplename: Sample#5 Root: 22.116 --> Root in between the borders! Added to results. Hyperbolic solved: 33.0730206710577 [20250519_004757.]: Samplename: Sample#6 Root: 33.073 --> Root in between the borders! Added to results. Hyperbolic solved: 36.8757560868803 [20250519_004757.]: Samplename: Sample#7 Root: 36.876 --> Root in between the borders! Added to results. Hyperbolic solved: 89.6667208986789 [20250519_004757.]: Samplename: Sample#8 Root: 89.667 --> Root in between the borders! Added to results. Hyperbolic solved: 8.10593089892949 [20250519_004757.]: Samplename: Sample#9 Root: 8.106 --> Root in between the borders! Added to results. [20250519_004757.]: Solving hyperbolic regression for row_means Hyperbolic solved: 74.8404708557895 [20250519_004757.]: Samplename: Sample#1 Root: 74.84 --> Root in between the borders! Added to results. Hyperbolic solved: 26.2806125509682 [20250519_004757.]: Samplename: Sample#10 Root: 26.281 --> Root in between the borders! Added to results. Hyperbolic solved: 38.1683813928189 [20250519_004757.]: Samplename: Sample#2 Root: 38.168 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3554454867083 [20250519_004757.]: Samplename: Sample#3 Root: 52.355 --> Root in between the borders! Added to results. Hyperbolic solved: 12.0337443526331 [20250519_004757.]: Samplename: Sample#4 Root: 12.034 --> Root in between the borders! Added to results. Hyperbolic solved: 21.2452112654646 [20250519_004757.]: Samplename: Sample#5 Root: 21.245 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5661392150144 [20250519_004757.]: Samplename: Sample#6 Root: 26.566 --> Root in between the borders! Added to results. Hyperbolic solved: 38.9839362125804 [20250519_004757.]: Samplename: Sample#7 Root: 38.984 --> Root in between the borders! Added to results. Hyperbolic solved: 87.2201089452416 [20250519_004757.]: Samplename: Sample#8 Root: 87.22 --> Root in between the borders! Added to results. Hyperbolic solved: 6.14149092380673 [20250519_004757.]: Samplename: Sample#9 Root: 6.141 --> Root in between the borders! Added to results. [20250519_004758.]: Entered 'clean_dt'-Function [20250519_004758.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_004758.]: got experimental data [20250519_004758.]: Entered 'clean_dt'-Function [20250519_004758.]: Importing data of type 2: Many loci in one sample (e.g., next-gen seq or microarray data) [20250519_004758.]: got experimental data [20250519_004758.]: Entered 'clean_dt'-Function [20250519_004758.]: Entered 'clean_dt'-Function [20250519_004758.]: Entered 'clean_dt'-Function [20250519_004758.]: Entered 'clean_dt'-Function [20250519_004758.]: Entered 'clean_dt'-Function [20250519_004758.]: Entered 'clean_dt'-Function [20250519_004758.]: Entered 'clean_dt'-Function [20250519_004758.]: Entered 'clean_dt'-Function [20250519_004758.]: Entered 'clean_dt'-Function [20250519_004758.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_004758.]: got calibration data ### ERROR 54: first column cannot be parsed to numeric ### [20250519_004758.]: Entered 'clean_dt'-Function [20250519_004758.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_004758.]: got calibration data ### ERROR 54: first column cannot be parsed to numeric ### [20250519_004758.]: Entered 'clean_dt'-Function [20250519_004758.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_004758.]: got calibration data [20250519_004758.]: ### ERROR ### The data provided contains less than four calibration steps. At least four distinct calibration steps are required to perform bias correction. [20250519_004758.]: Entered 'clean_dt'-Function [20250519_004758.]: Importing data of type 2: Many loci in one sample (e.g., next-gen seq or microarray data) [20250519_004758.]: got calibration data [20250519_004758.]: ### ERROR ### The data provided contains locus ids with heterogeneous counts of CpG-sites. [20250519_004758.]: Entered 'clean_dt'-Function [20250519_004758.]: Importing data of type 2: Many loci in one sample (e.g., next-gen seq or microarray data) [20250519_004758.]: got experimental data [20250519_004758.]: ### ERROR ### The data provided contains locus ids with heterogeneous counts of CpG-sites. [20250519_004758.]: Entered 'clean_dt'-Function [20250519_004758.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_004758.]: got calibration data [20250519_004758.]: Entered 'clean_dt'-Function [20250519_004758.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_004758.]: got calibration data [20250519_004758.]: Entered 'cubic_regression'-Function [20250519_004758.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004758.]: Entered 'cubic_regression'-Function [20250519_004758.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004759.]: Entered 'clean_dt'-Function [20250519_004759.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_004759.]: got calibration data [20250519_004759.]: Entered 'hyperbolic_regression'-Function [20250519_004759.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004759.]: Entered 'hyperbolic_regression'-Function [20250519_004759.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_004759.]: on_start: using future::plan("sequential") [20250519_004759.]: Entered 'clean_dt'-Function [20250519_004759.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_004759.]: got experimental data [20250519_004759.]: Entered 'clean_dt'-Function [20250519_004759.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_004759.]: got calibration data [20250519_004759.]: ### Starting with regression calculations ### [20250519_004759.]: Entered 'regression_type1'-Function [20250519_004759.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_004759.]: Logging df_agg: CpG#1 [20250519_004759.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004759.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_004759.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_004759.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_004759.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_004759.]: Entered 'hyperbolic_regression'-Function [20250519_004759.]: 'hyperbolic_regression': minmax = FALSE [20250519_004759.]: Entered 'cubic_regression'-Function [20250519_004759.]: 'cubic_regression': minmax = FALSE [20250519_004759.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_004759.]: Logging df_agg: CpG#2 [20250519_004759.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004759.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_004759.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_004759.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_004759.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_004759.]: Entered 'hyperbolic_regression'-Function [20250519_004759.]: 'hyperbolic_regression': minmax = FALSE [20250519_004759.]: Entered 'cubic_regression'-Function [20250519_004759.]: 'cubic_regression': minmax = FALSE [20250519_004759.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_004759.]: Logging df_agg: CpG#3 [20250519_004759.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004759.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_004759.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_004759.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_004759.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_004759.]: Entered 'hyperbolic_regression'-Function [20250519_004759.]: 'hyperbolic_regression': minmax = FALSE [20250519_004759.]: Entered 'cubic_regression'-Function [20250519_004759.]: 'cubic_regression': minmax = FALSE [20250519_004759.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_004759.]: Logging df_agg: CpG#4 [20250519_004759.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004759.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_004759.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_004759.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_004759.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_004759.]: Entered 'hyperbolic_regression'-Function [20250519_004759.]: 'hyperbolic_regression': minmax = FALSE [20250519_004759.]: Entered 'cubic_regression'-Function [20250519_004759.]: 'cubic_regression': minmax = FALSE [20250519_004759.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_004759.]: Logging df_agg: CpG#5 [20250519_004759.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004759.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_004759.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_004759.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_004759.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_004759.]: Entered 'hyperbolic_regression'-Function [20250519_004759.]: 'hyperbolic_regression': minmax = FALSE [20250519_004759.]: Entered 'cubic_regression'-Function [20250519_004759.]: 'cubic_regression': minmax = FALSE [20250519_004759.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_004759.]: Logging df_agg: CpG#6 [20250519_004759.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004759.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_004759.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_004759.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_004759.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_004759.]: Entered 'hyperbolic_regression'-Function [20250519_004759.]: 'hyperbolic_regression': minmax = FALSE [20250519_004759.]: Entered 'cubic_regression'-Function [20250519_004759.]: 'cubic_regression': minmax = FALSE [20250519_004759.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_004759.]: Logging df_agg: CpG#7 [20250519_004759.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004759.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_004759.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_004759.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_004759.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_004759.]: Entered 'hyperbolic_regression'-Function [20250519_004759.]: 'hyperbolic_regression': minmax = FALSE [20250519_004759.]: Entered 'cubic_regression'-Function [20250519_004759.]: 'cubic_regression': minmax = FALSE [20250519_004759.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_004759.]: Logging df_agg: CpG#8 [20250519_004759.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004759.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_004759.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_004759.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_004759.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_004759.]: Entered 'hyperbolic_regression'-Function [20250519_004759.]: 'hyperbolic_regression': minmax = FALSE [20250519_004759.]: Entered 'cubic_regression'-Function [20250519_004759.]: 'cubic_regression': minmax = FALSE [20250519_004759.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_004759.]: Logging df_agg: CpG#9 [20250519_004759.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004759.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_004759.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_004759.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_004759.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_004759.]: Entered 'hyperbolic_regression'-Function [20250519_004759.]: 'hyperbolic_regression': minmax = FALSE [20250519_004759.]: Entered 'cubic_regression'-Function [20250519_004759.]: 'cubic_regression': minmax = FALSE [20250519_004759.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_004759.]: Logging df_agg: row_means [20250519_004759.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004759.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_004759.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_004759.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_004759.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_004759.]: Entered 'hyperbolic_regression'-Function [20250519_004759.]: 'hyperbolic_regression': minmax = FALSE [20250519_004759.]: Entered 'cubic_regression'-Function [20250519_004759.]: 'cubic_regression': minmax = FALSE [20250519_004800.]: ### Starting with plotting ### [20250519_004800.]: Creating plot No. 1 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Test_CpG1.png [20250519_004800.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -123249135.40483 , a = 49589952.0989949 , b = -123249135.40483 , d = 59280789.0121927 [20250519_004800.]: # CpG-site: CpG#1 Cubic: Using a = 6.53413423120091e-05 , b = -0.0055806968734969 , c = 0.784061853455188 , d = 1.93182659932656 [20250519_004802.]: Creating plot No. 2 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Test_CpG2.png [20250519_004802.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = 167929575.171327 , a = 42299444.0962795 , b = 167929575.171327 , d = 47897274.2220611 [20250519_004802.]: # CpG-site: CpG#2 Cubic: Using a = 8.04237934904601e-06 , b = 0.00293158941798942 , c = 0.514821742825076 , d = 9.27667003367003 [20250519_004803.]: Creating plot No. 3 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Test_CpG3.png [20250519_004803.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = 31004745.3756238 , a = 31050253.3779659 , b = 31004745.3756238 , d = 40665231.758814 [20250519_004803.]: # CpG-site: CpG#3 Cubic: Using a = 2.30555869809204e-05 , b = -0.000797009331409346 , c = 0.62783129228796 , d = 3.88705218855218 [20250519_004805.]: Creating plot No. 4 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Test_CpG4.png [20250519_004805.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = 128022258.276155 , a = 42779497.5538086 , b = 128022258.276155 , d = 50999439.9227065 [20250519_004805.]: # CpG-site: CpG#4 Cubic: Using a = 3.2166356902357e-05 , b = -0.0015913142857143 , c = 0.697398364598366 , d = 6.17055050505048 [20250519_004806.]: Creating plot No. 5 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Test_CpG5.png [20250519_004806.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = 29447513.6184781 , a = 41864399.8673718 , b = 29447513.6184781 , d = 55382346.8470806 [20250519_004806.]: # CpG-site: CpG#5 Cubic: Using a = -4.48459708193036e-06 , b = 0.00375371236171235 , c = 0.422446384479718 , d = 5.17203872053872 [20250519_004808.]: Creating plot No. 6 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Test_CpG6.png [20250519_004808.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = 17150080.5960938 , a = 40932834.1078748 , b = 17150080.5960938 , d = 46723471.6686748 [20250519_004808.]: # CpG-site: CpG#6 Cubic: Using a = 2.38852884399552e-05 , b = 0.000918637037037021 , c = 0.561022132435467 , d = 6.1479276094276 [20250519_004809.]: Creating plot No. 7 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Test_CpG7.png [20250519_004809.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -57204506.2862076 , a = 29554652.6579632 , b = -57204506.2862076 , d = 48548425.6483363 [20250519_004809.]: # CpG-site: CpG#7 Cubic: Using a = 4.98010505050505e-05 , b = -0.00436152150072151 , c = 0.579588917748918 , d = 1.72104545454544 [20250519_004811.]: Creating plot No. 8 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Test_CpG8.png [20250519_004811.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = 263871870.982016 , a = 38159683.1948542 , b = 263871870.982016 , d = 45284547.6704654 [20250519_004811.]: # CpG-site: CpG#8 Cubic: Using a = -1.50060965207632e-05 , b = 0.00447978143338143 , c = 0.534897737694404 , d = 9.56981481481482 [20250519_004812.]: Creating plot No. 9 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Test_CpG9.png [20250519_004812.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -224404013.660658 , a = 51672526.9851193 , b = -224404013.660658 , d = 60201205.3472543 [20250519_004812.]: # CpG-site: CpG#9 Cubic: Using a = 7.72300426487093e-05 , b = -0.00542281173641174 , c = 0.67899229597563 , d = 2.72413468013467 [20250519_004814.]: Creating plot No. 10 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Test_rowmeans.png [20250519_004814.]: # CpG-site: row_means Hyperbolic: Using bias_weight = 29838275.3876239 , a = 33244770.7608578 , b = 29838275.3876239 , d = 41191067.4974404 [20250519_004814.]: # CpG-site: row_means Cubic: Using a = 2.88923726150392e-05 , b = -0.000629959275292592 , c = 0.600117857944524 , d = 5.17789562289563 [20250519_004815.]: on_start: using future::plan("sequential") [20250519_004815.]: Entered 'clean_dt'-Function [20250519_004815.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_004815.]: got experimental data [20250519_004815.]: Entered 'clean_dt'-Function [20250519_004815.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_004815.]: got calibration data [20250519_004815.]: ### Starting with regression calculations ### [20250519_004815.]: Entered 'regression_type1'-Function [20250519_004815.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_004816.]: Logging df_agg: CpG#1 [20250519_004816.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004816.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_004816.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_004816.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_004816.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_004816.]: Entered 'hyperbolic_regression'-Function [20250519_004816.]: 'hyperbolic_regression': minmax = FALSE [20250519_004816.]: Entered 'cubic_regression'-Function [20250519_004816.]: 'cubic_regression': minmax = FALSE [20250519_004816.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_004816.]: Logging df_agg: CpG#2 [20250519_004816.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004816.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_004816.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_004816.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_004816.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_004816.]: Entered 'hyperbolic_regression'-Function [20250519_004816.]: 'hyperbolic_regression': minmax = FALSE [20250519_004816.]: Entered 'cubic_regression'-Function [20250519_004816.]: 'cubic_regression': minmax = FALSE [20250519_004816.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_004816.]: Logging df_agg: CpG#3 [20250519_004816.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004816.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_004816.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_004816.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_004816.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_004816.]: Entered 'hyperbolic_regression'-Function [20250519_004816.]: 'hyperbolic_regression': minmax = FALSE [20250519_004816.]: Entered 'cubic_regression'-Function [20250519_004816.]: 'cubic_regression': minmax = FALSE [20250519_004816.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_004816.]: Logging df_agg: CpG#4 [20250519_004816.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004816.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_004816.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_004816.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_004816.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_004816.]: Entered 'hyperbolic_regression'-Function [20250519_004816.]: 'hyperbolic_regression': minmax = FALSE [20250519_004816.]: Entered 'cubic_regression'-Function [20250519_004816.]: 'cubic_regression': minmax = FALSE [20250519_004816.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_004816.]: Logging df_agg: CpG#5 [20250519_004816.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004816.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_004816.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_004816.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_004816.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_004816.]: Entered 'hyperbolic_regression'-Function [20250519_004816.]: 'hyperbolic_regression': minmax = FALSE [20250519_004816.]: Entered 'cubic_regression'-Function [20250519_004816.]: 'cubic_regression': minmax = FALSE [20250519_004816.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_004816.]: Logging df_agg: CpG#6 [20250519_004816.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004816.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_004816.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_004816.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_004816.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_004816.]: Entered 'hyperbolic_regression'-Function [20250519_004816.]: 'hyperbolic_regression': minmax = FALSE [20250519_004816.]: Entered 'cubic_regression'-Function [20250519_004816.]: 'cubic_regression': minmax = FALSE [20250519_004816.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_004816.]: Logging df_agg: CpG#7 [20250519_004816.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004816.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_004816.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_004816.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_004816.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_004816.]: Entered 'hyperbolic_regression'-Function [20250519_004816.]: 'hyperbolic_regression': minmax = FALSE [20250519_004816.]: Entered 'cubic_regression'-Function [20250519_004816.]: 'cubic_regression': minmax = FALSE [20250519_004816.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_004816.]: Logging df_agg: CpG#8 [20250519_004816.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004816.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_004816.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_004816.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_004816.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_004816.]: Entered 'hyperbolic_regression'-Function [20250519_004816.]: 'hyperbolic_regression': minmax = FALSE [20250519_004816.]: Entered 'cubic_regression'-Function [20250519_004816.]: 'cubic_regression': minmax = FALSE [20250519_004816.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_004816.]: Logging df_agg: CpG#9 [20250519_004816.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004816.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_004816.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_004816.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_004816.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_004816.]: Entered 'hyperbolic_regression'-Function [20250519_004816.]: 'hyperbolic_regression': minmax = FALSE [20250519_004816.]: Entered 'cubic_regression'-Function [20250519_004816.]: 'cubic_regression': minmax = FALSE [20250519_004816.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_004816.]: Logging df_agg: row_means [20250519_004816.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004816.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_004816.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_004816.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_004816.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_004816.]: Entered 'hyperbolic_regression'-Function [20250519_004816.]: 'hyperbolic_regression': minmax = FALSE [20250519_004816.]: Entered 'cubic_regression'-Function [20250519_004816.]: 'cubic_regression': minmax = FALSE [20250519_004817.]: Entered 'solving_equations'-Function [20250519_004817.]: Solving cubic regression for CpG#1 Coefficients: -1.03617340067344Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004817.]: Samplename: 0 Root: 1.334 --> Root in between the borders! Added to results. Coefficients: -8.34150673400678Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004817.]: Samplename: 12.5 Root: 11.446 --> Root in between the borders! Added to results. Coefficients: -15.3881734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004817.]: Samplename: 25 Root: 22.228 --> Root in between the borders! Added to results. Coefficients: -24.2801734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004817.]: Samplename: 37.5 Root: 36.374 --> Root in between the borders! Added to results. Coefficients: -34.9006734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004817.]: Samplename: 50 Root: 52.044 --> Root in between the borders! Added to results. Coefficients: -46.3541734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004817.]: Samplename: 62.5 Root: 66.144 --> Root in between the borders! Added to results. Coefficients: -55.8931734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004817.]: Samplename: 75 Root: 75.864 --> Root in between the borders! Added to results. Coefficients: -63.0981734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004817.]: Samplename: 87.5 Root: 82.254 --> Root in between the borders! Added to results. Coefficients: -91.0461734006735Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_004817.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.877 --> '100 < root < 110' --> substitute 100 [20250519_004817.]: Solving cubic regression for CpG#2 Coefficients: -0.283329966329966Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004817.]: Samplename: 0 Root: 0.549 --> Root in between the borders! Added to results. Coefficients: -6.33999663299663Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004817.]: Samplename: 12.5 Root: 11.533 --> Root in between the borders! Added to results. Coefficients: -15.93932996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004817.]: Samplename: 25 Root: 26.628 --> Root in between the borders! Added to results. Coefficients: -22.33732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004817.]: Samplename: 37.5 Root: 35.509 --> Root in between the borders! Added to results. Coefficients: -32.22832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004817.]: Samplename: 50 Root: 47.851 --> Root in between the borders! Added to results. Coefficients: -49.96332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004817.]: Samplename: 62.5 Root: 66.893 --> Root in between the borders! Added to results. Coefficients: -58.96582996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004817.]: Samplename: 75 Root: 75.431 --> Root in between the borders! Added to results. Coefficients: -68.8366632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004817.]: Samplename: 87.5 Root: 84.118 --> Root in between the borders! Added to results. Coefficients: -90.57732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_004817.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.287 --> '100 < root < 110' --> substitute 100 [20250519_004817.]: Solving cubic regression for CpG#3 Coefficients: -0.90294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004817.]: Samplename: 0 Root: 1.441 --> Root in between the borders! Added to results. Coefficients: -6.57294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004817.]: Samplename: 12.5 Root: 10.568 --> Root in between the borders! Added to results. Coefficients: -15.4289478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004817.]: Samplename: 25 Root: 24.796 --> Root in between the borders! Added to results. Coefficients: -22.6129478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004817.]: Samplename: 37.5 Root: 35.952 --> Root in between the borders! Added to results. Coefficients: -32.7754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004817.]: Samplename: 50 Root: 50.684 --> Root in between the borders! Added to results. Coefficients: -43.8889478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004817.]: Samplename: 62.5 Root: 65.142 --> Root in between the borders! Added to results. Coefficients: -54.9754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004817.]: Samplename: 75 Root: 77.905 --> Root in between the borders! Added to results. Coefficients: -57.6562811447812Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004817.]: Samplename: 87.5 Root: 80.767 --> Root in between the borders! Added to results. Coefficients: -80.6649478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_004817.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.38 --> '100 < root < 110' --> substitute 100 [20250519_004817.]: Solving cubic regression for CpG#4 Coefficients: -0.597449494949524Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004817.]: Samplename: 0 Root: 0.858 --> Root in between the borders! Added to results. Coefficients: -8.25278282828286Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004817.]: Samplename: 12.5 Root: 12.086 --> Root in between the borders! Added to results. Coefficients: -15.8034494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004817.]: Samplename: 25 Root: 23.316 --> Root in between the borders! Added to results. Coefficients: -25.5274494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004817.]: Samplename: 37.5 Root: 37.383 --> Root in between the borders! Added to results. Coefficients: -33.6369494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004817.]: Samplename: 50 Root: 48.353 --> Root in between the borders! Added to results. Coefficients: -50.2554494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004817.]: Samplename: 62.5 Root: 68.082 --> Root in between the borders! Added to results. Coefficients: -56.5394494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004817.]: Samplename: 75 Root: 74.615 --> Root in between the borders! Added to results. Coefficients: -65.5927828282829Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004817.]: Samplename: 87.5 Root: 83.254 --> Root in between the borders! Added to results. Coefficients: -88.3214494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_004817.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.715 --> '100 < root < 110' --> substitute 100 [20250519_004817.]: Solving cubic regression for CpG#5 Coefficients: -0.623961279461278Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004817.]: Samplename: 0 Root: 1.458 --> Root in between the borders! Added to results. Coefficients: -4.76796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004817.]: Samplename: 12.5 Root: 10.347 --> Root in between the borders! Added to results. Coefficients: -12.7259612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004817.]: Samplename: 25 Root: 24.815 --> Root in between the borders! Added to results. Coefficients: -21.1599612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004817.]: Samplename: 37.5 Root: 37.902 --> Root in between the borders! Added to results. Coefficients: -30.6954612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004817.]: Samplename: 50 Root: 50.977 --> Root in between the borders! Added to results. Coefficients: -39.6579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004817.]: Samplename: 62.5 Root: 62.126 --> Root in between the borders! Added to results. Coefficients: -51.6829612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004817.]: Samplename: 75 Root: 75.852 --> Root in between the borders! Added to results. Coefficients: -61.0146279461279Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004817.]: Samplename: 87.5 Root: 85.767 --> Root in between the borders! Added to results. Coefficients: -76.0699612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_004817.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.743 --> '100 < root < 110' --> substitute 100 [20250519_004817.]: Solving cubic regression for CpG#6 Coefficients: -0.196072390572403Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004817.]: Samplename: 0 Root: 0.349 --> Root in between the borders! Added to results. Coefficients: -6.73873905723907Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004817.]: Samplename: 12.5 Root: 11.718 --> Root in between the borders! Added to results. Coefficients: -15.8880723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004817.]: Samplename: 25 Root: 26.396 --> Root in between the borders! Added to results. Coefficients: -22.0000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004817.]: Samplename: 37.5 Root: 35.301 --> Root in between the borders! Added to results. Coefficients: -33.4445723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004817.]: Samplename: 50 Root: 50.134 --> Root in between the borders! Added to results. Coefficients: -46.9000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004817.]: Samplename: 62.5 Root: 64.993 --> Root in between the borders! Added to results. Coefficients: -55.8320723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004817.]: Samplename: 75 Root: 73.639 --> Root in between the borders! Added to results. Coefficients: -71.5454057239057Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004817.]: Samplename: 87.5 Root: 87.043 --> Root in between the borders! Added to results. Coefficients: -89.6560723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_004817.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.329 --> '100 < root < 110' --> substitute 100 [20250519_004817.]: Solving cubic regression for CpG#7 Coefficients: -1.21495454545456Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004817.]: Samplename: 0 Root: 2.13 --> Root in between the borders! Added to results. Coefficients: -5.39562121212123Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004817.]: Samplename: 12.5 Root: 9.973 --> Root in between the borders! Added to results. Coefficients: -11.2649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004817.]: Samplename: 25 Root: 22.206 --> Root in between the borders! Added to results. Coefficients: -17.4509545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004817.]: Samplename: 37.5 Root: 35.814 --> Root in between the borders! Added to results. Coefficients: -26.0314545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004817.]: Samplename: 50 Root: 53.28 --> Root in between the borders! Added to results. Coefficients: -33.9649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004817.]: Samplename: 62.5 Root: 66.598 --> Root in between the borders! Added to results. Coefficients: -41.1689545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004817.]: Samplename: 75 Root: 76.575 --> Root in between the borders! Added to results. Coefficients: -44.1356212121212Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004817.]: Samplename: 87.5 Root: 80.219 --> Root in between the borders! Added to results. Coefficients: -67.2229545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_004817.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.506 --> '100 < root < 110' --> substitute 100 [20250519_004817.]: Solving cubic regression for CpG#8 Coefficients: -1.09618518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004817.]: Samplename: 0 Root: 2.016 --> Root in between the borders! Added to results. Coefficients: -5.44685185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004817.]: Samplename: 12.5 Root: 9.458 --> Root in between the borders! Added to results. Coefficients: -16.9301851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004817.]: Samplename: 25 Root: 26.35 --> Root in between the borders! Added to results. Coefficients: -23.3501851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004817.]: Samplename: 37.5 Root: 34.728 --> Root in between the borders! Added to results. Coefficients: -37.6251851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004817.]: Samplename: 50 Root: 51.781 --> Root in between the borders! Added to results. Coefficients: -48.8261851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004817.]: Samplename: 62.5 Root: 64.192 --> Root in between the borders! Added to results. Coefficients: -61.6676851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004817.]: Samplename: 75 Root: 77.804 --> Root in between the borders! Added to results. Coefficients: -64.5101851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004817.]: Samplename: 87.5 Root: 80.758 --> Root in between the borders! Added to results. Coefficients: -86.0601851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_004817.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.834 --> '100 < root < 110' --> substitute 100 [20250519_004817.]: Solving cubic regression for CpG#9 Coefficients: -0.989865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004817.]: Samplename: 0 Root: 1.475 --> Root in between the borders! Added to results. Coefficients: -6.39586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004817.]: Samplename: 12.5 Root: 10.12 --> Root in between the borders! Added to results. Coefficients: -14.7058653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004817.]: Samplename: 25 Root: 24.844 --> Root in between the borders! Added to results. Coefficients: -20.6238653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004817.]: Samplename: 37.5 Root: 35.327 --> Root in between the borders! Added to results. Coefficients: -31.3958653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004817.]: Samplename: 50 Root: 51.855 --> Root in between the borders! Added to results. Coefficients: -42.6858653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004817.]: Samplename: 62.5 Root: 65.265 --> Root in between the borders! Added to results. Coefficients: -52.9033653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004817.]: Samplename: 75 Root: 74.915 --> Root in between the borders! Added to results. Coefficients: -65.492531986532Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004817.]: Samplename: 87.5 Root: 84.67 --> Root in between the borders! Added to results. Coefficients: -92.9898653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_004817.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.082 --> '100 < root < 110' --> substitute 100 [20250519_004817.]: Solving cubic regression for row_means Coefficients: -0.771215488215478Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 [20250519_004817.]: Samplename: 0 Root: 1.287 --> Root in between the borders! Added to results. Coefficients: -6.47247474747474Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 [20250519_004817.]: Samplename: 12.5 Root: 10.847 --> Root in between the borders! Added to results. Coefficients: -14.8972154882155Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 [20250519_004817.]: Samplename: 25 Root: 24.737 --> Root in between the borders! Added to results. Coefficients: -22.1492154882155Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 [20250519_004817.]: Samplename: 37.5 Root: 36.02 --> Root in between the borders! Added to results. Coefficients: -32.5259932659933Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 [20250519_004817.]: Samplename: 50 Root: 50.639 --> Root in between the borders! Added to results. Coefficients: -44.7218821548821Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 [20250519_004817.]: Samplename: 62.5 Root: 65.497 --> Root in between the borders! Added to results. Coefficients: -54.4032154882155Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 [20250519_004817.]: Samplename: 75 Root: 75.751 --> Root in between the borders! Added to results. Coefficients: -62.4313636363636Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 [20250519_004817.]: Samplename: 87.5 Root: 83.403 --> Root in between the borders! Added to results. Coefficients: -84.7343265993266Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 [20250519_004817.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.573 --> '100 < root < 110' --> substitute 100 [20250519_004817.]: ### Starting with regression calculations ### [20250519_004817.]: Entered 'regression_type1'-Function [20250519_004817.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) [20250519_004817.]: Logging df_agg: CpG#1 [20250519_004817.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004817.]: c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)[20250519_004817.]: c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)[20250519_004817.]: c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) [20250519_004817.]: Entered 'hyperbolic_regression'-Function [20250519_004817.]: 'hyperbolic_regression': minmax = FALSE [20250519_004817.]: Entered 'cubic_regression'-Function [20250519_004817.]: 'cubic_regression': minmax = FALSE [20250519_004817.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) [20250519_004817.]: Logging df_agg: CpG#2 [20250519_004817.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004817.]: c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)[20250519_004817.]: c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)[20250519_004817.]: c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) [20250519_004817.]: Entered 'hyperbolic_regression'-Function [20250519_004817.]: 'hyperbolic_regression': minmax = FALSE [20250519_004817.]: Entered 'cubic_regression'-Function [20250519_004817.]: 'cubic_regression': minmax = FALSE [20250519_004817.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) [20250519_004817.]: Logging df_agg: CpG#3 [20250519_004817.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004817.]: c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)[20250519_004817.]: c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)[20250519_004817.]: c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) [20250519_004817.]: Entered 'hyperbolic_regression'-Function [20250519_004817.]: 'hyperbolic_regression': minmax = FALSE [20250519_004817.]: Entered 'cubic_regression'-Function [20250519_004817.]: 'cubic_regression': minmax = FALSE [20250519_004817.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) [20250519_004817.]: Logging df_agg: CpG#4 [20250519_004817.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004817.]: c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)[20250519_004817.]: c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)[20250519_004817.]: c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) [20250519_004817.]: Entered 'hyperbolic_regression'-Function [20250519_004817.]: 'hyperbolic_regression': minmax = FALSE [20250519_004817.]: Entered 'cubic_regression'-Function [20250519_004817.]: 'cubic_regression': minmax = FALSE [20250519_004817.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) [20250519_004817.]: Logging df_agg: CpG#5 [20250519_004817.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004817.]: c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)[20250519_004817.]: c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)[20250519_004817.]: c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) [20250519_004817.]: Entered 'hyperbolic_regression'-Function [20250519_004817.]: 'hyperbolic_regression': minmax = FALSE [20250519_004817.]: Entered 'cubic_regression'-Function [20250519_004817.]: 'cubic_regression': minmax = FALSE [20250519_004817.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) [20250519_004817.]: Logging df_agg: CpG#6 [20250519_004817.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004817.]: c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)[20250519_004817.]: c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)[20250519_004817.]: c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) [20250519_004817.]: Entered 'hyperbolic_regression'-Function [20250519_004817.]: 'hyperbolic_regression': minmax = FALSE [20250519_004817.]: Entered 'cubic_regression'-Function [20250519_004817.]: 'cubic_regression': minmax = FALSE [20250519_004817.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) [20250519_004817.]: Logging df_agg: CpG#7 [20250519_004817.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004817.]: c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)[20250519_004817.]: c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)[20250519_004817.]: c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) [20250519_004817.]: Entered 'hyperbolic_regression'-Function [20250519_004818.]: 'hyperbolic_regression': minmax = FALSE [20250519_004818.]: Entered 'cubic_regression'-Function [20250519_004818.]: 'cubic_regression': minmax = FALSE [20250519_004818.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) [20250519_004818.]: Logging df_agg: CpG#8 [20250519_004818.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004818.]: c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)[20250519_004818.]: c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)[20250519_004818.]: c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) [20250519_004818.]: Entered 'hyperbolic_regression'-Function [20250519_004818.]: 'hyperbolic_regression': minmax = FALSE [20250519_004818.]: Entered 'cubic_regression'-Function [20250519_004818.]: 'cubic_regression': minmax = FALSE [20250519_004818.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) [20250519_004818.]: Logging df_agg: CpG#9 [20250519_004818.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004818.]: c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)[20250519_004818.]: c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)[20250519_004818.]: c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) [20250519_004818.]: Entered 'hyperbolic_regression'-Function [20250519_004818.]: 'hyperbolic_regression': minmax = FALSE [20250519_004818.]: Entered 'cubic_regression'-Function [20250519_004818.]: 'cubic_regression': minmax = FALSE [20250519_004818.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) [20250519_004818.]: Logging df_agg: row_means [20250519_004818.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004818.]: c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)[20250519_004818.]: c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)[20250519_004818.]: c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) [20250519_004818.]: Entered 'hyperbolic_regression'-Function [20250519_004818.]: 'hyperbolic_regression': minmax = FALSE [20250519_004818.]: Entered 'cubic_regression'-Function [20250519_004818.]: 'cubic_regression': minmax = FALSE [20250519_004818.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Test_error_CpG1_corrected_h.png [20250519_004819.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Test_error_CpG2_corrected_h.png [20250519_004821.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Test_error_CpG3_corrected_h.png [20250519_004822.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Test_error_CpG4_corrected_h.png [20250519_004823.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Test_error_CpG5_corrected_h.png [20250519_004825.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Test_error_CpG6_corrected_h.png [20250519_004826.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Test_error_CpG7_corrected_h.png [20250519_004827.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Test_error_CpG8_corrected_h.png [20250519_004828.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Test_error_CpG9_corrected_h.png [20250519_004830.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Test_error_rowmeans_corrected_h.png [20250519_004831.]: on_start: using future::plan("sequential") [20250519_004833.]: on_start: using future::plan("sequential") [ FAIL 4 | WARN 209 | SKIP 12 | PASS 42 ] == Skipped tests (12) ========================================================== * On CRAN (12): 'test-algorithm_minmax_FALSE.R:113:5', 'test-algorithm_minmax_FALSE_re.R:173:5', 'test-algorithm_minmax_TRUE.R:114:5', 'test-algorithm_minmax_TRUE_re.R:175:5', 'test-clean_dt.R:19:5', 'test-clean_dt.R:61:5', 'test-create_aggregated.R:21:5', 'test-cubic.R:31:5', 'test-cubic.R:60:5', 'test-hyperbolic.R:32:5', 'test-hyperbolic.R:61:5', 'test-lints.R:12:5' == Failed tests ================================================================ -- Failure ('test-algorithm_minmax_FALSE.R:71:5'): algorithm test, type 1, minmax = FALSE -- regression_results$result_list (`actual`) not equal to regression_results_fast$result_list (`expected`). `actual$CpG#1$Coef_hyper$a`: -108.5680 `expected$CpG#1$Coef_hyper$a`: -108.5682 `actual$CpG#1$Coef_hyper$b`: -937.719 `expected$CpG#1$Coef_hyper$b`: -937.718 `actual$CpG#1$Coef_hyper$d`: -232.0571 `expected$CpG#1$Coef_hyper$d`: -232.0574 `actual$CpG#1$Coef_hyper$b1`: 0.5690716 `expected$CpG#1$Coef_hyper$b1`: 0.5690721 `actual$CpG#1$Coef_hyper$s`: 4.075791 `expected$CpG#1$Coef_hyper$s`: 4.075782 `actual$CpG#2$Coef_hyper$a`: -141.862264 `expected$CpG#2$Coef_hyper$a`: -141.862268 `actual$CpG#3$Coef_hyper$a`: -170.4245 `expected$CpG#3$Coef_hyper$a`: -170.4243 `actual$CpG#3$Coef_hyper$b`: -1477.4686 `expected$CpG#3$Coef_hyper$b`: -1477.4687 `actual$CpG#3$Coef_hyper$d`: -327.1902 `expected$CpG#3$Coef_hyper$d`: -327.1899 `actual$CpG#3$Coef_hyper$b1`: 0.6943673 `expected$CpG#3$Coef_hyper$b1`: 0.6943671 `actual$CpG#3$Coef_hyper$s`: 4.540975 `expected$CpG#3$Coef_hyper$s`: 4.540980 `actual$CpG#4$Coef_hyper$a`: -162.1698 `expected$CpG#4$Coef_hyper$a`: -162.1699 `actual$CpG#4$Coef_hyper$b`: -2115.8779 `expected$CpG#4$Coef_hyper$b`: -2115.8781 `actual$CpG#4$Coef_hyper$d`: -299.6623 `expected$CpG#4$Coef_hyper$d`: -299.6625 `actual$CpG#4$Coef_hyper$b1`: 0.6662910 `expected$CpG#4$Coef_hyper$b1`: 0.6662912 `actual$CpG#4$Coef_hyper$s`: 7.075768 `expected$CpG#4$Coef_hyper$s`: 7.075764 `actual$CpG#5$Coef_hyper$a`: -151.44007 `expected$CpG#5$Coef_hyper$a`: -151.44009 `actual$CpG#5$Coef_hyper$b`: -1383.32739 `expected$CpG#5$Coef_hyper$b`: -1383.32735 `actual$CpG#5$Coef_hyper$d`: -304.20025 `expected$CpG#5$Coef_hyper$d`: -304.20028 `actual$CpG#5$Coef_hyper$b1`: 0.67126918 `expected$CpG#5$Coef_hyper$b1`: 0.67126921 `actual$CpG#5$Coef_hyper$s`: 4.5750679 `expected$CpG#5$Coef_hyper$s`: 4.5750673 `actual$CpG#6$Coef_hyper$a`: -125.016146 `expected$CpG#6$Coef_hyper$a`: -125.016150 `actual$CpG#6$Coef_hyper$b`: -1550.59308 `expected$CpG#6$Coef_hyper$b`: -1550.59305 `actual$CpG#6$Coef_hyper$d`: -247.279679 `expected$CpG#6$Coef_hyper$d`: -247.279684 `actual$CpG#6$Coef_hyper$s`: 6.2900686 `expected$CpG#6$Coef_hyper$s`: 6.2900684 `actual$CpG#7$Coef_hyper$a`: -75.2725 `expected$CpG#7$Coef_hyper$a`: -75.2727 `actual$CpG#7$Coef_hyper$b`: -772.8442 `expected$CpG#7$Coef_hyper$b`: -772.8433 `actual$CpG#7$Coef_hyper$d`: -226.6731 `expected$CpG#7$Coef_hyper$d`: -226.6734 `actual$CpG#7$Coef_hyper$b1`: 0.5588361 `expected$CpG#7$Coef_hyper$b1`: 0.5588367 `actual$CpG#7$Coef_hyper$s`: 3.474320 `expected$CpG#7$Coef_hyper$s`: 3.474311 `actual$CpG#8$Coef_hyper$a`: -278.0173 `expected$CpG#8$Coef_hyper$a`: -278.0172 `actual$CpG#8$Coef_hyper$b`: -3872.7362 `expected$CpG#8$Coef_hyper$b`: -3872.7353 `actual$CpG#8$Coef_hyper$d`: -438.8943 `expected$CpG#8$Coef_hyper$d`: -438.8941 `actual$CpG#8$Coef_hyper$b1`: 0.77215469 `expected$CpG#8$Coef_hyper$b1`: 0.77215462 `actual$CpG#8$Coef_hyper$s`: 8.831458 `expected$CpG#8$Coef_hyper$s`: 8.831459 `actual$CpG#9$Coef_hyper$a`: -70.67961 `expected$CpG#9$Coef_hyper$a`: -70.67963 `actual$CpG#9$Coef_hyper$b`: -843.9255 `expected$CpG#9$Coef_hyper$b`: -843.9253 `actual$CpG#9$Coef_hyper$d`: -184.48215 `expected$CpG#9$Coef_hyper$d`: -184.48218 `actual$CpG#9$Coef_hyper$b1`: 0.45794215 `expected$CpG#9$Coef_hyper$b1`: 0.45794222 `actual$CpG#9$Coef_hyper$s`: 4.615973 `expected$CpG#9$Coef_hyper$s`: 4.615971 `actual$row_means$Coef_hyper$a`: -127.67503 `expected$row_means$Coef_hyper$a`: -127.67508 `actual$row_means$Coef_hyper$b`: -1525.7576 `expected$row_means$Coef_hyper$b`: -1525.7575 `actual$row_means$Coef_hyper$d`: -263.00305 `expected$row_means$Coef_hyper$d`: -263.00310 `actual$row_means$Coef_hyper$b1`: 0.61977627 `expected$row_means$Coef_hyper$b1`: 0.61977635 `actual$row_means$Coef_hyper$s`: 5.824068 `expected$row_means$Coef_hyper$s`: 5.824067 -- Failure ('test-algorithm_minmax_FALSE.R:88:5'): algorithm test, type 1, minmax = FALSE -- regression_results$result_list (`actual`) not equal to regression_results_minpack$result_list (`expected`). `actual$CpG#1$SSE_hyper`: 77 `expected$CpG#1$SSE_hyper`: 228 `actual$CpG#1$Coef_hyper$a`: -109 `expected$CpG#1$Coef_hyper$a`: 49589952 `actual$CpG#1$Coef_hyper$b`: -938 `expected$CpG#1$Coef_hyper$b`: -123249135 `actual$CpG#1$Coef_hyper$d`: -232 `expected$CpG#1$Coef_hyper$d`: 59280789 `actual$CpG#1$Coef_hyper$R2`: 0.99 `expected$CpG#1$Coef_hyper$R2`: 0.97 `actual$CpG#1$Coef_hyper$b1`: 0.6 `expected$CpG#1$Coef_hyper$b1`: 1.0 `actual$CpG#1$Coef_hyper$s`: 4 `expected$CpG#1$Coef_hyper$s`: 2 `actual$CpG#2$SSE_hyper`: 46 `expected$CpG#2$SSE_hyper`: 175 `actual$CpG#2$Coef_hyper$a`: -142 `expected$CpG#2$Coef_hyper$a`: 42299444 `actual$CpG#2$Coef_hyper$b`: -2393 `expected$CpG#2$Coef_hyper$b`: 167929575 `actual$CpG#2$Coef_hyper$d`: -268 `expected$CpG#2$Coef_hyper$d`: 47897274 `actual$CpG#2$Coef_hyper$R2`: 0.99 `expected$CpG#2$Coef_hyper$R2`: 0.98 `actual$CpG#2$Coef_hyper$b1`: 0.6 `expected$CpG#2$Coef_hyper$b1`: 1.0 `actual$CpG#2$Coef_hyper$s`: 9 `expected$CpG#2$Coef_hyper$s`: 4 `actual$CpG#3$SSE_hyper`: 67 `expected$CpG#3$SSE_hyper`: 123 `actual$CpG#3$Coef_hyper$a`: -170 `expected$CpG#3$Coef_hyper$a`: 31050253 `actual$CpG#3$Coef_hyper$b`: -1477 `expected$CpG#3$Coef_hyper$b`: 31004745 `actual$CpG#3$Coef_hyper$d`: -327 `expected$CpG#3$Coef_hyper$d`: 40665232 `actual$CpG#3$Coef_hyper$R2`: 0.99 `expected$CpG#3$Coef_hyper$R2`: 0.98 `actual$CpG#3$Coef_hyper$b1`: 0.7 `expected$CpG#3$Coef_hyper$b1`: 1.0 `actual$CpG#3$Coef_hyper$s`: 5 `expected$CpG#3$Coef_hyper$s`: 1 `actual$CpG#4$SSE_hyper`: 58 `expected$CpG#4$SSE_hyper`: 141 `actual$CpG#4$Coef_hyper$a`: -162 `expected$CpG#4$Coef_hyper$a`: 42779498 `actual$CpG#4$Coef_hyper$b`: -2116 `expected$CpG#4$Coef_hyper$b`: 128022258 `actual$CpG#4$Coef_hyper$d`: -300 `expected$CpG#4$Coef_hyper$d`: 50999440 `actual$CpG#4$Coef_hyper$R2`: 0.99 `expected$CpG#4$Coef_hyper$R2`: 0.98 `actual$CpG#4$Coef_hyper$b1`: 0.7 `expected$CpG#4$Coef_hyper$b1`: 1.0 `actual$CpG#4$Coef_hyper$s`: 7 `expected$CpG#4$Coef_hyper$s`: 3 `actual$CpG#5$SSE_hyper`: 8 `expected$CpG#5$SSE_hyper`: 78 `actual$CpG#5$Coef_hyper$a`: -151 `expected$CpG#5$Coef_hyper$a`: 41864400 `actual$CpG#5$Coef_hyper$b`: -1383 `expected$CpG#5$Coef_hyper$b`: 29447514 `actual$CpG#5$Coef_hyper$d`: -304 `expected$CpG#5$Coef_hyper$d`: 55382347 `actual$CpG#5$Coef_hyper$R2`: 1.00 `expected$CpG#5$Coef_hyper$R2`: 0.99 `actual$CpG#5$Coef_hyper$b1`: 0.7 `expected$CpG#5$Coef_hyper$b1`: 1.0 `actual$CpG#5$Coef_hyper$s`: 5 `expected$CpG#5$Coef_hyper$s`: 1 `actual$CpG#6$SSE_hyper`: 12 `expected$CpG#6$SSE_hyper`: 168 `actual$CpG#6$Coef_hyper$a`: -125 `expected$CpG#6$Coef_hyper$a`: 40932834 `actual$CpG#6$Coef_hyper$b`: -1551 `expected$CpG#6$Coef_hyper$b`: 17150081 `actual$CpG#6$Coef_hyper$d`: -247 `expected$CpG#6$Coef_hyper$d`: 46723472 `actual$CpG#6$Coef_hyper$R2`: 1.00 `expected$CpG#6$Coef_hyper$R2`: 0.98 `actual$CpG#6$Coef_hyper$b1`: 0.6 `expected$CpG#6$Coef_hyper$b1`: 1.0 `actual$CpG#6$Coef_hyper$s`: 6 `expected$CpG#6$Coef_hyper$s`: 0 `actual$CpG#7$SSE_hyper`: 72 `expected$CpG#7$SSE_hyper`: 155 `actual$CpG#7$Coef_hyper$a`: -75 `expected$CpG#7$Coef_hyper$a`: 29554653 `actual$CpG#7$Coef_hyper$b`: -773 `expected$CpG#7$Coef_hyper$b`: -57204506 `actual$CpG#7$Coef_hyper$d`: -227 `expected$CpG#7$Coef_hyper$d`: 48548426 `actual$CpG#7$Coef_hyper$R2`: 0.98 `expected$CpG#7$Coef_hyper$R2`: 0.96 `actual$CpG#7$Coef_hyper$b1`: 0.6 `expected$CpG#7$Coef_hyper$b1`: 1.0 `actual$CpG#7$Coef_hyper$s`: 3 `expected$CpG#7$Coef_hyper$s`: 1 `actual$CpG#8$SSE_hyper`: 75 `expected$CpG#8$SSE_hyper`: 112 `actual$CpG#8$Coef_hyper$a`: -278 `expected$CpG#8$Coef_hyper$a`: 38159683 `actual$CpG#8$Coef_hyper$b`: -3873 `expected$CpG#8$Coef_hyper$b`: 263871871 `actual$CpG#8$Coef_hyper$d`: -439 `expected$CpG#8$Coef_hyper$d`: 45284548 `actual$CpG#8$Coef_hyper$R2`: 0.989 `expected$CpG#8$Coef_hyper$R2`: 0.983 `actual$CpG#8$Coef_hyper$b1`: 0.8 `expected$CpG#8$Coef_hyper$b1`: 1.0 `actual$CpG#8$Coef_hyper$s`: 9 `expected$CpG#8$Coef_hyper$s`: 6 `actual$CpG#9$SSE_hyper`: 33 `expected$CpG#9$SSE_hyper`: 352 `actual$CpG#9$Coef_hyper$a`: -71 `expected$CpG#9$Coef_hyper$a`: 51672527 `actual$CpG#9$Coef_hyper$b`: -844 `expected$CpG#9$Coef_hyper$b`: -224404014 `actual$CpG#9$Coef_hyper$d`: -184 `expected$CpG#9$Coef_hyper$d`: 60201205 `actual$CpG#9$Coef_hyper$R2`: 1.00 `expected$CpG#9$Coef_hyper$R2`: 0.95 `actual$CpG#9$Coef_hyper$b1`: 0.5 `expected$CpG#9$Coef_hyper$b1`: 1.0 `actual$CpG#9$Coef_hyper$s`: 4.6 `expected$CpG#9$Coef_hyper$s`: 3.7 `actual$row_means$SSE_hyper`: 35 `expected$row_means$SSE_hyper`: 144 `actual$row_means$Coef_hyper$a`: -128 `expected$row_means$Coef_hyper$a`: 33244771 `actual$row_means$Coef_hyper$b`: -1526 `expected$row_means$Coef_hyper$b`: 29838275 `actual$row_means$Coef_hyper$d`: -263 `expected$row_means$Coef_hyper$d`: 41191067 `actual$row_means$Coef_hyper$R2`: 0.99 `expected$row_means$Coef_hyper$R2`: 0.98 `actual$row_means$Coef_hyper$b1`: 0.6 `expected$row_means$Coef_hyper$b1`: 1.0 `actual$row_means$Coef_hyper$s`: 6 `expected$row_means$Coef_hyper$s`: 1 -- Failure ('test-algorithm_minmax_TRUE.R:71:5'): algorithm test, type 1, minmax = TRUE -- regression_results$result_list (`actual`) not equal to regression_results_fast$result_list (`expected`). `actual$CpG#1$Coef_hyper$b`: 0.5364372 `expected$CpG#1$Coef_hyper$b`: 0.5364373 actual$CpG#1$Coef_cubic$a != expected$CpG#1$Coef_cubic$a but don't know how to show the difference actual$CpG#1$Coef_cubic$b != expected$CpG#1$Coef_cubic$b but don't know how to show the difference actual$CpG#2$Coef_cubic$a != expected$CpG#2$Coef_cubic$a but don't know how to show the difference actual$CpG#2$Coef_cubic$b != expected$CpG#2$Coef_cubic$b but don't know how to show the difference actual$CpG#3$Coef_cubic$a != expected$CpG#3$Coef_cubic$a but don't know how to show the difference `actual$CpG#3$Coef_cubic$b`: -0.00173040 `expected$CpG#3$Coef_cubic$b`: -0.00173041 `actual$CpG#4$Coef_hyper$b`: 0.6263293 `expected$CpG#4$Coef_hyper$b`: 0.6263302 actual$CpG#4$Coef_cubic$a != expected$CpG#4$Coef_cubic$a but don't know how to show the difference actual$CpG#4$Coef_cubic$b != expected$CpG#4$Coef_cubic$b but don't know how to show the difference actual$CpG#5$Coef_cubic$a != expected$CpG#5$Coef_cubic$a but don't know how to show the difference actual$CpG#5$Coef_cubic$b != expected$CpG#5$Coef_cubic$b but don't know how to show the difference actual$CpG#6$Coef_cubic$a != expected$CpG#6$Coef_cubic$a but don't know how to show the difference actual$CpG#6$Coef_cubic$b != expected$CpG#6$Coef_cubic$b but don't know how to show the difference `actual$CpG#7$Coef_hyper$b`: 0.50578129 `expected$CpG#7$Coef_hyper$b`: 0.50578130 actual$CpG#7$Coef_cubic$a != expected$CpG#7$Coef_cubic$a but don't know how to show the difference actual$CpG#7$Coef_cubic$b != expected$CpG#7$Coef_cubic$b but don't know how to show the difference `actual$CpG#8$Coef_hyper$b`: 0.68763945 `expected$CpG#8$Coef_hyper$b`: 0.68763952 actual$CpG#8$Coef_cubic$a != expected$CpG#8$Coef_cubic$a but don't know how to show the difference actual$CpG#8$Coef_cubic$b != expected$CpG#8$Coef_cubic$b but don't know how to show the difference `actual$CpG#9$Coef_hyper$b`: 0.44888828 `expected$CpG#9$Coef_hyper$b`: 0.44888829 actual$CpG#9$Coef_cubic$a != expected$CpG#9$Coef_cubic$a but don't know how to show the difference actual$CpG#9$Coef_cubic$b != expected$CpG#9$Coef_cubic$b but don't know how to show the difference actual$row_means$Coef_cubic$a != expected$row_means$Coef_cubic$a but don't know how to show the difference actual$row_means$Coef_cubic$b != expected$row_means$Coef_cubic$b but don't know how to show the difference -- Failure ('test-algorithm_minmax_TRUE.R:88:5'): algorithm test, type 1, minmax = TRUE -- regression_results$result_list (`actual`) not equal to regression_results_minpack$result_list (`expected`). `actual$CpG#1$Coef_hyper$b`: 0.5364372 `expected$CpG#1$Coef_hyper$b`: 0.5364373 actual$CpG#1$Coef_cubic$a != expected$CpG#1$Coef_cubic$a but don't know how to show the difference actual$CpG#1$Coef_cubic$b != expected$CpG#1$Coef_cubic$b but don't know how to show the difference actual$CpG#2$Coef_cubic$a != expected$CpG#2$Coef_cubic$a but don't know how to show the difference actual$CpG#2$Coef_cubic$b != expected$CpG#2$Coef_cubic$b but don't know how to show the difference `actual$CpG#3$Coef_hyper$b`: 0.6287998 `expected$CpG#3$Coef_hyper$b`: 0.6287994 actual$CpG#3$Coef_cubic$a != expected$CpG#3$Coef_cubic$a but don't know how to show the difference actual$CpG#3$Coef_cubic$b != expected$CpG#3$Coef_cubic$b but don't know how to show the difference `actual$CpG#4$Coef_hyper$b`: 0.6263293 `expected$CpG#4$Coef_hyper$b`: 0.6263302 `actual$CpG#4$Coef_cubic$a`: 0.00004340 `expected$CpG#4$Coef_cubic$a`: 0.00004341 actual$CpG#4$Coef_cubic$b != expected$CpG#4$Coef_cubic$b but don't know how to show the difference `actual$CpG#5$Coef_hyper$b`: 0.63653152 `expected$CpG#5$Coef_hyper$b`: 0.63653153 actual$CpG#5$Coef_cubic$a != expected$CpG#5$Coef_cubic$a but don't know how to show the difference actual$CpG#5$Coef_cubic$b != expected$CpG#5$Coef_cubic$b but don't know how to show the difference actual$CpG#6$Coef_cubic$a != expected$CpG#6$Coef_cubic$a but don't know how to show the difference actual$CpG#6$Coef_cubic$b != expected$CpG#6$Coef_cubic$b but don't know how to show the difference `actual$CpG#7$Coef_hyper$b`: 0.5057813 `expected$CpG#7$Coef_hyper$b`: 0.5057809 actual$CpG#7$Coef_cubic$a != expected$CpG#7$Coef_cubic$a but don't know how to show the difference `actual$CpG#7$Coef_cubic$b`: -0.00511940 `expected$CpG#7$Coef_cubic$b`: -0.00511941 `actual$CpG#8$Coef_hyper$b`: 0.68763945 `expected$CpG#8$Coef_hyper$b`: 0.68763952 actual$CpG#8$Coef_cubic$a != expected$CpG#8$Coef_cubic$a but don't know how to show the difference actual$CpG#8$Coef_cubic$b != expected$CpG#8$Coef_cubic$b but don't know how to show the difference `actual$CpG#9$Coef_hyper$b`: 0.44888828 `expected$CpG#9$Coef_hyper$b`: 0.44888824 actual$CpG#9$Coef_cubic$b != expected$CpG#9$Coef_cubic$b but don't know how to show the difference actual$row_means$Coef_cubic$a != expected$row_means$Coef_cubic$a but don't know how to show the difference `actual$row_means$Coef_cubic$b`: -0.00123898 `expected$row_means$Coef_cubic$b`: -0.00123897 [ FAIL 4 | WARN 209 | SKIP 12 | PASS 42 ] Error: Test failures Execution halted Error in deferred_run(env) : could not find function "deferred_run" Calls: * checking for unstated dependencies in vignettes ... OK * checking package vignettes ... OK * checking re-building of vignette outputs ... OK * checking PDF version of manual ... OK * DONE Status: 1 ERROR See '/tmp/th798/17369309/R-devel/1149/rBiasCorrection.Rcheck/00check.log' for details. [1] "2025-05-19 00:48:43 MST" > system(paste(c("diff -u", Rcheck.list), collapse=" ")) --- R_Under_development_unstable_2025-05-18_r88216/release_1.17.2.Rcheck/00check.log 2025-05-19 00:41:25.506569811 -0700 +++ R_Under_development_unstable_2025-05-18_r88216/master_1.17.99.c4ea09e099b1833fae0eacb50552bce6f051ed82.Rcheck/00check.log 2025-05-19 00:48:43.871881500 -0700 @@ -55,9 +55,6 @@ * checking installed files from 'inst/doc' ... OK * checking files in 'vignettes' ... OK * checking examples ... OK -Examples with CPU (user + system) or elapsed time > 5s - user system elapsed -regression_utility 4.553 0.043 5.842 * checking for unstated dependencies in 'tests' ... OK * checking tests ... ERROR Running 'testthat.R' @@ -73,15 +70,15 @@ > local_edition(3) > > test_check("rBiasCorrection") - [20250519_003523.]: Entered 'clean_dt'-Function - [20250519_003523.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_003523.]: got experimental data - [20250519_003523.]: Entered 'clean_dt'-Function - [20250519_003523.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_003523.]: got calibration data - [20250519_003523.]: + [20250519_004252.]: Entered 'clean_dt'-Function + [20250519_004252.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_004252.]: got experimental data + [20250519_004252.]: Entered 'clean_dt'-Function + [20250519_004252.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_004252.]: got calibration data + [20250519_004252.]: ### Starting with regression calculations ### - [20250519_003523.]: Entered 'regression_type1'-Function + [20250519_004252.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : @@ -606,89 +603,89 @@ singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient - [20250519_003523.]: # CpG-site: CpG#1 + [20250519_004252.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_003523.]: Logging df_agg: CpG#1 - [20250519_003523.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003523.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_003523.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_003523.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_003523.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_003523.]: Entered 'hyperbolic_regression'-Function - [20250519_003523.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003523.]: Entered 'cubic_regression'-Function - [20250519_003523.]: 'cubic_regression': minmax = FALSE - [20250519_003523.]: # CpG-site: CpG#2 + [20250519_004252.]: Logging df_agg: CpG#1 + [20250519_004252.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004252.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_004252.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_004252.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_004252.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) + [20250519_004252.]: Entered 'hyperbolic_regression'-Function + [20250519_004252.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004253.]: Entered 'cubic_regression'-Function + [20250519_004253.]: 'cubic_regression': minmax = FALSE + [20250519_004253.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_003523.]: Logging df_agg: CpG#2 - [20250519_003523.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003523.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_003523.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_003523.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_003523.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_003523.]: Entered 'hyperbolic_regression'-Function - [20250519_003523.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003523.]: Entered 'cubic_regression'-Function - [20250519_003523.]: 'cubic_regression': minmax = FALSE - [20250519_003524.]: # CpG-site: CpG#3 + [20250519_004253.]: Logging df_agg: CpG#2 + [20250519_004253.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004253.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_004253.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_004253.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_004253.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) + [20250519_004253.]: Entered 'hyperbolic_regression'-Function + [20250519_004253.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004253.]: Entered 'cubic_regression'-Function + [20250519_004253.]: 'cubic_regression': minmax = FALSE + [20250519_004253.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_003524.]: Logging df_agg: CpG#3 - [20250519_003524.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003524.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_003524.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_003524.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_003524.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_003524.]: Entered 'hyperbolic_regression'-Function - [20250519_003524.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003524.]: Entered 'cubic_regression'-Function - [20250519_003524.]: 'cubic_regression': minmax = FALSE - [20250519_003524.]: # CpG-site: CpG#4 + [20250519_004253.]: Logging df_agg: CpG#3 + [20250519_004253.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004253.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_004253.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_004253.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_004253.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) + [20250519_004253.]: Entered 'hyperbolic_regression'-Function + [20250519_004253.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004253.]: Entered 'cubic_regression'-Function + [20250519_004253.]: 'cubic_regression': minmax = FALSE + [20250519_004253.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_003524.]: Logging df_agg: CpG#4 - [20250519_003524.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003524.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_003524.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_003524.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_003524.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_003524.]: Entered 'hyperbolic_regression'-Function - [20250519_003524.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003524.]: Entered 'cubic_regression'-Function - [20250519_003524.]: 'cubic_regression': minmax = FALSE - [20250519_003524.]: # CpG-site: CpG#5 + [20250519_004253.]: Logging df_agg: CpG#4 + [20250519_004253.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004253.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_004253.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_004253.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_004253.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) + [20250519_004253.]: Entered 'hyperbolic_regression'-Function + [20250519_004253.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004253.]: Entered 'cubic_regression'-Function + [20250519_004253.]: 'cubic_regression': minmax = FALSE + [20250519_004253.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_003524.]: Logging df_agg: CpG#5 - [20250519_003524.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003524.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_003524.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_003524.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_003524.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_003524.]: Entered 'hyperbolic_regression'-Function - [20250519_003524.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003524.]: Entered 'cubic_regression'-Function - [20250519_003524.]: 'cubic_regression': minmax = FALSE - [20250519_003524.]: # CpG-site: CpG#6 + [20250519_004253.]: Logging df_agg: CpG#5 + [20250519_004253.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004253.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_004253.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_004253.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_004253.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) + [20250519_004253.]: Entered 'hyperbolic_regression'-Function + [20250519_004253.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004254.]: Entered 'cubic_regression'-Function + [20250519_004254.]: 'cubic_regression': minmax = FALSE + [20250519_004254.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_003524.]: Logging df_agg: CpG#6 - [20250519_003524.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003524.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_003524.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_003524.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_003524.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_003524.]: Entered 'hyperbolic_regression'-Function - [20250519_003524.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003525.]: Entered 'cubic_regression'-Function - [20250519_003525.]: 'cubic_regression': minmax = FALSE - [20250519_003525.]: # CpG-site: CpG#7 + [20250519_004254.]: Logging df_agg: CpG#6 + [20250519_004254.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004254.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_004254.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_004254.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_004254.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) + [20250519_004254.]: Entered 'hyperbolic_regression'-Function + [20250519_004254.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004254.]: Entered 'cubic_regression'-Function + [20250519_004254.]: 'cubic_regression': minmax = FALSE + [20250519_004254.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_003525.]: Logging df_agg: CpG#7 - [20250519_003525.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003525.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_003525.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_003525.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_003525.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_003525.]: Entered 'hyperbolic_regression'-Function - [20250519_003525.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003525.]: Entered 'cubic_regression'-Function - [20250519_003525.]: 'cubic_regression': minmax = FALSE - [20250519_003525.]: # CpG-site: CpG#8 + [20250519_004254.]: Logging df_agg: CpG#7 + [20250519_004254.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004254.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_004254.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_004254.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_004254.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) + [20250519_004254.]: Entered 'hyperbolic_regression'-Function + [20250519_004254.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004254.]: Entered 'cubic_regression'-Function + [20250519_004254.]: 'cubic_regression': minmax = FALSE + [20250519_004254.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_003525.]: Logging df_agg: CpG#8 - [20250519_003525.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003525.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_003525.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_003525.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_003525.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_003525.]: Entered 'hyperbolic_regression'-Function - [20250519_003525.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003525.]: Entered 'cubic_regression'-Function - [20250519_003525.]: 'cubic_regression': minmax = FALSE - [20250519_003525.]: # CpG-site: CpG#9 + [20250519_004254.]: Logging df_agg: CpG#8 + [20250519_004254.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004254.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_004254.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_004254.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_004254.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) + [20250519_004254.]: Entered 'hyperbolic_regression'-Function + [20250519_004254.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004254.]: Entered 'cubic_regression'-Function + [20250519_004254.]: 'cubic_regression': minmax = FALSE + [20250519_004254.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_003525.]: Logging df_agg: CpG#9 - [20250519_003525.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003525.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_003525.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_003525.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_003525.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_003525.]: Entered 'hyperbolic_regression'-Function - [20250519_003525.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003525.]: Entered 'cubic_regression'-Function - [20250519_003525.]: 'cubic_regression': minmax = FALSE - [20250519_003525.]: # CpG-site: row_means + [20250519_004254.]: Logging df_agg: CpG#9 + [20250519_004254.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004254.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_004254.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_004254.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_004254.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) + [20250519_004254.]: Entered 'hyperbolic_regression'-Function + [20250519_004254.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004255.]: Entered 'cubic_regression'-Function + [20250519_004255.]: 'cubic_regression': minmax = FALSE + [20250519_004255.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_003525.]: Logging df_agg: row_means - [20250519_003525.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003525.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_003525.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_003525.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_003525.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_003525.]: Entered 'hyperbolic_regression'-Function - [20250519_003525.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003526.]: Entered 'cubic_regression'-Function - [20250519_003526.]: 'cubic_regression': minmax = FALSE - [20250519_003526.]: + [20250519_004255.]: Logging df_agg: row_means + [20250519_004255.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004255.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_004255.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_004255.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_004255.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) + [20250519_004255.]: Entered 'hyperbolic_regression'-Function + [20250519_004255.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004255.]: Entered 'cubic_regression'-Function + [20250519_004255.]: 'cubic_regression': minmax = FALSE + [20250519_004256.]: ### Starting with regression calculations ### - [20250519_003526.]: Entered 'regression_type1'-Function + [20250519_004256.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : @@ -1461,170 +1458,170 @@ step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient - [20250519_003527.]: # CpG-site: CpG#1 + [20250519_004256.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_003527.]: Logging df_agg: CpG#1 - [20250519_003527.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003527.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_003527.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_003527.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_003527.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_003527.]: Entered 'hyperbolic_regression'-Function - [20250519_003527.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003527.]: Entered 'cubic_regression'-Function - [20250519_003527.]: 'cubic_regression': minmax = FALSE - [20250519_003527.]: # CpG-site: CpG#2 + [20250519_004256.]: Logging df_agg: CpG#1 + [20250519_004256.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004256.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_004256.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_004256.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_004256.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) + [20250519_004256.]: Entered 'hyperbolic_regression'-Function + [20250519_004256.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004256.]: Entered 'cubic_regression'-Function + [20250519_004256.]: 'cubic_regression': minmax = FALSE + [20250519_004256.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_003527.]: Logging df_agg: CpG#2 - [20250519_003527.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003527.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_003527.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_003527.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_003527.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_003527.]: Entered 'hyperbolic_regression'-Function - [20250519_003527.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003527.]: Entered 'cubic_regression'-Function - [20250519_003527.]: 'cubic_regression': minmax = FALSE - [20250519_003527.]: # CpG-site: CpG#3 + [20250519_004256.]: Logging df_agg: CpG#2 + [20250519_004256.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004256.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_004256.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_004256.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_004256.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) + [20250519_004256.]: Entered 'hyperbolic_regression'-Function + [20250519_004256.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004256.]: Entered 'cubic_regression'-Function + [20250519_004256.]: 'cubic_regression': minmax = FALSE + [20250519_004256.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_003527.]: Logging df_agg: CpG#3 - [20250519_003527.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003527.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_003527.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_003527.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_003527.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_003527.]: Entered 'hyperbolic_regression'-Function - [20250519_003527.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003527.]: Entered 'cubic_regression'-Function - [20250519_003527.]: 'cubic_regression': minmax = FALSE - [20250519_003527.]: # CpG-site: CpG#4 + [20250519_004256.]: Logging df_agg: CpG#3 + [20250519_004256.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004256.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_004256.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_004256.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_004256.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) + [20250519_004256.]: Entered 'hyperbolic_regression'-Function + [20250519_004256.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004257.]: Entered 'cubic_regression'-Function + [20250519_004257.]: 'cubic_regression': minmax = FALSE + [20250519_004257.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_003527.]: Logging df_agg: CpG#4 - [20250519_003527.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003527.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_003527.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_003527.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_003527.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_003527.]: Entered 'hyperbolic_regression'-Function - [20250519_003527.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003528.]: Entered 'cubic_regression'-Function - [20250519_003528.]: 'cubic_regression': minmax = FALSE - [20250519_003528.]: # CpG-site: CpG#5 + [20250519_004257.]: Logging df_agg: CpG#4 + [20250519_004257.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004257.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_004257.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_004257.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_004257.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) + [20250519_004257.]: Entered 'hyperbolic_regression'-Function + [20250519_004257.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004257.]: Entered 'cubic_regression'-Function + [20250519_004257.]: 'cubic_regression': minmax = FALSE + [20250519_004257.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_003528.]: Logging df_agg: CpG#5 - [20250519_003528.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003528.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_003528.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_003528.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_003528.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_003528.]: Entered 'hyperbolic_regression'-Function - [20250519_003528.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003528.]: Entered 'cubic_regression'-Function - [20250519_003528.]: 'cubic_regression': minmax = FALSE - [20250519_003528.]: # CpG-site: CpG#6 + [20250519_004257.]: Logging df_agg: CpG#5 + [20250519_004257.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004257.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_004257.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_004257.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_004257.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) + [20250519_004257.]: Entered 'hyperbolic_regression'-Function + [20250519_004257.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004257.]: Entered 'cubic_regression'-Function + [20250519_004257.]: 'cubic_regression': minmax = FALSE + [20250519_004257.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_003528.]: Logging df_agg: CpG#6 - [20250519_003528.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003528.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_003528.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_003528.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_003528.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_003528.]: Entered 'hyperbolic_regression'-Function - [20250519_003528.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003528.]: Entered 'cubic_regression'-Function - [20250519_003528.]: 'cubic_regression': minmax = FALSE - [20250519_003528.]: # CpG-site: CpG#7 + [20250519_004257.]: Logging df_agg: CpG#6 + [20250519_004257.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004257.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_004257.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_004257.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_004257.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) + [20250519_004257.]: Entered 'hyperbolic_regression'-Function + [20250519_004257.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004257.]: Entered 'cubic_regression'-Function + [20250519_004257.]: 'cubic_regression': minmax = FALSE + [20250519_004257.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_003528.]: Logging df_agg: CpG#7 - [20250519_003528.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003528.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_003528.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_003528.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_003528.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_003528.]: Entered 'hyperbolic_regression'-Function - [20250519_003528.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003528.]: Entered 'cubic_regression'-Function - [20250519_003528.]: 'cubic_regression': minmax = FALSE - [20250519_003528.]: # CpG-site: CpG#8 + [20250519_004257.]: Logging df_agg: CpG#7 + [20250519_004257.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004257.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_004257.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_004257.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_004257.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) + [20250519_004257.]: Entered 'hyperbolic_regression'-Function + [20250519_004257.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004258.]: Entered 'cubic_regression'-Function + [20250519_004258.]: 'cubic_regression': minmax = FALSE + [20250519_004258.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_003528.]: Logging df_agg: CpG#8 - [20250519_003528.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003528.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_003528.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_003528.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_003528.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_003528.]: Entered 'hyperbolic_regression'-Function - [20250519_003528.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003529.]: Entered 'cubic_regression'-Function - [20250519_003529.]: 'cubic_regression': minmax = FALSE - [20250519_003529.]: # CpG-site: CpG#9 + [20250519_004258.]: Logging df_agg: CpG#8 + [20250519_004258.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004258.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_004258.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_004258.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_004258.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) + [20250519_004258.]: Entered 'hyperbolic_regression'-Function + [20250519_004258.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004258.]: Entered 'cubic_regression'-Function + [20250519_004258.]: 'cubic_regression': minmax = FALSE + [20250519_004258.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_003529.]: Logging df_agg: CpG#9 - [20250519_003529.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003529.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_003529.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_003529.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_003529.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_003529.]: Entered 'hyperbolic_regression'-Function - [20250519_003529.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003529.]: Entered 'cubic_regression'-Function - [20250519_003529.]: 'cubic_regression': minmax = FALSE - [20250519_003529.]: # CpG-site: row_means + [20250519_004258.]: Logging df_agg: CpG#9 + [20250519_004258.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004258.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_004258.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_004258.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_004258.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) + [20250519_004258.]: Entered 'hyperbolic_regression'-Function + [20250519_004258.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004258.]: Entered 'cubic_regression'-Function + [20250519_004258.]: 'cubic_regression': minmax = FALSE + [20250519_004258.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_003529.]: Logging df_agg: row_means - [20250519_003529.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003529.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_003529.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_003529.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_003529.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_003529.]: Entered 'hyperbolic_regression'-Function - [20250519_003529.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003529.]: Entered 'cubic_regression'-Function - [20250519_003529.]: 'cubic_regression': minmax = FALSE - [20250519_003531.]: + [20250519_004258.]: Logging df_agg: row_means + [20250519_004258.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004258.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_004258.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_004258.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_004258.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) + [20250519_004258.]: Entered 'hyperbolic_regression'-Function + [20250519_004258.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004258.]: Entered 'cubic_regression'-Function + [20250519_004258.]: 'cubic_regression': minmax = FALSE + [20250519_004300.]: ### Starting with regression calculations ### - [20250519_003531.]: Entered 'regression_type1'-Function - [20250519_003531.]: # CpG-site: CpG#1 + [20250519_004300.]: Entered 'regression_type1'-Function + [20250519_004300.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_003531.]: Logging df_agg: CpG#1 - [20250519_003531.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003531.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_003531.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_003531.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_003531.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_003531.]: Entered 'hyperbolic_regression'-Function - [20250519_003531.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003531.]: Entered 'cubic_regression'-Function - [20250519_003531.]: 'cubic_regression': minmax = FALSE - [20250519_003531.]: # CpG-site: CpG#2 + [20250519_004300.]: Logging df_agg: CpG#1 + [20250519_004300.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004300.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_004300.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_004300.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_004300.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) + [20250519_004300.]: Entered 'hyperbolic_regression'-Function + [20250519_004300.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004300.]: Entered 'cubic_regression'-Function + [20250519_004300.]: 'cubic_regression': minmax = FALSE + [20250519_004300.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_003531.]: Logging df_agg: CpG#2 - [20250519_003531.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003531.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_003531.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_003531.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_003531.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_003531.]: Entered 'hyperbolic_regression'-Function - [20250519_003531.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003531.]: Entered 'cubic_regression'-Function - [20250519_003531.]: 'cubic_regression': minmax = FALSE - [20250519_003531.]: # CpG-site: CpG#3 + [20250519_004300.]: Logging df_agg: CpG#2 + [20250519_004300.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004300.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_004300.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_004300.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_004300.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) + [20250519_004300.]: Entered 'hyperbolic_regression'-Function + [20250519_004300.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004300.]: Entered 'cubic_regression'-Function + [20250519_004300.]: 'cubic_regression': minmax = FALSE + [20250519_004300.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_003531.]: Logging df_agg: CpG#3 - [20250519_003531.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003531.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_003531.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_003531.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_003531.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_003531.]: Entered 'hyperbolic_regression'-Function - [20250519_003531.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003531.]: Entered 'cubic_regression'-Function - [20250519_003531.]: 'cubic_regression': minmax = FALSE - [20250519_003531.]: # CpG-site: CpG#4 + [20250519_004300.]: Logging df_agg: CpG#3 + [20250519_004300.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004300.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_004300.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_004300.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_004300.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) + [20250519_004300.]: Entered 'hyperbolic_regression'-Function + [20250519_004300.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004300.]: Entered 'cubic_regression'-Function + [20250519_004300.]: 'cubic_regression': minmax = FALSE + [20250519_004300.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_003531.]: Logging df_agg: CpG#4 - [20250519_003531.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003531.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_003531.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_003531.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_003531.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_003531.]: Entered 'hyperbolic_regression'-Function - [20250519_003531.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003531.]: Entered 'cubic_regression'-Function - [20250519_003531.]: 'cubic_regression': minmax = FALSE - [20250519_003531.]: # CpG-site: CpG#5 + [20250519_004300.]: Logging df_agg: CpG#4 + [20250519_004300.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004300.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_004300.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_004300.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_004300.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) + [20250519_004300.]: Entered 'hyperbolic_regression'-Function + [20250519_004300.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004300.]: Entered 'cubic_regression'-Function + [20250519_004300.]: 'cubic_regression': minmax = FALSE + [20250519_004300.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_003531.]: Logging df_agg: CpG#5 - [20250519_003531.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003531.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_003531.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_003531.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_003531.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_003531.]: Entered 'hyperbolic_regression'-Function - [20250519_003531.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003531.]: Entered 'cubic_regression'-Function - [20250519_003531.]: 'cubic_regression': minmax = FALSE - [20250519_003531.]: # CpG-site: CpG#6 + [20250519_004300.]: Logging df_agg: CpG#5 + [20250519_004300.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004300.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_004300.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_004300.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_004300.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) + [20250519_004300.]: Entered 'hyperbolic_regression'-Function + [20250519_004300.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004300.]: Entered 'cubic_regression'-Function + [20250519_004300.]: 'cubic_regression': minmax = FALSE + [20250519_004300.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_003531.]: Logging df_agg: CpG#6 - [20250519_003531.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003531.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_003531.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_003531.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_003531.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_003531.]: Entered 'hyperbolic_regression'-Function - [20250519_003531.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003532.]: Entered 'cubic_regression'-Function - [20250519_003532.]: 'cubic_regression': minmax = FALSE - [20250519_003532.]: # CpG-site: CpG#7 + [20250519_004300.]: Logging df_agg: CpG#6 + [20250519_004300.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004300.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_004300.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_004300.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_004300.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) + [20250519_004300.]: Entered 'hyperbolic_regression'-Function + [20250519_004300.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004300.]: Entered 'cubic_regression'-Function + [20250519_004300.]: 'cubic_regression': minmax = FALSE + [20250519_004300.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_003532.]: Logging df_agg: CpG#7 - [20250519_003532.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003532.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_003532.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_003532.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_003532.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_003532.]: Entered 'hyperbolic_regression'-Function - [20250519_003532.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003532.]: Entered 'cubic_regression'-Function - [20250519_003532.]: 'cubic_regression': minmax = FALSE - [20250519_003532.]: # CpG-site: CpG#8 + [20250519_004300.]: Logging df_agg: CpG#7 + [20250519_004300.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004300.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_004300.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_004300.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_004300.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) + [20250519_004300.]: Entered 'hyperbolic_regression'-Function + [20250519_004300.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004301.]: Entered 'cubic_regression'-Function + [20250519_004301.]: 'cubic_regression': minmax = FALSE + [20250519_004301.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_003532.]: Logging df_agg: CpG#8 - [20250519_003532.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003532.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_003532.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_003532.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_003532.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_003532.]: Entered 'hyperbolic_regression'-Function - [20250519_003532.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003532.]: Entered 'cubic_regression'-Function - [20250519_003532.]: 'cubic_regression': minmax = FALSE - [20250519_003532.]: # CpG-site: CpG#9 + [20250519_004301.]: Logging df_agg: CpG#8 + [20250519_004301.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004301.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_004301.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_004301.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_004301.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) + [20250519_004301.]: Entered 'hyperbolic_regression'-Function + [20250519_004301.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004301.]: Entered 'cubic_regression'-Function + [20250519_004301.]: 'cubic_regression': minmax = FALSE + [20250519_004301.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_003532.]: Logging df_agg: CpG#9 - [20250519_003532.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003532.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_003532.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_003532.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_003532.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_003532.]: Entered 'hyperbolic_regression'-Function - [20250519_003532.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003532.]: Entered 'cubic_regression'-Function - [20250519_003532.]: 'cubic_regression': minmax = FALSE - [20250519_003532.]: # CpG-site: row_means + [20250519_004301.]: Logging df_agg: CpG#9 + [20250519_004301.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004301.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_004301.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_004301.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_004301.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) + [20250519_004301.]: Entered 'hyperbolic_regression'-Function + [20250519_004301.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004301.]: Entered 'cubic_regression'-Function + [20250519_004301.]: 'cubic_regression': minmax = FALSE + [20250519_004301.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_003532.]: Logging df_agg: row_means - [20250519_003532.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003532.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_003532.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_003532.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_003532.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_003532.]: Entered 'hyperbolic_regression'-Function - [20250519_003532.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003532.]: Entered 'cubic_regression'-Function - [20250519_003532.]: 'cubic_regression': minmax = FALSE - [20250519_003534.]: Entered 'regression_type1'-Function + [20250519_004301.]: Logging df_agg: row_means + [20250519_004301.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004301.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_004301.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_004301.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_004301.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) + [20250519_004301.]: Entered 'hyperbolic_regression'-Function + [20250519_004301.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004301.]: Entered 'cubic_regression'-Function + [20250519_004301.]: 'cubic_regression': minmax = FALSE + [20250519_004302.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : @@ -2149,95 +2146,95 @@ singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient - [20250519_003534.]: # CpG-site: CpG#1 + [20250519_004303.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_003534.]: Logging df_agg: CpG#1 - [20250519_003534.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003534.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_003534.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_003534.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_003534.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_003534.]: Entered 'hyperbolic_regression'-Function - [20250519_003534.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003534.]: Entered 'cubic_regression'-Function - [20250519_003534.]: 'cubic_regression': minmax = FALSE - [20250519_003534.]: # CpG-site: CpG#2 + [20250519_004303.]: Logging df_agg: CpG#1 + [20250519_004303.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004303.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_004303.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_004303.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_004303.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) + [20250519_004303.]: Entered 'hyperbolic_regression'-Function + [20250519_004303.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004303.]: Entered 'cubic_regression'-Function + [20250519_004303.]: 'cubic_regression': minmax = FALSE + [20250519_004303.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_003534.]: Logging df_agg: CpG#2 - [20250519_003534.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003534.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_003534.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_003534.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_003534.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_003534.]: Entered 'hyperbolic_regression'-Function - [20250519_003534.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003535.]: Entered 'cubic_regression'-Function - [20250519_003535.]: 'cubic_regression': minmax = FALSE - [20250519_003535.]: # CpG-site: CpG#3 + [20250519_004303.]: Logging df_agg: CpG#2 + [20250519_004303.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004303.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_004303.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_004303.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_004303.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) + [20250519_004303.]: Entered 'hyperbolic_regression'-Function + [20250519_004303.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004303.]: Entered 'cubic_regression'-Function + [20250519_004303.]: 'cubic_regression': minmax = FALSE + [20250519_004303.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_003535.]: Logging df_agg: CpG#3 - [20250519_003535.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003535.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_003535.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_003535.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_003535.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_003535.]: Entered 'hyperbolic_regression'-Function - [20250519_003535.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003535.]: Entered 'cubic_regression'-Function - [20250519_003535.]: 'cubic_regression': minmax = FALSE - [20250519_003535.]: # CpG-site: CpG#4 + [20250519_004303.]: Logging df_agg: CpG#3 + [20250519_004303.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004303.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_004303.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_004303.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_004303.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) + [20250519_004303.]: Entered 'hyperbolic_regression'-Function + [20250519_004303.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004304.]: Entered 'cubic_regression'-Function + [20250519_004304.]: 'cubic_regression': minmax = FALSE + [20250519_004304.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_003535.]: Logging df_agg: CpG#4 - [20250519_003535.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003535.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_003535.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_003535.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_003535.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_003535.]: Entered 'hyperbolic_regression'-Function - [20250519_003535.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003535.]: Entered 'cubic_regression'-Function - [20250519_003535.]: 'cubic_regression': minmax = FALSE - [20250519_003535.]: # CpG-site: CpG#5 + [20250519_004304.]: Logging df_agg: CpG#4 + [20250519_004304.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004304.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_004304.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_004304.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_004304.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) + [20250519_004304.]: Entered 'hyperbolic_regression'-Function + [20250519_004304.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004304.]: Entered 'cubic_regression'-Function + [20250519_004304.]: 'cubic_regression': minmax = FALSE + [20250519_004304.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_003535.]: Logging df_agg: CpG#5 - [20250519_003535.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003535.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_003535.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_003535.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_003535.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_003535.]: Entered 'hyperbolic_regression'-Function - [20250519_003535.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003535.]: Entered 'cubic_regression'-Function - [20250519_003535.]: 'cubic_regression': minmax = FALSE - [20250519_003535.]: # CpG-site: CpG#6 + [20250519_004304.]: Logging df_agg: CpG#5 + [20250519_004304.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004304.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_004304.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_004304.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_004304.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) + [20250519_004304.]: Entered 'hyperbolic_regression'-Function + [20250519_004304.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004304.]: Entered 'cubic_regression'-Function + [20250519_004304.]: 'cubic_regression': minmax = FALSE + [20250519_004304.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_003535.]: Logging df_agg: CpG#6 - [20250519_003535.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003535.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_003535.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_003535.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_003535.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_003535.]: Entered 'hyperbolic_regression'-Function - [20250519_003535.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003536.]: Entered 'cubic_regression'-Function - [20250519_003536.]: 'cubic_regression': minmax = FALSE - [20250519_003536.]: # CpG-site: CpG#7 + [20250519_004304.]: Logging df_agg: CpG#6 + [20250519_004304.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004304.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_004304.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_004304.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_004304.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) + [20250519_004304.]: Entered 'hyperbolic_regression'-Function + [20250519_004304.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004304.]: Entered 'cubic_regression'-Function + [20250519_004304.]: 'cubic_regression': minmax = FALSE + [20250519_004304.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_003536.]: Logging df_agg: CpG#7 - [20250519_003536.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003536.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_003536.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_003536.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_003536.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_003536.]: Entered 'hyperbolic_regression'-Function - [20250519_003536.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003536.]: Entered 'cubic_regression'-Function - [20250519_003536.]: 'cubic_regression': minmax = FALSE - [20250519_003536.]: # CpG-site: CpG#8 + [20250519_004304.]: Logging df_agg: CpG#7 + [20250519_004304.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004304.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_004304.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_004304.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_004304.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) + [20250519_004304.]: Entered 'hyperbolic_regression'-Function + [20250519_004304.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004305.]: Entered 'cubic_regression'-Function + [20250519_004305.]: 'cubic_regression': minmax = FALSE + [20250519_004305.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_003536.]: Logging df_agg: CpG#8 - [20250519_003536.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003536.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_003536.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_003536.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_003536.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_003536.]: Entered 'hyperbolic_regression'-Function - [20250519_003536.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003536.]: Entered 'cubic_regression'-Function - [20250519_003536.]: 'cubic_regression': minmax = FALSE - [20250519_003536.]: # CpG-site: CpG#9 + [20250519_004305.]: Logging df_agg: CpG#8 + [20250519_004305.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004305.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_004305.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_004305.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_004305.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) + [20250519_004305.]: Entered 'hyperbolic_regression'-Function + [20250519_004305.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004305.]: Entered 'cubic_regression'-Function + [20250519_004305.]: 'cubic_regression': minmax = FALSE + [20250519_004305.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_003536.]: Logging df_agg: CpG#9 - [20250519_003536.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003536.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_003536.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_003536.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_003536.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_003536.]: Entered 'hyperbolic_regression'-Function - [20250519_003536.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003537.]: Entered 'cubic_regression'-Function - [20250519_003537.]: 'cubic_regression': minmax = FALSE - [20250519_003537.]: # CpG-site: row_means + [20250519_004305.]: Logging df_agg: CpG#9 + [20250519_004305.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004305.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_004305.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_004305.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_004305.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) + [20250519_004305.]: Entered 'hyperbolic_regression'-Function + [20250519_004305.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004305.]: Entered 'cubic_regression'-Function + [20250519_004305.]: 'cubic_regression': minmax = FALSE + [20250519_004305.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_003537.]: Logging df_agg: row_means - [20250519_003537.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003537.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_003537.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_003537.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_003537.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_003537.]: Entered 'hyperbolic_regression'-Function - [20250519_003537.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003537.]: Entered 'cubic_regression'-Function - [20250519_003537.]: 'cubic_regression': minmax = FALSE - [20250519_003538.]: Entered 'clean_dt'-Function - [20250519_003538.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_003538.]: got experimental data - [20250519_003538.]: Entered 'clean_dt'-Function - [20250519_003538.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_003538.]: got calibration data - [20250519_003538.]: + [20250519_004305.]: Logging df_agg: row_means + [20250519_004305.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004305.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_004305.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_004305.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_004305.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) + [20250519_004305.]: Entered 'hyperbolic_regression'-Function + [20250519_004305.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004306.]: Entered 'cubic_regression'-Function + [20250519_004306.]: 'cubic_regression': minmax = FALSE + [20250519_004306.]: Entered 'clean_dt'-Function + [20250519_004306.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_004306.]: got experimental data + [20250519_004306.]: Entered 'clean_dt'-Function + [20250519_004306.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_004306.]: got calibration data + [20250519_004306.]: ### Starting with regression calculations ### - [20250519_003538.]: Entered 'regression_type1'-Function + [20250519_004306.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : @@ -2762,87 +2759,87 @@ singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient - [20250519_003538.]: # CpG-site: CpG#1 + [20250519_004306.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_003538.]: Logging df_agg: CpG#1 - [20250519_003538.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003538.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_003538.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_003538.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_003538.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_003538.]: Entered 'hyperbolic_regression'-Function - [20250519_003538.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003538.]: Entered 'cubic_regression'-Function - [20250519_003538.]: 'cubic_regression': minmax = FALSE - [20250519_003538.]: # CpG-site: CpG#2 + [20250519_004306.]: Logging df_agg: CpG#1 + [20250519_004306.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004306.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_004306.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_004306.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_004306.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) + [20250519_004306.]: Entered 'hyperbolic_regression'-Function + [20250519_004306.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004307.]: Entered 'cubic_regression'-Function + [20250519_004307.]: 'cubic_regression': minmax = FALSE + [20250519_004307.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_003538.]: Logging df_agg: CpG#2 - [20250519_003538.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003538.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_003538.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_003538.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_003538.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_003538.]: Entered 'hyperbolic_regression'-Function - [20250519_003538.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003538.]: Entered 'cubic_regression'-Function - [20250519_003538.]: 'cubic_regression': minmax = FALSE - [20250519_003538.]: # CpG-site: CpG#3 + [20250519_004307.]: Logging df_agg: CpG#2 + [20250519_004307.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004307.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_004307.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_004307.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_004307.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) + [20250519_004307.]: Entered 'hyperbolic_regression'-Function + [20250519_004307.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004307.]: Entered 'cubic_regression'-Function + [20250519_004307.]: 'cubic_regression': minmax = FALSE + [20250519_004307.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_003538.]: Logging df_agg: CpG#3 - [20250519_003538.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003538.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_003538.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_003538.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_003538.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_003538.]: Entered 'hyperbolic_regression'-Function - [20250519_003538.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003538.]: Entered 'cubic_regression'-Function - [20250519_003538.]: 'cubic_regression': minmax = FALSE - [20250519_003538.]: # CpG-site: CpG#4 + [20250519_004307.]: Logging df_agg: CpG#3 + [20250519_004307.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004307.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_004307.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_004307.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_004307.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) + [20250519_004307.]: Entered 'hyperbolic_regression'-Function + [20250519_004307.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004307.]: Entered 'cubic_regression'-Function + [20250519_004307.]: 'cubic_regression': minmax = FALSE + [20250519_004307.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_003538.]: Logging df_agg: CpG#4 - [20250519_003538.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003538.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_003538.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_003538.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_003538.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_003538.]: Entered 'hyperbolic_regression'-Function - [20250519_003538.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003539.]: Entered 'cubic_regression'-Function - [20250519_003539.]: 'cubic_regression': minmax = FALSE - [20250519_003539.]: # CpG-site: CpG#5 + [20250519_004307.]: Logging df_agg: CpG#4 + [20250519_004307.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004307.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_004307.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_004307.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_004307.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) + [20250519_004307.]: Entered 'hyperbolic_regression'-Function + [20250519_004307.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004307.]: Entered 'cubic_regression'-Function + [20250519_004307.]: 'cubic_regression': minmax = FALSE + [20250519_004307.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_003539.]: Logging df_agg: CpG#5 - [20250519_003539.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003539.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_003539.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_003539.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_003539.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_003539.]: Entered 'hyperbolic_regression'-Function - [20250519_003539.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003539.]: Entered 'cubic_regression'-Function - [20250519_003539.]: 'cubic_regression': minmax = FALSE - [20250519_003539.]: # CpG-site: CpG#6 + [20250519_004307.]: Logging df_agg: CpG#5 + [20250519_004307.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004307.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_004307.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_004307.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_004307.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) + [20250519_004307.]: Entered 'hyperbolic_regression'-Function + [20250519_004307.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004308.]: Entered 'cubic_regression'-Function + [20250519_004308.]: 'cubic_regression': minmax = FALSE + [20250519_004308.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_003539.]: Logging df_agg: CpG#6 - [20250519_003539.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003539.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_003539.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_003539.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_003539.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_003539.]: Entered 'hyperbolic_regression'-Function - [20250519_003539.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003539.]: Entered 'cubic_regression'-Function - [20250519_003539.]: 'cubic_regression': minmax = FALSE - [20250519_003539.]: # CpG-site: CpG#7 + [20250519_004308.]: Logging df_agg: CpG#6 + [20250519_004308.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004308.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_004308.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_004308.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_004308.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) + [20250519_004308.]: Entered 'hyperbolic_regression'-Function + [20250519_004308.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004308.]: Entered 'cubic_regression'-Function + [20250519_004308.]: 'cubic_regression': minmax = FALSE + [20250519_004308.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_003539.]: Logging df_agg: CpG#7 - [20250519_003539.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003539.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_003539.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_003539.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_003539.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_003539.]: Entered 'hyperbolic_regression'-Function - [20250519_003539.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003539.]: Entered 'cubic_regression'-Function - [20250519_003539.]: 'cubic_regression': minmax = FALSE - [20250519_003539.]: # CpG-site: CpG#8 + [20250519_004308.]: Logging df_agg: CpG#7 + [20250519_004308.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004308.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_004308.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_004308.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_004308.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) + [20250519_004308.]: Entered 'hyperbolic_regression'-Function + [20250519_004308.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004308.]: Entered 'cubic_regression'-Function + [20250519_004308.]: 'cubic_regression': minmax = FALSE + [20250519_004308.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_003539.]: Logging df_agg: CpG#8 - [20250519_003539.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003539.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_003539.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_003539.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_003539.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_003539.]: Entered 'hyperbolic_regression'-Function - [20250519_003539.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003540.]: Entered 'cubic_regression'-Function - [20250519_003540.]: 'cubic_regression': minmax = FALSE - [20250519_003540.]: # CpG-site: CpG#9 + [20250519_004308.]: Logging df_agg: CpG#8 + [20250519_004308.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004308.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_004308.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_004308.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_004308.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) + [20250519_004308.]: Entered 'hyperbolic_regression'-Function + [20250519_004308.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004308.]: Entered 'cubic_regression'-Function + [20250519_004308.]: 'cubic_regression': minmax = FALSE + [20250519_004308.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_003540.]: Logging df_agg: CpG#9 - [20250519_003540.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003540.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_003540.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_003540.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_003540.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_003540.]: Entered 'hyperbolic_regression'-Function - [20250519_003540.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003540.]: Entered 'cubic_regression'-Function - [20250519_003540.]: 'cubic_regression': minmax = FALSE - [20250519_003540.]: # CpG-site: row_means + [20250519_004308.]: Logging df_agg: CpG#9 + [20250519_004308.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004308.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_004308.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_004308.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_004308.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) + [20250519_004308.]: Entered 'hyperbolic_regression'-Function + [20250519_004308.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004309.]: Entered 'cubic_regression'-Function + [20250519_004309.]: 'cubic_regression': minmax = FALSE + [20250519_004309.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_003540.]: Logging df_agg: row_means - [20250519_003540.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003540.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_003540.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_003540.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_003540.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_003540.]: Entered 'hyperbolic_regression'-Function - [20250519_003540.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003540.]: Entered 'cubic_regression'-Function - [20250519_003540.]: 'cubic_regression': minmax = FALSE - [20250519_003541.]: Entered 'regression_type1'-Function + [20250519_004309.]: Logging df_agg: row_means + [20250519_004309.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004309.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_004309.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_004309.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_004309.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) + [20250519_004309.]: Entered 'hyperbolic_regression'-Function + [20250519_004309.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004309.]: Entered 'cubic_regression'-Function + [20250519_004309.]: 'cubic_regression': minmax = FALSE + [20250519_004309.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : @@ -3367,90 +3364,90 @@ singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient - [20250519_003541.]: # CpG-site: CpG#1 + [20250519_004310.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_003541.]: Logging df_agg: CpG#1 - [20250519_003541.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003541.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_003541.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_003541.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_003541.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_003541.]: Entered 'hyperbolic_regression'-Function - [20250519_003541.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003541.]: Entered 'cubic_regression'-Function - [20250519_003541.]: 'cubic_regression': minmax = FALSE - [20250519_003541.]: # CpG-site: CpG#2 + [20250519_004310.]: Logging df_agg: CpG#1 + [20250519_004310.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004310.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_004310.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_004310.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_004310.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) + [20250519_004310.]: Entered 'hyperbolic_regression'-Function + [20250519_004310.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004310.]: Entered 'cubic_regression'-Function + [20250519_004310.]: 'cubic_regression': minmax = FALSE + [20250519_004310.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_003541.]: Logging df_agg: CpG#2 - [20250519_003541.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003541.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_003541.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_003541.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_003541.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_003541.]: Entered 'hyperbolic_regression'-Function - [20250519_003541.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003541.]: Entered 'cubic_regression'-Function - [20250519_003541.]: 'cubic_regression': minmax = FALSE - [20250519_003541.]: # CpG-site: CpG#3 + [20250519_004310.]: Logging df_agg: CpG#2 + [20250519_004310.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004310.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_004310.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_004310.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_004310.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) + [20250519_004310.]: Entered 'hyperbolic_regression'-Function + [20250519_004310.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004310.]: Entered 'cubic_regression'-Function + [20250519_004310.]: 'cubic_regression': minmax = FALSE + [20250519_004310.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_003541.]: Logging df_agg: CpG#3 - [20250519_003541.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003541.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_003541.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_003541.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_003541.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_003541.]: Entered 'hyperbolic_regression'-Function - [20250519_003541.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003542.]: Entered 'cubic_regression'-Function - [20250519_003542.]: 'cubic_regression': minmax = FALSE - [20250519_003542.]: # CpG-site: CpG#4 + [20250519_004310.]: Logging df_agg: CpG#3 + [20250519_004310.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004310.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_004310.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_004310.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_004310.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) + [20250519_004310.]: Entered 'hyperbolic_regression'-Function + [20250519_004310.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004310.]: Entered 'cubic_regression'-Function + [20250519_004310.]: 'cubic_regression': minmax = FALSE + [20250519_004310.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_003542.]: Logging df_agg: CpG#4 - [20250519_003542.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003542.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_003542.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_003542.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_003542.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_003542.]: Entered 'hyperbolic_regression'-Function - [20250519_003542.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003542.]: Entered 'cubic_regression'-Function - [20250519_003542.]: 'cubic_regression': minmax = FALSE - [20250519_003542.]: # CpG-site: CpG#5 + [20250519_004310.]: Logging df_agg: CpG#4 + [20250519_004310.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004310.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_004310.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_004310.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_004310.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) + [20250519_004310.]: Entered 'hyperbolic_regression'-Function + [20250519_004310.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004311.]: Entered 'cubic_regression'-Function + [20250519_004311.]: 'cubic_regression': minmax = FALSE + [20250519_004311.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_003542.]: Logging df_agg: CpG#5 - [20250519_003542.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003542.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_003542.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_003542.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_003542.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_003542.]: Entered 'hyperbolic_regression'-Function - [20250519_003542.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003542.]: Entered 'cubic_regression'-Function - [20250519_003542.]: 'cubic_regression': minmax = FALSE - [20250519_003542.]: # CpG-site: CpG#6 + [20250519_004311.]: Logging df_agg: CpG#5 + [20250519_004311.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004311.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_004311.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_004311.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_004311.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) + [20250519_004311.]: Entered 'hyperbolic_regression'-Function + [20250519_004311.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004311.]: Entered 'cubic_regression'-Function + [20250519_004311.]: 'cubic_regression': minmax = FALSE + [20250519_004311.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_003542.]: Logging df_agg: CpG#6 - [20250519_003542.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003542.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_003542.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_003542.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_003542.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_003542.]: Entered 'hyperbolic_regression'-Function - [20250519_003542.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003542.]: Entered 'cubic_regression'-Function - [20250519_003542.]: 'cubic_regression': minmax = FALSE - [20250519_003542.]: # CpG-site: CpG#7 + [20250519_004311.]: Logging df_agg: CpG#6 + [20250519_004311.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004311.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_004311.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_004311.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_004311.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) + [20250519_004311.]: Entered 'hyperbolic_regression'-Function + [20250519_004311.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004311.]: Entered 'cubic_regression'-Function + [20250519_004311.]: 'cubic_regression': minmax = FALSE + [20250519_004311.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_003542.]: Logging df_agg: CpG#7 - [20250519_003542.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003542.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_003542.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_003542.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_003542.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_003542.]: Entered 'hyperbolic_regression'-Function - [20250519_003542.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003543.]: Entered 'cubic_regression'-Function - [20250519_003543.]: 'cubic_regression': minmax = FALSE - [20250519_003543.]: # CpG-site: CpG#8 + [20250519_004311.]: Logging df_agg: CpG#7 + [20250519_004311.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004311.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_004311.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_004311.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_004311.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) + [20250519_004311.]: Entered 'hyperbolic_regression'-Function + [20250519_004311.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004311.]: Entered 'cubic_regression'-Function + [20250519_004311.]: 'cubic_regression': minmax = FALSE + [20250519_004311.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_003543.]: Logging df_agg: CpG#8 - [20250519_003543.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003543.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_003543.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_003543.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_003543.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_003543.]: Entered 'hyperbolic_regression'-Function - [20250519_003543.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003543.]: Entered 'cubic_regression'-Function - [20250519_003543.]: 'cubic_regression': minmax = FALSE - [20250519_003543.]: # CpG-site: CpG#9 + [20250519_004311.]: Logging df_agg: CpG#8 + [20250519_004311.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004311.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_004311.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_004311.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_004311.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) + [20250519_004311.]: Entered 'hyperbolic_regression'-Function + [20250519_004311.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004311.]: Entered 'cubic_regression'-Function + [20250519_004312.]: 'cubic_regression': minmax = FALSE + [20250519_004312.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_003543.]: Logging df_agg: CpG#9 - [20250519_003543.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003543.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_003543.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_003543.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_003543.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_003543.]: Entered 'hyperbolic_regression'-Function - [20250519_003543.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003543.]: Entered 'cubic_regression'-Function - [20250519_003543.]: 'cubic_regression': minmax = FALSE - [20250519_003543.]: # CpG-site: row_means + [20250519_004312.]: Logging df_agg: CpG#9 + [20250519_004312.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004312.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_004312.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_004312.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_004312.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) + [20250519_004312.]: Entered 'hyperbolic_regression'-Function + [20250519_004312.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004312.]: Entered 'cubic_regression'-Function + [20250519_004312.]: 'cubic_regression': minmax = FALSE + [20250519_004312.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_003543.]: Logging df_agg: row_means - [20250519_003543.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003543.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_003543.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_003543.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_003543.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_003543.]: Entered 'hyperbolic_regression'-Function - [20250519_003543.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003543.]: Entered 'cubic_regression'-Function - [20250519_003543.]: 'cubic_regression': minmax = FALSE - [20250519_003544.]: Entered 'solving_equations'-Function - [20250519_003544.]: Solving hyperbolic regression for CpG#1 + [20250519_004312.]: Logging df_agg: row_means + [20250519_004312.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004312.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_004312.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_004312.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_004312.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) + [20250519_004312.]: Entered 'hyperbolic_regression'-Function + [20250519_004312.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004312.]: Entered 'cubic_regression'-Function + [20250519_004312.]: 'cubic_regression': minmax = FALSE + [20250519_004313.]: Entered 'solving_equations'-Function + [20250519_004313.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: -2.23222837469517 - [20250519_003544.]: Samplename: 0 + [20250519_004313.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. @@ -3458,126 +3455,126 @@ Root: -2.232 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.1698391693126 - [20250519_003544.]: Samplename: 12.5 + [20250519_004313.]: Samplename: 12.5 Root: 12.17 --> Root in between the borders! Added to results. Hyperbolic solved: 24.4781729791561 - [20250519_003544.]: Samplename: 25 + [20250519_004313.]: Samplename: 25 Root: 24.478 --> Root in between the borders! Added to results. Hyperbolic solved: 38.1730159074047 - [20250519_003544.]: Samplename: 37.5 + [20250519_004313.]: Samplename: 37.5 Root: 38.173 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3348987967717 - [20250519_003544.]: Samplename: 50 + [20250519_004313.]: Samplename: 50 Root: 52.335 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4582305966058 - [20250519_003544.]: Samplename: 62.5 + [20250519_004313.]: Samplename: 62.5 Root: 65.458 --> Root in between the borders! Added to results. Hyperbolic solved: 75.0090269722885 - [20250519_003544.]: Samplename: 75 + [20250519_004313.]: Samplename: 75 Root: 75.009 --> Root in between the borders! Added to results. Hyperbolic solved: 81.527135738891 - [20250519_003544.]: Samplename: 87.5 + [20250519_004313.]: Samplename: 87.5 Root: 81.527 --> Root in between the borders! Added to results. Hyperbolic solved: 102.400825350044 - [20250519_003544.]: Samplename: 100 + [20250519_004313.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.401 --> '100 < root < 110' --> substitute 100 - [20250519_003544.]: Solving hyperbolic regression for CpG#2 + [20250519_004313.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 1.13663087266051 - [20250519_003544.]: Samplename: 0 + [20250519_004313.]: Samplename: 0 Root: 1.137 --> Root in between the borders! Added to results. Hyperbolic solved: 11.4130277963576 - [20250519_003544.]: Samplename: 12.5 + [20250519_004313.]: Samplename: 12.5 Root: 11.413 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1741025868157 - [20250519_003544.]: Samplename: 25 + [20250519_004313.]: Samplename: 25 Root: 26.174 --> Root in between the borders! Added to results. Hyperbolic solved: 35.1051721736724 - [20250519_003544.]: Samplename: 37.5 + [20250519_004313.]: Samplename: 37.5 Root: 35.105 --> Root in between the borders! Added to results. Hyperbolic solved: 47.6856613240114 - [20250519_003544.]: Samplename: 50 + [20250519_004313.]: Samplename: 50 Root: 47.686 --> Root in between the borders! Added to results. Hyperbolic solved: 67.1442585354696 - [20250519_003544.]: Samplename: 62.5 + [20250519_004313.]: Samplename: 62.5 Root: 67.144 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7646957109439 - [20250519_003544.]: Samplename: 75 + [20250519_004313.]: Samplename: 75 Root: 75.765 --> Root in between the borders! Added to results. Hyperbolic solved: 84.4056634834815 - [20250519_003544.]: Samplename: 87.5 + [20250519_004313.]: Samplename: 87.5 Root: 84.406 --> Root in between the borders! Added to results. Hyperbolic solved: 100.94855337669 - [20250519_003544.]: Samplename: 100 + [20250519_004313.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.949 --> '100 < root < 110' --> substitute 100 - [20250519_003544.]: Solving hyperbolic regression for CpG#3 + [20250519_004313.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 0.512348257854635 - [20250519_003544.]: Samplename: 0 + [20250519_004313.]: Samplename: 0 Root: 0.512 --> Root in between the borders! Added to results. Hyperbolic solved: 10.7523476241811 - [20250519_003544.]: Samplename: 12.5 + [20250519_004313.]: Samplename: 12.5 Root: 10.752 --> Root in between the borders! Added to results. Hyperbolic solved: 25.5218062516799 - [20250519_003544.]: Samplename: 25 + [20250519_004313.]: Samplename: 25 Root: 25.522 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5269315729339 - [20250519_003544.]: Samplename: 37.5 + [20250519_004313.]: Samplename: 37.5 Root: 36.527 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7907738274884 - [20250519_003544.]: Samplename: 50 + [20250519_004313.]: Samplename: 50 Root: 50.791 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8684489988589 - [20250519_003544.]: Samplename: 62.5 + [20250519_004313.]: Samplename: 62.5 Root: 64.868 --> Root in between the borders! Added to results. Hyperbolic solved: 77.5522101073979 - [20250519_003544.]: Samplename: 75 + [20250519_004313.]: Samplename: 75 Root: 77.552 --> Root in between the borders! Added to results. Hyperbolic solved: 80.4372474691229 - [20250519_003544.]: Samplename: 87.5 + [20250519_004313.]: Samplename: 87.5 Root: 80.437 --> Root in between the borders! Added to results. Hyperbolic solved: 102.703772810881 - [20250519_003544.]: Samplename: 100 + [20250519_004313.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.704 --> '100 < root < 110' --> substitute 100 - [20250519_003544.]: Solving hyperbolic regression for CpG#4 + [20250519_004313.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: -0.519513573026794 - [20250519_003544.]: Samplename: 0 + [20250519_004313.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. @@ -3585,126 +3582,126 @@ Root: -0.52 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.4933760051342 - [20250519_003544.]: Samplename: 12.5 + [20250519_004313.]: Samplename: 12.5 Root: 12.493 --> Root in between the borders! Added to results. Hyperbolic solved: 24.2684789512555 - [20250519_003544.]: Samplename: 25 + [20250519_004313.]: Samplename: 25 Root: 24.268 --> Root in between the borders! Added to results. Hyperbolic solved: 38.0816229393195 - [20250519_003544.]: Samplename: 37.5 + [20250519_004313.]: Samplename: 37.5 Root: 38.082 --> Root in between the borders! Added to results. Hyperbolic solved: 48.5842089589187 - [20250519_003544.]: Samplename: 50 + [20250519_004313.]: Samplename: 50 Root: 48.584 --> Root in between the borders! Added to results. Hyperbolic solved: 67.6720983549562 - [20250519_003544.]: Samplename: 62.5 + [20250519_004313.]: Samplename: 62.5 Root: 67.672 --> Root in between the borders! Added to results. Hyperbolic solved: 74.1547759694059 - [20250519_003544.]: Samplename: 75 + [20250519_004313.]: Samplename: 75 Root: 74.155 --> Root in between the borders! Added to results. Hyperbolic solved: 82.8820147905386 - [20250519_003544.]: Samplename: 87.5 + [20250519_004313.]: Samplename: 87.5 Root: 82.882 --> Root in between the borders! Added to results. Hyperbolic solved: 102.078935346876 - [20250519_003544.]: Samplename: 100 + [20250519_004313.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.079 --> '100 < root < 110' --> substitute 100 - [20250519_003544.]: Solving hyperbolic regression for CpG#5 + [20250519_004313.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 2.41558040143479 - [20250519_003544.]: Samplename: 0 + [20250519_004313.]: Samplename: 0 Root: 2.416 --> Root in between the borders! Added to results. Hyperbolic solved: 10.1649584830834 - [20250519_003544.]: Samplename: 12.5 + [20250519_004313.]: Samplename: 12.5 Root: 10.165 --> Root in between the borders! Added to results. Hyperbolic solved: 23.9830670020905 - [20250519_003544.]: Samplename: 25 + [20250519_004313.]: Samplename: 25 Root: 23.983 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2773406385708 - [20250519_003544.]: Samplename: 37.5 + [20250519_004313.]: Samplename: 37.5 Root: 37.277 --> Root in between the borders! Added to results. Hyperbolic solved: 50.8659103346102 - [20250519_003544.]: Samplename: 50 + [20250519_004313.]: Samplename: 50 Root: 50.866 --> Root in between the borders! Added to results. Hyperbolic solved: 62.4341926937382 - [20250519_003544.]: Samplename: 62.5 + [20250519_004313.]: Samplename: 62.5 Root: 62.434 --> Root in between the borders! Added to results. Hyperbolic solved: 76.3914832329149 - [20250519_003544.]: Samplename: 75 + [20250519_004313.]: Samplename: 75 Root: 76.391 --> Root in between the borders! Added to results. Hyperbolic solved: 86.1597399215782 - [20250519_003544.]: Samplename: 87.5 + [20250519_004313.]: Samplename: 87.5 Root: 86.16 --> Root in between the borders! Added to results. Hyperbolic solved: 100.267701841999 - [20250519_003544.]: Samplename: 100 + [20250519_004313.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.268 --> '100 < root < 110' --> substitute 100 - [20250519_003544.]: Solving hyperbolic regression for CpG#6 + [20250519_004313.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 0.138179963459196 - [20250519_003544.]: Samplename: 0 + [20250519_004313.]: Samplename: 0 Root: 0.138 --> Root in between the borders! Added to results. Hyperbolic solved: 11.8635989635202 - [20250519_003544.]: Samplename: 12.5 + [20250519_004313.]: Samplename: 12.5 Root: 11.864 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5108198460409 - [20250519_003544.]: Samplename: 25 + [20250519_004313.]: Samplename: 25 Root: 26.511 --> Root in between the borders! Added to results. Hyperbolic solved: 35.3206004021833 - [20250519_003544.]: Samplename: 37.5 + [20250519_004313.]: Samplename: 37.5 Root: 35.321 --> Root in between the borders! Added to results. Hyperbolic solved: 50.0571987473308 - [20250519_003544.]: Samplename: 50 + [20250519_004313.]: Samplename: 50 Root: 50.057 --> Root in between the borders! Added to results. Hyperbolic solved: 64.9604436607063 - [20250519_003544.]: Samplename: 62.5 + [20250519_004313.]: Samplename: 62.5 Root: 64.96 --> Root in between the borders! Added to results. Hyperbolic solved: 73.6690699266294 - [20250519_003544.]: Samplename: 75 + [20250519_004313.]: Samplename: 75 Root: 73.669 --> Root in between the borders! Added to results. Hyperbolic solved: 87.1267946879604 - [20250519_003544.]: Samplename: 87.5 + [20250519_004313.]: Samplename: 87.5 Root: 87.127 --> Root in between the borders! Added to results. Hyperbolic solved: 100.261842767865 - [20250519_003544.]: Samplename: 100 + [20250519_004313.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.262 --> '100 < root < 110' --> substitute 100 - [20250519_003544.]: Solving hyperbolic regression for CpG#7 + [20250519_004313.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: -1.37239249181669 - [20250519_003544.]: Samplename: 0 + [20250519_004313.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. @@ -3712,85 +3709,85 @@ Root: -1.372 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.199366215418 - [20250519_003544.]: Samplename: 12.5 + [20250519_004313.]: Samplename: 12.5 Root: 10.199 --> Root in between the borders! Added to results. Hyperbolic solved: 24.595295361327 - [20250519_003544.]: Samplename: 25 + [20250519_004313.]: Samplename: 25 Root: 24.595 --> Root in between the borders! Added to results. Hyperbolic solved: 37.8312095633391 - [20250519_003544.]: Samplename: 37.5 + [20250519_004313.]: Samplename: 37.5 Root: 37.831 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5590896582852 - [20250519_003544.]: Samplename: 50 + [20250519_004313.]: Samplename: 50 Root: 53.559 --> Root in between the borders! Added to results. Hyperbolic solved: 65.9367389282431 - [20250519_003544.]: Samplename: 62.5 + [20250519_004313.]: Samplename: 62.5 Root: 65.937 --> Root in between the borders! Added to results. Hyperbolic solved: 75.736370152841 - [20250519_003544.]: Samplename: 75 + [20250519_004313.]: Samplename: 75 Root: 75.736 --> Root in between the borders! Added to results. Hyperbolic solved: 79.433089359524 - [20250519_003544.]: Samplename: 87.5 + [20250519_004313.]: Samplename: 87.5 Root: 79.433 --> Root in between the borders! Added to results. Hyperbolic solved: 103.004516225662 - [20250519_003544.]: Samplename: 100 + [20250519_004313.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 103.005 --> '100 < root < 110' --> substitute 100 - [20250519_003544.]: Solving hyperbolic regression for CpG#8 + [20250519_004313.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 2.80067549526643 - [20250519_003544.]: Samplename: 0 + [20250519_004313.]: Samplename: 0 Root: 2.801 --> Root in between the borders! Added to results. Hyperbolic solved: 9.27534686003087 - [20250519_003544.]: Samplename: 12.5 + [20250519_004313.]: Samplename: 12.5 Root: 9.275 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4762624089952 - [20250519_003544.]: Samplename: 25 + [20250519_004313.]: Samplename: 25 Root: 25.476 --> Root in between the borders! Added to results. Hyperbolic solved: 34.0122098045585 - [20250519_003544.]: Samplename: 37.5 + [20250519_004313.]: Samplename: 37.5 Root: 34.012 --> Root in between the borders! Added to results. Hyperbolic solved: 51.784270967861 - [20250519_003544.]: Samplename: 50 + [20250519_004313.]: Samplename: 50 Root: 51.784 --> Root in between the borders! Added to results. Hyperbolic solved: 64.6732380258789 - [20250519_003544.]: Samplename: 62.5 + [20250519_004313.]: Samplename: 62.5 Root: 64.673 --> Root in between the borders! Added to results. Hyperbolic solved: 78.4327054483192 - [20250519_003544.]: Samplename: 75 + [20250519_004313.]: Samplename: 75 Root: 78.433 --> Root in between the borders! Added to results. Hyperbolic solved: 81.3427308951795 - [20250519_003544.]: Samplename: 87.5 + [20250519_004313.]: Samplename: 87.5 Root: 81.343 --> Root in between the borders! Added to results. Hyperbolic solved: 101.964413523995 - [20250519_003544.]: Samplename: 100 + [20250519_004313.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.964 --> '100 < root < 110' --> substitute 100 - [20250519_003544.]: Solving hyperbolic regression for CpG#9 + [20250519_004313.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: -2.13402763663736 - [20250519_003544.]: Samplename: 0 + [20250519_004313.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. @@ -3798,85 +3795,85 @@ Root: -2.134 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.5081558897243 - [20250519_003544.]: Samplename: 12.5 + [20250519_004313.]: Samplename: 12.5 Root: 10.508 --> Root in between the borders! Added to results. Hyperbolic solved: 26.9163121098627 - [20250519_003544.]: Samplename: 25 + [20250519_004313.]: Samplename: 25 Root: 26.916 --> Root in between the borders! Added to results. Hyperbolic solved: 36.8332915301825 - [20250519_003544.]: Samplename: 37.5 + [20250519_004313.]: Samplename: 37.5 Root: 36.833 --> Root in between the borders! Added to results. Hyperbolic solved: 52.00955 - [20250519_003544.]: Samplename: 50 + [20250519_004313.]: Samplename: 50 Root: 52.01 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8927781893359 - [20250519_003544.]: Samplename: 62.5 + [20250519_004313.]: Samplename: 62.5 Root: 64.893 --> Root in between the borders! Added to results. Hyperbolic solved: 74.566810799042 - [20250519_003544.]: Samplename: 75 + [20250519_004313.]: Samplename: 75 Root: 74.567 --> Root in between the borders! Added to results. Hyperbolic solved: 84.5291854856128 - [20250519_003544.]: Samplename: 87.5 + [20250519_004313.]: Samplename: 87.5 Root: 84.529 --> Root in between the borders! Added to results. Hyperbolic solved: 101.046823491232 - [20250519_003544.]: Samplename: 100 + [20250519_004313.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.047 --> '100 < root < 110' --> substitute 100 - [20250519_003544.]: Solving hyperbolic regression for row_means + [20250519_004313.]: Solving hyperbolic regression for row_means Hyperbolic solved: 0.290936038655697 - [20250519_003544.]: Samplename: 0 + [20250519_004313.]: Samplename: 0 Root: 0.291 --> Root in between the borders! Added to results. Hyperbolic solved: 11.0412364555656 - [20250519_003544.]: Samplename: 12.5 + [20250519_004313.]: Samplename: 12.5 Root: 11.041 --> Root in between the borders! Added to results. Hyperbolic solved: 25.408146358228 - [20250519_003544.]: Samplename: 25 + [20250519_004313.]: Samplename: 25 Root: 25.408 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5243683648753 - [20250519_003544.]: Samplename: 37.5 + [20250519_004313.]: Samplename: 37.5 Root: 36.524 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7348788337891 - [20250519_003544.]: Samplename: 50 + [20250519_004313.]: Samplename: 50 Root: 50.735 --> Root in between the borders! Added to results. Hyperbolic solved: 65.3135169308955 - [20250519_003544.]: Samplename: 62.5 + [20250519_004313.]: Samplename: 62.5 Root: 65.314 --> Root in between the borders! Added to results. Hyperbolic solved: 75.5342663138126 - [20250519_003544.]: Samplename: 75 + [20250519_004313.]: Samplename: 75 Root: 75.534 --> Root in between the borders! Added to results. Hyperbolic solved: 83.2411177153911 - [20250519_003544.]: Samplename: 87.5 + [20250519_004313.]: Samplename: 87.5 Root: 83.241 --> Root in between the borders! Added to results. Hyperbolic solved: 101.666935931185 - [20250519_003544.]: Samplename: 100 + [20250519_004313.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.667 --> '100 < root < 110' --> substitute 100 - [20250519_003544.]: + [20250519_004313.]: ### Starting with regression calculations ### - [20250519_003544.]: Entered 'regression_type1'-Function + [20250519_004313.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : @@ -4371,202 +4368,202 @@ singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient - [20250519_003545.]: # CpG-site: CpG#1 + [20250519_004313.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.1698391693126, 24.4781729791561, 38.1730159074047, 52.3348987967717, 65.4582305966058, 75.0090269722885, 81.527135738891, 100)c(0, 0.330160830687401, 0.521827020843901, 0.673015907404697, 2.3348987967717, 2.9582305966058, 0.00902697228849547, 5.972864261109, 0)c(NA, 2.64128664549921, 2.0873080833756, 1.79470908641252, 4.66979759354339, 4.73316895456928, 0.0120359630513273, 6.82613058412457, 0) - [20250519_003545.]: Logging df_agg: CpG#1 - [20250519_003545.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003545.]: c(0, 12.1698391693126, 24.4781729791561, 38.1730159074047, 52.3348987967717, 65.4582305966058, 75.0090269722885, 81.527135738891, 100)[20250519_003545.]: c(0, 0.330160830687401, 0.521827020843901, 0.673015907404697, 2.3348987967717, 2.9582305966058, 0.00902697228849547, 5.972864261109, 0)[20250519_003545.]: c(NA, 2.64128664549921, 2.0873080833756, 1.79470908641252, 4.66979759354339, 4.73316895456928, 0.0120359630513273, 6.82613058412457, 0) - [20250519_003545.]: Entered 'hyperbolic_regression'-Function - [20250519_003545.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003545.]: Entered 'cubic_regression'-Function - [20250519_003545.]: 'cubic_regression': minmax = FALSE - [20250519_003545.]: # CpG-site: CpG#2 + [20250519_004313.]: Logging df_agg: CpG#1 + [20250519_004313.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004313.]: c(0, 12.1698391693126, 24.4781729791561, 38.1730159074047, 52.3348987967717, 65.4582305966058, 75.0090269722885, 81.527135738891, 100)[20250519_004313.]: c(0, 0.330160830687401, 0.521827020843901, 0.673015907404697, 2.3348987967717, 2.9582305966058, 0.00902697228849547, 5.972864261109, 0)[20250519_004313.]: c(NA, 2.64128664549921, 2.0873080833756, 1.79470908641252, 4.66979759354339, 4.73316895456928, 0.0120359630513273, 6.82613058412457, 0) + [20250519_004313.]: Entered 'hyperbolic_regression'-Function + [20250519_004313.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004313.]: Entered 'cubic_regression'-Function + [20250519_004313.]: 'cubic_regression': minmax = FALSE + [20250519_004313.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.13663087266051, 11.4130277963576, 26.1741025868157, 35.1051721736724, 47.6856613240114, 67.1442585354696, 75.7646957109439, 84.4056634834815, 100)c(1.13663087266051, 1.0869722036424, 1.1741025868157, 2.3948278263276, 2.3143386759886, 4.6442585354696, 0.764695710943897, 3.0943365165185, 0)c(NA, 8.6957776291392, 4.6964103472628, 6.3862075368736, 4.6286773519772, 7.43081365675137, 1.01959428125853, 3.53638459030685, 0) - [20250519_003545.]: Logging df_agg: CpG#2 - [20250519_003545.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003545.]: c(1.13663087266051, 11.4130277963576, 26.1741025868157, 35.1051721736724, 47.6856613240114, 67.1442585354696, 75.7646957109439, 84.4056634834815, 100)[20250519_003545.]: c(1.13663087266051, 1.0869722036424, 1.1741025868157, 2.3948278263276, 2.3143386759886, 4.6442585354696, 0.764695710943897, 3.0943365165185, 0)[20250519_003545.]: c(NA, 8.6957776291392, 4.6964103472628, 6.3862075368736, 4.6286773519772, 7.43081365675137, 1.01959428125853, 3.53638459030685, 0) - [20250519_003545.]: Entered 'hyperbolic_regression'-Function - [20250519_003545.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003545.]: Entered 'cubic_regression'-Function - [20250519_003545.]: 'cubic_regression': minmax = FALSE - [20250519_003545.]: # CpG-site: CpG#3 + [20250519_004313.]: Logging df_agg: CpG#2 + [20250519_004314.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004314.]: c(1.13663087266051, 11.4130277963576, 26.1741025868157, 35.1051721736724, 47.6856613240114, 67.1442585354696, 75.7646957109439, 84.4056634834815, 100)[20250519_004314.]: c(1.13663087266051, 1.0869722036424, 1.1741025868157, 2.3948278263276, 2.3143386759886, 4.6442585354696, 0.764695710943897, 3.0943365165185, 0)[20250519_004314.]: c(NA, 8.6957776291392, 4.6964103472628, 6.3862075368736, 4.6286773519772, 7.43081365675137, 1.01959428125853, 3.53638459030685, 0) + [20250519_004314.]: Entered 'hyperbolic_regression'-Function + [20250519_004314.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004314.]: Entered 'cubic_regression'-Function + [20250519_004314.]: 'cubic_regression': minmax = FALSE + [20250519_004314.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.512348257854635, 10.7523476241811, 25.5218062516799, 36.5269315729339, 50.7907738274884, 64.8684489988589, 77.5522101073979, 80.4372474691229, 100)c(0.512348257854635, 1.7476523758189, 0.521806251679902, 0.973068427066103, 0.790773827488401, 2.3684489988589, 2.5522101073979, 7.06275253087711, 0)c(NA, 13.9812190065512, 2.08722500671961, 2.59484913884294, 1.5815476549768, 3.78951839817423, 3.40294680986386, 8.07171717814526, 0) - [20250519_003545.]: Logging df_agg: CpG#3 - [20250519_003545.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003545.]: c(0.512348257854635, 10.7523476241811, 25.5218062516799, 36.5269315729339, 50.7907738274884, 64.8684489988589, 77.5522101073979, 80.4372474691229, 100)[20250519_003545.]: c(0.512348257854635, 1.7476523758189, 0.521806251679902, 0.973068427066103, 0.790773827488401, 2.3684489988589, 2.5522101073979, 7.06275253087711, 0)[20250519_003545.]: c(NA, 13.9812190065512, 2.08722500671961, 2.59484913884294, 1.5815476549768, 3.78951839817423, 3.40294680986386, 8.07171717814526, 0) - [20250519_003545.]: Entered 'hyperbolic_regression'-Function - [20250519_003545.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003546.]: Entered 'cubic_regression'-Function - [20250519_003546.]: 'cubic_regression': minmax = FALSE - [20250519_003546.]: # CpG-site: CpG#4 + [20250519_004314.]: Logging df_agg: CpG#3 + [20250519_004314.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004314.]: c(0.512348257854635, 10.7523476241811, 25.5218062516799, 36.5269315729339, 50.7907738274884, 64.8684489988589, 77.5522101073979, 80.4372474691229, 100)[20250519_004314.]: c(0.512348257854635, 1.7476523758189, 0.521806251679902, 0.973068427066103, 0.790773827488401, 2.3684489988589, 2.5522101073979, 7.06275253087711, 0)[20250519_004314.]: c(NA, 13.9812190065512, 2.08722500671961, 2.59484913884294, 1.5815476549768, 3.78951839817423, 3.40294680986386, 8.07171717814526, 0) + [20250519_004314.]: Entered 'hyperbolic_regression'-Function + [20250519_004314.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004314.]: Entered 'cubic_regression'-Function + [20250519_004314.]: 'cubic_regression': minmax = FALSE + [20250519_004314.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.4933760051342, 24.2684789512555, 38.0816229393195, 48.5842089589187, 67.6720983549562, 74.1547759694059, 82.8820147905386, 100)c(0, 0.00662399486579979, 0.731521048744501, 0.581622939319502, 1.4157910410813, 5.1720983549562, 0.845224030594096, 4.61798520946139, 0)c(NA, 0.0529919589263983, 2.926084194978, 1.550994504852, 2.8315820821626, 8.27535736792993, 1.12696537412546, 5.27769738224159, 0) - [20250519_003546.]: Logging df_agg: CpG#4 - [20250519_003546.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003546.]: c(0, 12.4933760051342, 24.2684789512555, 38.0816229393195, 48.5842089589187, 67.6720983549562, 74.1547759694059, 82.8820147905386, 100)[20250519_003546.]: c(0, 0.00662399486579979, 0.731521048744501, 0.581622939319502, 1.4157910410813, 5.1720983549562, 0.845224030594096, 4.61798520946139, 0)[20250519_003546.]: c(NA, 0.0529919589263983, 2.926084194978, 1.550994504852, 2.8315820821626, 8.27535736792993, 1.12696537412546, 5.27769738224159, 0) - [20250519_003546.]: Entered 'hyperbolic_regression'-Function - [20250519_003546.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003546.]: Entered 'cubic_regression'-Function - [20250519_003546.]: 'cubic_regression': minmax = FALSE - [20250519_003546.]: # CpG-site: CpG#5 + [20250519_004314.]: Logging df_agg: CpG#4 + [20250519_004314.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004314.]: c(0, 12.4933760051342, 24.2684789512555, 38.0816229393195, 48.5842089589187, 67.6720983549562, 74.1547759694059, 82.8820147905386, 100)[20250519_004314.]: c(0, 0.00662399486579979, 0.731521048744501, 0.581622939319502, 1.4157910410813, 5.1720983549562, 0.845224030594096, 4.61798520946139, 0)[20250519_004314.]: c(NA, 0.0529919589263983, 2.926084194978, 1.550994504852, 2.8315820821626, 8.27535736792993, 1.12696537412546, 5.27769738224159, 0) + [20250519_004314.]: Entered 'hyperbolic_regression'-Function + [20250519_004314.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004314.]: Entered 'cubic_regression'-Function + [20250519_004314.]: 'cubic_regression': minmax = FALSE + [20250519_004314.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.41558040143479, 10.1649584830834, 23.9830670020905, 37.2773406385708, 50.8659103346102, 62.4341926937382, 76.3914832329149, 86.1597399215782, 100)c(2.41558040143479, 2.3350415169166, 1.0169329979095, 0.222659361429201, 0.865910334610199, 0.065807306261803, 1.39148323291489, 1.34026007842181, 0)c(NA, 18.6803321353328, 4.067731991638, 0.593758297144537, 1.7318206692204, 0.105291690018885, 1.85531097721986, 1.53172580391064, 0) - [20250519_003546.]: Logging df_agg: CpG#5 - [20250519_003546.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003546.]: c(2.41558040143479, 10.1649584830834, 23.9830670020905, 37.2773406385708, 50.8659103346102, 62.4341926937382, 76.3914832329149, 86.1597399215782, 100)[20250519_003546.]: c(2.41558040143479, 2.3350415169166, 1.0169329979095, 0.222659361429201, 0.865910334610199, 0.065807306261803, 1.39148323291489, 1.34026007842181, 0)[20250519_003546.]: c(NA, 18.6803321353328, 4.067731991638, 0.593758297144537, 1.7318206692204, 0.105291690018885, 1.85531097721986, 1.53172580391064, 0) - [20250519_003546.]: Entered 'hyperbolic_regression'-Function - [20250519_003546.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003546.]: Entered 'cubic_regression'-Function - [20250519_003546.]: 'cubic_regression': minmax = FALSE - [20250519_003546.]: # CpG-site: CpG#6 + [20250519_004314.]: Logging df_agg: CpG#5 + [20250519_004314.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004314.]: c(2.41558040143479, 10.1649584830834, 23.9830670020905, 37.2773406385708, 50.8659103346102, 62.4341926937382, 76.3914832329149, 86.1597399215782, 100)[20250519_004314.]: c(2.41558040143479, 2.3350415169166, 1.0169329979095, 0.222659361429201, 0.865910334610199, 0.065807306261803, 1.39148323291489, 1.34026007842181, 0)[20250519_004314.]: c(NA, 18.6803321353328, 4.067731991638, 0.593758297144537, 1.7318206692204, 0.105291690018885, 1.85531097721986, 1.53172580391064, 0) + [20250519_004314.]: Entered 'hyperbolic_regression'-Function + [20250519_004314.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004314.]: Entered 'cubic_regression'-Function + [20250519_004314.]: 'cubic_regression': minmax = FALSE + [20250519_004314.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.138179963459196, 11.8635989635202, 26.5108198460409, 35.3206004021833, 50.0571987473308, 64.9604436607063, 73.6690699266294, 87.1267946879604, 100)c(0.138179963459196, 0.6364010364798, 1.5108198460409, 2.1793995978167, 0.0571987473308013, 2.4604436607063, 1.3309300733706, 0.373205312039602, 0)c(NA, 5.0912082918384, 6.0432793841636, 5.81173226084453, 0.114397494661603, 3.93670985713009, 1.77457343116079, 0.426520356616688, 0) - [20250519_003546.]: Logging df_agg: CpG#6 - [20250519_003546.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003546.]: c(0.138179963459196, 11.8635989635202, 26.5108198460409, 35.3206004021833, 50.0571987473308, 64.9604436607063, 73.6690699266294, 87.1267946879604, 100)[20250519_003546.]: c(0.138179963459196, 0.6364010364798, 1.5108198460409, 2.1793995978167, 0.0571987473308013, 2.4604436607063, 1.3309300733706, 0.373205312039602, 0)[20250519_003546.]: c(NA, 5.0912082918384, 6.0432793841636, 5.81173226084453, 0.114397494661603, 3.93670985713009, 1.77457343116079, 0.426520356616688, 0) - [20250519_003546.]: Entered 'hyperbolic_regression'-Function - [20250519_003546.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003546.]: Entered 'cubic_regression'-Function - [20250519_003546.]: 'cubic_regression': minmax = FALSE - [20250519_003546.]: # CpG-site: CpG#7 + [20250519_004314.]: Logging df_agg: CpG#6 + [20250519_004314.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004314.]: c(0.138179963459196, 11.8635989635202, 26.5108198460409, 35.3206004021833, 50.0571987473308, 64.9604436607063, 73.6690699266294, 87.1267946879604, 100)[20250519_004314.]: c(0.138179963459196, 0.6364010364798, 1.5108198460409, 2.1793995978167, 0.0571987473308013, 2.4604436607063, 1.3309300733706, 0.373205312039602, 0)[20250519_004314.]: c(NA, 5.0912082918384, 6.0432793841636, 5.81173226084453, 0.114397494661603, 3.93670985713009, 1.77457343116079, 0.426520356616688, 0) + [20250519_004314.]: Entered 'hyperbolic_regression'-Function + [20250519_004314.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004315.]: Entered 'cubic_regression'-Function + [20250519_004315.]: 'cubic_regression': minmax = FALSE + [20250519_004315.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.199366215418, 24.595295361327, 37.8312095633391, 53.5590896582852, 65.9367389282431, 75.736370152841, 79.433089359524, 100)c(0, 2.300633784582, 0.404704638673, 0.331209563339101, 3.5590896582852, 3.4367389282431, 0.736370152841005, 8.066910640476, 0)c(NA, 18.405070276656, 1.618818554692, 0.883225502237603, 7.11817931657041, 5.49878228518896, 0.981826870454673, 9.21932644625828, 0) - [20250519_003546.]: Logging df_agg: CpG#7 - [20250519_003546.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003546.]: c(0, 10.199366215418, 24.595295361327, 37.8312095633391, 53.5590896582852, 65.9367389282431, 75.736370152841, 79.433089359524, 100)[20250519_003546.]: c(0, 2.300633784582, 0.404704638673, 0.331209563339101, 3.5590896582852, 3.4367389282431, 0.736370152841005, 8.066910640476, 0)[20250519_003546.]: c(NA, 18.405070276656, 1.618818554692, 0.883225502237603, 7.11817931657041, 5.49878228518896, 0.981826870454673, 9.21932644625828, 0) - [20250519_003546.]: Entered 'hyperbolic_regression'-Function - [20250519_003546.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003547.]: Entered 'cubic_regression'-Function - [20250519_003547.]: 'cubic_regression': minmax = FALSE - [20250519_003547.]: # CpG-site: CpG#8 + [20250519_004315.]: Logging df_agg: CpG#7 + [20250519_004315.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004315.]: c(0, 10.199366215418, 24.595295361327, 37.8312095633391, 53.5590896582852, 65.9367389282431, 75.736370152841, 79.433089359524, 100)[20250519_004315.]: c(0, 2.300633784582, 0.404704638673, 0.331209563339101, 3.5590896582852, 3.4367389282431, 0.736370152841005, 8.066910640476, 0)[20250519_004315.]: c(NA, 18.405070276656, 1.618818554692, 0.883225502237603, 7.11817931657041, 5.49878228518896, 0.981826870454673, 9.21932644625828, 0) + [20250519_004315.]: Entered 'hyperbolic_regression'-Function + [20250519_004315.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004315.]: Entered 'cubic_regression'-Function + [20250519_004315.]: 'cubic_regression': minmax = FALSE + [20250519_004315.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.80067549526643, 9.27534686003087, 25.4762624089952, 34.0122098045585, 51.784270967861, 64.6732380258789, 78.4327054483192, 81.3427308951795, 100)c(2.80067549526643, 3.22465313996913, 0.476262408995201, 3.4877901954415, 1.784270967861, 2.1732380258789, 3.4327054483192, 6.1572691048205, 0)c(NA, 25.797225119753, 1.9050496359808, 9.30077385451066, 3.568541935722, 3.47718084140624, 4.57694059775893, 7.03687897693771, 0) - [20250519_003547.]: Logging df_agg: CpG#8 - [20250519_003547.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003547.]: c(2.80067549526643, 9.27534686003087, 25.4762624089952, 34.0122098045585, 51.784270967861, 64.6732380258789, 78.4327054483192, 81.3427308951795, 100)[20250519_003547.]: c(2.80067549526643, 3.22465313996913, 0.476262408995201, 3.4877901954415, 1.784270967861, 2.1732380258789, 3.4327054483192, 6.1572691048205, 0)[20250519_003547.]: c(NA, 25.797225119753, 1.9050496359808, 9.30077385451066, 3.568541935722, 3.47718084140624, 4.57694059775893, 7.03687897693771, 0) - [20250519_003547.]: Entered 'hyperbolic_regression'-Function - [20250519_003547.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003547.]: Entered 'cubic_regression'-Function - [20250519_003547.]: 'cubic_regression': minmax = FALSE - [20250519_003547.]: # CpG-site: CpG#9 + [20250519_004315.]: Logging df_agg: CpG#8 + [20250519_004315.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004315.]: c(2.80067549526643, 9.27534686003087, 25.4762624089952, 34.0122098045585, 51.784270967861, 64.6732380258789, 78.4327054483192, 81.3427308951795, 100)[20250519_004315.]: c(2.80067549526643, 3.22465313996913, 0.476262408995201, 3.4877901954415, 1.784270967861, 2.1732380258789, 3.4327054483192, 6.1572691048205, 0)[20250519_004315.]: c(NA, 25.797225119753, 1.9050496359808, 9.30077385451066, 3.568541935722, 3.47718084140624, 4.57694059775893, 7.03687897693771, 0) + [20250519_004315.]: Entered 'hyperbolic_regression'-Function + [20250519_004315.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004315.]: Entered 'cubic_regression'-Function + [20250519_004315.]: 'cubic_regression': minmax = FALSE + [20250519_004315.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.5081558897243, 26.9163121098627, 36.8332915301825, 52.00955, 64.8927781893359, 74.566810799042, 84.5291854856128, 100)c(0, 1.9918441102757, 1.9163121098627, 0.6667084698175, 2.00955, 2.3927781893359, 0.433189200957997, 2.9708145143872, 0)c(NA, 15.9347528822056, 7.66524843945081, 1.77788925284667, 4.01909999999999, 3.82844510293744, 0.57758560127733, 3.39521658787109, 0) - [20250519_003547.]: Logging df_agg: CpG#9 - [20250519_003547.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003547.]: c(0, 10.5081558897243, 26.9163121098627, 36.8332915301825, 52.00955, 64.8927781893359, 74.566810799042, 84.5291854856128, 100)[20250519_003547.]: c(0, 1.9918441102757, 1.9163121098627, 0.6667084698175, 2.00955, 2.3927781893359, 0.433189200957997, 2.9708145143872, 0)[20250519_003547.]: c(NA, 15.9347528822056, 7.66524843945081, 1.77788925284667, 4.01909999999999, 3.82844510293744, 0.57758560127733, 3.39521658787109, 0) - [20250519_003547.]: Entered 'hyperbolic_regression'-Function - [20250519_003547.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003547.]: Entered 'cubic_regression'-Function - [20250519_003547.]: 'cubic_regression': minmax = FALSE - [20250519_003547.]: # CpG-site: row_means + [20250519_004315.]: Logging df_agg: CpG#9 + [20250519_004315.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004315.]: c(0, 10.5081558897243, 26.9163121098627, 36.8332915301825, 52.00955, 64.8927781893359, 74.566810799042, 84.5291854856128, 100)[20250519_004315.]: c(0, 1.9918441102757, 1.9163121098627, 0.6667084698175, 2.00955, 2.3927781893359, 0.433189200957997, 2.9708145143872, 0)[20250519_004315.]: c(NA, 15.9347528822056, 7.66524843945081, 1.77788925284667, 4.01909999999999, 3.82844510293744, 0.57758560127733, 3.39521658787109, 0) + [20250519_004315.]: Entered 'hyperbolic_regression'-Function + [20250519_004315.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004315.]: Entered 'cubic_regression'-Function + [20250519_004315.]: 'cubic_regression': minmax = FALSE + [20250519_004315.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.290936038655697, 11.0412364555656, 25.408146358228, 36.5243683648753, 50.7348788337891, 65.3135169308955, 75.5342663138126, 83.2411177153911, 100)c(0.290936038655697, 1.4587635444344, 0.408146358227999, 0.975631635124699, 0.734878833789097, 2.8135169308955, 0.534266313812594, 4.2588822846089, 0)c(NA, 11.6701083554752, 1.63258543291199, 2.60168436033253, 1.46975766757819, 4.5016270894328, 0.712355085083459, 4.86729403955303, 0) - [20250519_003547.]: Logging df_agg: row_means - [20250519_003547.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003547.]: c(0.290936038655697, 11.0412364555656, 25.408146358228, 36.5243683648753, 50.7348788337891, 65.3135169308955, 75.5342663138126, 83.2411177153911, 100)[20250519_003547.]: c(0.290936038655697, 1.4587635444344, 0.408146358227999, 0.975631635124699, 0.734878833789097, 2.8135169308955, 0.534266313812594, 4.2588822846089, 0)[20250519_003547.]: c(NA, 11.6701083554752, 1.63258543291199, 2.60168436033253, 1.46975766757819, 4.5016270894328, 0.712355085083459, 4.86729403955303, 0) - [20250519_003547.]: Entered 'hyperbolic_regression'-Function - [20250519_003547.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003547.]: Entered 'cubic_regression'-Function - [20250519_003547.]: 'cubic_regression': minmax = FALSE - [20250519_003548.]: Entered 'solving_equations'-Function - [20250519_003548.]: Solving cubic regression for CpG#1 + [20250519_004315.]: Logging df_agg: row_means + [20250519_004315.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004315.]: c(0.290936038655697, 11.0412364555656, 25.408146358228, 36.5243683648753, 50.7348788337891, 65.3135169308955, 75.5342663138126, 83.2411177153911, 100)[20250519_004315.]: c(0.290936038655697, 1.4587635444344, 0.408146358227999, 0.975631635124699, 0.734878833789097, 2.8135169308955, 0.534266313812594, 4.2588822846089, 0)[20250519_004315.]: c(NA, 11.6701083554752, 1.63258543291199, 2.60168436033253, 1.46975766757819, 4.5016270894328, 0.712355085083459, 4.86729403955303, 0) + [20250519_004315.]: Entered 'hyperbolic_regression'-Function + [20250519_004315.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004316.]: Entered 'cubic_regression'-Function + [20250519_004316.]: 'cubic_regression': minmax = FALSE + [20250519_004316.]: Entered 'solving_equations'-Function + [20250519_004316.]: Solving cubic regression for CpG#1 Coefficients: -1.036Coefficients: 0.784Coefficients: -0.006Coefficients: 0 - [20250519_003548.]: Samplename: 0 + [20250519_004316.]: Samplename: 0 Root: 1.335 --> Root in between the borders! Added to results. Coefficients: -8.34133333333333Coefficients: 0.784Coefficients: -0.006Coefficients: 0 - [20250519_003548.]: Samplename: 12.5 + [20250519_004316.]: Samplename: 12.5 Root: 11.684 --> Root in between the borders! Added to results. Coefficients: -15.388Coefficients: 0.784Coefficients: -0.006Coefficients: 0 - [20250519_003548.]: Samplename: 25 + [20250519_004316.]: Samplename: 25 Root: 24.056 --> Root in between the borders! Added to results. Coefficients: -24.28Coefficients: 0.784Coefficients: -0.006Coefficients: 0 - [20250519_003548.]: Samplename: 37.5 + [20250519_004316.]: Samplename: 37.5 Root: 50.441 --> Root in between the borders! Added to results. Coefficients: -34.9005Coefficients: 0.784Coefficients: -0.006Coefficients: 0 - [20250519_003548.]: Samplename: 50 + [20250519_004316.]: Samplename: 50 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -46.354Coefficients: 0.784Coefficients: -0.006Coefficients: 0 - [20250519_003548.]: Samplename: 62.5 + [20250519_004316.]: Samplename: 62.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -55.893Coefficients: 0.784Coefficients: -0.006Coefficients: 0 - [20250519_003548.]: Samplename: 75 + [20250519_004316.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -63.098Coefficients: 0.784Coefficients: -0.006Coefficients: 0 - [20250519_003548.]: Samplename: 87.5 + [20250519_004316.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -91.046Coefficients: 0.784Coefficients: -0.006Coefficients: 0 - [20250519_003548.]: Samplename: 100 + [20250519_004316.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA - [20250519_003548.]: Solving cubic regression for CpG#2 + [20250519_004316.]: Solving cubic regression for CpG#2 Coefficients: -0.283000000000001Coefficients: 0.515Coefficients: 0.003Coefficients: 0 - [20250519_003548.]: Samplename: 0 + [20250519_004316.]: Samplename: 0 Root: 0.548 --> Root in between the borders! Added to results. Coefficients: -6.33966666666667Coefficients: 0.515Coefficients: 0.003Coefficients: 0 - [20250519_003548.]: Samplename: 12.5 + [20250519_004316.]: Samplename: 12.5 Root: 11.535 --> Root in between the borders! Added to results. Coefficients: -15.939Coefficients: 0.515Coefficients: 0.003Coefficients: 0 - [20250519_003548.]: Samplename: 25 + [20250519_004316.]: Samplename: 25 Root: 26.774 --> Root in between the borders! Added to results. Coefficients: -22.337Coefficients: 0.515Coefficients: 0.003Coefficients: 0 - [20250519_003548.]: Samplename: 37.5 + [20250519_004316.]: Samplename: 37.5 Root: 35.875 --> Root in between the borders! Added to results. Coefficients: -32.228Coefficients: 0.515Coefficients: 0.003Coefficients: 0 - [20250519_003548.]: Samplename: 50 + [20250519_004316.]: Samplename: 50 Root: 48.74 --> Root in between the borders! Added to results. Coefficients: -49.963Coefficients: 0.515Coefficients: 0.003Coefficients: 0 - [20250519_003548.]: Samplename: 62.5 + [20250519_004316.]: Samplename: 62.5 Root: 69.156 --> Root in between the borders! Added to results. Coefficients: -58.9655Coefficients: 0.515Coefficients: 0.003Coefficients: 0 - [20250519_003548.]: Samplename: 75 + [20250519_004316.]: Samplename: 75 Root: 78.552 --> Root in between the borders! Added to results. Coefficients: -68.8363333333333Coefficients: 0.515Coefficients: 0.003Coefficients: 0 - [20250519_003548.]: Samplename: 87.5 + [20250519_004316.]: Samplename: 87.5 Root: 88.272 --> Root in between the borders! Added to results. Coefficients: -90.577Coefficients: 0.515Coefficients: 0.003Coefficients: 0 - [20250519_003548.]: Samplename: 100 + [20250519_004316.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 107.97 --> '100 < root < 110' --> substitute 100 - [20250519_003548.]: Solving cubic regression for CpG#3 + [20250519_004316.]: Solving cubic regression for CpG#3 Coefficients: -0.903Coefficients: 0.628Coefficients: -0.001Coefficients: 0 - [20250519_003548.]: Samplename: 0 + [20250519_004316.]: Samplename: 0 Root: 1.441 --> Root in between the borders! Added to results. Coefficients: -6.573Coefficients: 0.628Coefficients: -0.001Coefficients: 0 - [20250519_003548.]: Samplename: 12.5 + [20250519_004316.]: Samplename: 12.5 Root: 10.647 --> Root in between the borders! Added to results. Coefficients: -15.429Coefficients: 0.628Coefficients: -0.001Coefficients: 0 - [20250519_003548.]: Samplename: 25 + [20250519_004316.]: Samplename: 25 Root: 25.613 --> Root in between the borders! Added to results. Coefficients: -22.613Coefficients: 0.628Coefficients: -0.001Coefficients: 0 - [20250519_003548.]: Samplename: 37.5 + [20250519_004316.]: Samplename: 37.5 Root: 38.35 --> Root in between the borders! Added to results. Coefficients: -32.7755Coefficients: 0.628Coefficients: -0.001Coefficients: 0 - [20250519_003548.]: Samplename: 50 + [20250519_004316.]: Samplename: 50 Root: 57.445 --> Root in between the borders! Added to results. Coefficients: -43.889Coefficients: 0.628Coefficients: -0.001Coefficients: 0 - [20250519_003548.]: Samplename: 62.5 + [20250519_004316.]: Samplename: 62.5 Root: 80.105 --> Root in between the borders! Added to results. Coefficients: -54.9755Coefficients: 0.628Coefficients: -0.001Coefficients: 0 - [20250519_003548.]: Samplename: 75 + [20250519_004316.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. @@ -4574,40 +4571,40 @@ Root: 105.145 --> '100 < root < 110' --> substitute 100 Coefficients: -57.6563333333333Coefficients: 0.628Coefficients: -0.001Coefficients: 0 - [20250519_003548.]: Samplename: 87.5 + [20250519_004316.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -80.665Coefficients: 0.628Coefficients: -0.001Coefficients: 0 - [20250519_003548.]: Samplename: 100 + [20250519_004316.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA - [20250519_003548.]: Solving cubic regression for CpG#4 + [20250519_004316.]: Solving cubic regression for CpG#4 Coefficients: -0.597Coefficients: 0.697Coefficients: -0.002Coefficients: 0 - [20250519_003548.]: Samplename: 0 + [20250519_004316.]: Samplename: 0 Root: 0.859 --> Root in between the borders! Added to results. Coefficients: -8.25233333333333Coefficients: 0.697Coefficients: -0.002Coefficients: 0 - [20250519_003548.]: Samplename: 12.5 + [20250519_004316.]: Samplename: 12.5 Root: 12.272 --> Root in between the borders! Added to results. Coefficients: -15.803Coefficients: 0.697Coefficients: -0.002Coefficients: 0 - [20250519_003548.]: Samplename: 25 + [20250519_004316.]: Samplename: 25 Root: 24.378 --> Root in between the borders! Added to results. Coefficients: -25.527Coefficients: 0.697Coefficients: -0.002Coefficients: 0 - [20250519_003548.]: Samplename: 37.5 + [20250519_004316.]: Samplename: 37.5 Root: 41.587 --> Root in between the borders! Added to results. Coefficients: -33.6365Coefficients: 0.697Coefficients: -0.002Coefficients: 0 - [20250519_003548.]: Samplename: 50 + [20250519_004316.]: Samplename: 50 Root: 57.868 --> Root in between the borders! Added to results. Coefficients: -50.255Coefficients: 0.697Coefficients: -0.002Coefficients: 0 - [20250519_003548.]: Samplename: 62.5 + [20250519_004316.]: Samplename: 62.5 ## WARNING ## No fitting root within the borders found. @@ -4615,91 +4612,91 @@ Root: 101.893 --> '100 < root < 110' --> substitute 100 Coefficients: -56.539Coefficients: 0.697Coefficients: -0.002Coefficients: 0 - [20250519_003548.]: Samplename: 75 + [20250519_004316.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -65.5923333333333Coefficients: 0.697Coefficients: -0.002Coefficients: 0 - [20250519_003548.]: Samplename: 87.5 + [20250519_004316.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -88.321Coefficients: 0.697Coefficients: -0.002Coefficients: 0 - [20250519_003548.]: Samplename: 100 + [20250519_004316.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA - [20250519_003548.]: Solving cubic regression for CpG#5 + [20250519_004316.]: Solving cubic regression for CpG#5 Coefficients: -0.624000000000001Coefficients: 0.422Coefficients: 0.004Coefficients: 0 - [20250519_003548.]: Samplename: 0 + [20250519_004316.]: Samplename: 0 Root: 1.459 --> Root in between the borders! Added to results. Coefficients: -4.768Coefficients: 0.422Coefficients: 0.004Coefficients: 0 - [20250519_003548.]: Samplename: 12.5 + [20250519_004316.]: Samplename: 12.5 Root: 10.294 --> Root in between the borders! Added to results. Coefficients: -12.726Coefficients: 0.422Coefficients: 0.004Coefficients: 0 - [20250519_003548.]: Samplename: 25 + [20250519_004316.]: Samplename: 25 Root: 24.477 --> Root in between the borders! Added to results. Coefficients: -21.16Coefficients: 0.422Coefficients: 0.004Coefficients: 0 - [20250519_003548.]: Samplename: 37.5 + [20250519_004316.]: Samplename: 37.5 Root: 37.097 --> Root in between the borders! Added to results. Coefficients: -30.6955Coefficients: 0.422Coefficients: 0.004Coefficients: 0 - [20250519_003548.]: Samplename: 50 + [20250519_004316.]: Samplename: 50 Root: 49.507 --> Root in between the borders! Added to results. Coefficients: -39.658Coefficients: 0.422Coefficients: 0.004Coefficients: 0 - [20250519_003548.]: Samplename: 62.5 + [20250519_004316.]: Samplename: 62.5 Root: 59.931 --> Root in between the borders! Added to results. Coefficients: -51.683Coefficients: 0.422Coefficients: 0.004Coefficients: 0 - [20250519_003548.]: Samplename: 75 + [20250519_004316.]: Samplename: 75 Root: 72.563 --> Root in between the borders! Added to results. Coefficients: -61.0146666666667Coefficients: 0.422Coefficients: 0.004Coefficients: 0 - [20250519_003548.]: Samplename: 87.5 + [20250519_004316.]: Samplename: 87.5 Root: 81.549 --> Root in between the borders! Added to results. Coefficients: -76.07Coefficients: 0.422Coefficients: 0.004Coefficients: 0 - [20250519_003548.]: Samplename: 100 + [20250519_004316.]: Samplename: 100 Root: 94.898 --> Root in between the borders! Added to results. - [20250519_003548.]: Solving cubic regression for CpG#6 + [20250519_004316.]: Solving cubic regression for CpG#6 Coefficients: -0.196000000000001Coefficients: 0.561Coefficients: 0.001Coefficients: 0 - [20250519_003548.]: Samplename: 0 + [20250519_004316.]: Samplename: 0 Root: 0.349 --> Root in between the borders! Added to results. Coefficients: -6.73866666666667Coefficients: 0.561Coefficients: 0.001Coefficients: 0 - [20250519_003548.]: Samplename: 12.5 + [20250519_004316.]: Samplename: 12.5 Root: 11.765 --> Root in between the borders! Added to results. Coefficients: -15.888Coefficients: 0.561Coefficients: 0.001Coefficients: 0 - [20250519_003548.]: Samplename: 25 + [20250519_004316.]: Samplename: 25 Root: 27.02 --> Root in between the borders! Added to results. Coefficients: -22Coefficients: 0.561Coefficients: 0.001Coefficients: 0 - [20250519_003548.]: Samplename: 37.5 + [20250519_004316.]: Samplename: 37.5 Root: 36.802 --> Root in between the borders! Added to results. Coefficients: -33.4445Coefficients: 0.561Coefficients: 0.001Coefficients: 0 - [20250519_003548.]: Samplename: 50 + [20250519_004316.]: Samplename: 50 Root: 54.35 --> Root in between the borders! Added to results. Coefficients: -46.9Coefficients: 0.561Coefficients: 0.001Coefficients: 0 - [20250519_003548.]: Samplename: 62.5 + [20250519_004316.]: Samplename: 62.5 Root: 73.873 --> Root in between the borders! Added to results. Coefficients: -55.832Coefficients: 0.561Coefficients: 0.001Coefficients: 0 - [20250519_003548.]: Samplename: 75 + [20250519_004316.]: Samplename: 75 Root: 86.259 --> Root in between the borders! Added to results. Coefficients: -71.5453333333333Coefficients: 0.561Coefficients: 0.001Coefficients: 0 - [20250519_003548.]: Samplename: 87.5 + [20250519_004316.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. @@ -4707,188 +4704,188 @@ Root: 107.089 --> '100 < root < 110' --> substitute 100 Coefficients: -89.656Coefficients: 0.561Coefficients: 0.001Coefficients: 0 - [20250519_003548.]: Samplename: 100 + [20250519_004316.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA - [20250519_003548.]: Solving cubic regression for CpG#7 + [20250519_004316.]: Solving cubic regression for CpG#7 Coefficients: -1.215Coefficients: 0.58Coefficients: -0.004Coefficients: 0 - [20250519_003548.]: Samplename: 0 + [20250519_004316.]: Samplename: 0 Root: 2.126 --> Root in between the borders! Added to results. Coefficients: -5.39566666666667Coefficients: 0.58Coefficients: -0.004Coefficients: 0 - [20250519_003548.]: Samplename: 12.5 + [20250519_004316.]: Samplename: 12.5 Root: 9.991 --> Root in between the borders! Added to results. Coefficients: -11.265Coefficients: 0.58Coefficients: -0.004Coefficients: 0 - [20250519_003548.]: Samplename: 25 + [20250519_004316.]: Samplename: 25 Root: 23.104 --> Root in between the borders! Added to results. Coefficients: -17.451Coefficients: 0.58Coefficients: -0.004Coefficients: 0 - [20250519_003548.]: Samplename: 37.5 + [20250519_004316.]: Samplename: 37.5 Root: 42.609 --> Root in between the borders! Added to results. Coefficients: -26.0315Coefficients: 0.58Coefficients: -0.004Coefficients: 0 - [20250519_003548.]: Samplename: 50 + [20250519_004316.]: Samplename: 50 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -33.965Coefficients: 0.58Coefficients: -0.004Coefficients: 0 - [20250519_003548.]: Samplename: 62.5 + [20250519_004316.]: Samplename: 62.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -41.169Coefficients: 0.58Coefficients: -0.004Coefficients: 0 - [20250519_003548.]: Samplename: 75 + [20250519_004316.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -44.1356666666667Coefficients: 0.58Coefficients: -0.004Coefficients: 0 - [20250519_003548.]: Samplename: 87.5 + [20250519_004316.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -67.223Coefficients: 0.58Coefficients: -0.004Coefficients: 0 - [20250519_003548.]: Samplename: 100 + [20250519_004316.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA - [20250519_003548.]: Solving cubic regression for CpG#8 + [20250519_004316.]: Solving cubic regression for CpG#8 Coefficients: -1.096Coefficients: 0.535Coefficients: 0.004Coefficients: 0 - [20250519_003548.]: Samplename: 0 + [20250519_004316.]: Samplename: 0 Root: 2.018 --> Root in between the borders! Added to results. Coefficients: -5.44666666666667Coefficients: 0.535Coefficients: 0.004Coefficients: 0 - [20250519_003548.]: Samplename: 12.5 + [20250519_004316.]: Samplename: 12.5 Root: 9.505 --> Root in between the borders! Added to results. Coefficients: -16.93Coefficients: 0.535Coefficients: 0.004Coefficients: 0 - [20250519_003548.]: Samplename: 25 + [20250519_004316.]: Samplename: 25 Root: 26.424 --> Root in between the borders! Added to results. Coefficients: -23.35Coefficients: 0.535Coefficients: 0.004Coefficients: 0 - [20250519_003548.]: Samplename: 37.5 + [20250519_004316.]: Samplename: 37.5 Root: 34.662 --> Root in between the borders! Added to results. Coefficients: -37.625Coefficients: 0.535Coefficients: 0.004Coefficients: 0 - [20250519_003548.]: Samplename: 50 + [20250519_004316.]: Samplename: 50 Root: 50.932 --> Root in between the borders! Added to results. Coefficients: -48.826Coefficients: 0.535Coefficients: 0.004Coefficients: 0 - [20250519_003548.]: Samplename: 62.5 + [20250519_004316.]: Samplename: 62.5 Root: 62.271 --> Root in between the borders! Added to results. Coefficients: -61.6675Coefficients: 0.535Coefficients: 0.004Coefficients: 0 - [20250519_003548.]: Samplename: 75 + [20250519_004316.]: Samplename: 75 Root: 74.154 --> Root in between the borders! Added to results. Coefficients: -64.51Coefficients: 0.535Coefficients: 0.004Coefficients: 0 - [20250519_003548.]: Samplename: 87.5 + [20250519_004316.]: Samplename: 87.5 Root: 76.651 --> Root in between the borders! Added to results. Coefficients: -86.06Coefficients: 0.535Coefficients: 0.004Coefficients: 0 - [20250519_003548.]: Samplename: 100 + [20250519_004316.]: Samplename: 100 Root: 94.331 --> Root in between the borders! Added to results. - [20250519_003548.]: Solving cubic regression for CpG#9 + [20250519_004316.]: Solving cubic regression for CpG#9 Coefficients: -0.99Coefficients: 0.679Coefficients: -0.005Coefficients: 0 - [20250519_003548.]: Samplename: 0 + [20250519_004316.]: Samplename: 0 Root: 1.474 --> Root in between the borders! Added to results. Coefficients: -6.396Coefficients: 0.679Coefficients: -0.005Coefficients: 0 - [20250519_003548.]: Samplename: 12.5 + [20250519_004316.]: Samplename: 12.5 Root: 10.183 --> Root in between the borders! Added to results. Coefficients: -14.706Coefficients: 0.679Coefficients: -0.005Coefficients: 0 - [20250519_003548.]: Samplename: 25 + [20250519_004316.]: Samplename: 25 Root: 27.044 --> Root in between the borders! Added to results. Coefficients: -20.624Coefficients: 0.679Coefficients: -0.005Coefficients: 0 - [20250519_003548.]: Samplename: 37.5 + [20250519_004316.]: Samplename: 37.5 Root: 45.863 --> Root in between the borders! Added to results. Coefficients: -31.396Coefficients: 0.679Coefficients: -0.005Coefficients: 0 - [20250519_003548.]: Samplename: 50 + [20250519_004316.]: Samplename: 50 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -42.686Coefficients: 0.679Coefficients: -0.005Coefficients: 0 - [20250519_003548.]: Samplename: 62.5 + [20250519_004316.]: Samplename: 62.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -52.9035Coefficients: 0.679Coefficients: -0.005Coefficients: 0 - [20250519_003548.]: Samplename: 75 + [20250519_004316.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -65.4926666666667Coefficients: 0.679Coefficients: -0.005Coefficients: 0 - [20250519_003548.]: Samplename: 87.5 + [20250519_004316.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -92.99Coefficients: 0.679Coefficients: -0.005Coefficients: 0 - [20250519_003548.]: Samplename: 100 + [20250519_004316.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA - [20250519_003548.]: Solving cubic regression for row_means + [20250519_004316.]: Solving cubic regression for row_means Coefficients: -0.771111111111111Coefficients: 0.6Coefficients: -0.001Coefficients: 0 - [20250519_003548.]: Samplename: 0 + [20250519_004316.]: Samplename: 0 Root: 1.288 --> Root in between the borders! Added to results. Coefficients: -6.47237037037037Coefficients: 0.6Coefficients: -0.001Coefficients: 0 - [20250519_003548.]: Samplename: 12.5 + [20250519_004316.]: Samplename: 12.5 Root: 10.989 --> Root in between the borders! Added to results. Coefficients: -14.8971111111111Coefficients: 0.6Coefficients: -0.001Coefficients: 0 - [20250519_003548.]: Samplename: 25 + [20250519_004316.]: Samplename: 25 Root: 25.951 --> Root in between the borders! Added to results. Coefficients: -22.1491111111111Coefficients: 0.6Coefficients: -0.001Coefficients: 0 - [20250519_003548.]: Samplename: 37.5 + [20250519_004316.]: Samplename: 37.5 Root: 39.518 --> Root in between the borders! Added to results. Coefficients: -32.5258888888889Coefficients: 0.6Coefficients: -0.001Coefficients: 0 - [20250519_003548.]: Samplename: 50 + [20250519_004316.]: Samplename: 50 Root: 60.262 --> Root in between the borders! Added to results. Coefficients: -44.7217777777778Coefficients: 0.6Coefficients: -0.001Coefficients: 0 - [20250519_003548.]: Samplename: 62.5 + [20250519_004316.]: Samplename: 62.5 Root: 87.213 --> Root in between the borders! Added to results. Coefficients: -54.4031111111111Coefficients: 0.6Coefficients: -0.001Coefficients: 0 - [20250519_003548.]: Samplename: 75 + [20250519_004316.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -62.4312592592593Coefficients: 0.6Coefficients: -0.001Coefficients: 0 - [20250519_003548.]: Samplename: 87.5 + [20250519_004316.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -84.7342222222222Coefficients: 0.6Coefficients: -0.001Coefficients: 0 - [20250519_003548.]: Samplename: 100 + [20250519_004316.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA - [20250519_003548.]: + [20250519_004316.]: ### Starting with regression calculations ### - [20250519_003548.]: Entered 'regression_type1'-Function + [20250519_004316.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : @@ -5413,525 +5410,525 @@ singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient - [20250519_003549.]: # CpG-site: CpG#1 + [20250519_004317.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5)c(1.3350694679926, 11.6842678967181, 24.056490215457, 50.4411280642076)c(1.3350694679926, 0.815732103281899, 0.943509784543, 12.9411280642076)c(NA, 6.52585682625519, 3.774039138172, 34.5096748378869) - [20250519_003549.]: Logging df_agg: CpG#1 - [20250519_003549.]: c(0, 12.5, 25, 37.5)[20250519_003549.]: c(1.3350694679926, 11.6842678967181, 24.056490215457, 50.4411280642076)[20250519_003549.]: c(1.3350694679926, 0.815732103281899, 0.943509784543, 12.9411280642076)[20250519_003549.]: c(NA, 6.52585682625519, 3.774039138172, 34.5096748378869) - [20250519_003549.]: Entered 'hyperbolic_regression'-Function - [20250519_003549.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003549.]: Entered 'cubic_regression'-Function - [20250519_003549.]: 'cubic_regression': minmax = FALSE - [20250519_003549.]: # CpG-site: CpG#2 + [20250519_004317.]: Logging df_agg: CpG#1 + [20250519_004317.]: c(0, 12.5, 25, 37.5)[20250519_004317.]: c(1.3350694679926, 11.6842678967181, 24.056490215457, 50.4411280642076)[20250519_004317.]: c(1.3350694679926, 0.815732103281899, 0.943509784543, 12.9411280642076)[20250519_004317.]: c(NA, 6.52585682625519, 3.774039138172, 34.5096748378869) + [20250519_004317.]: Entered 'hyperbolic_regression'-Function + [20250519_004317.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004317.]: Entered 'cubic_regression'-Function + [20250519_004317.]: 'cubic_regression': minmax = FALSE + [20250519_004317.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.547766708541827, 11.5349537694669, 26.7737761718445, 35.875449331591, 48.7401699196391, 69.1560032174257, 78.551969470104, 88.2723976336729, 100)c(0.547766708541827, 0.965046230533099, 1.7737761718445, 1.624550668409, 1.2598300803609, 6.65600321742571, 3.551969470104, 0.772397633672895, 0)c(NA, 7.72036984426479, 7.095104687378, 4.33213511575733, 2.5196601607218, 10.6496051478811, 4.735959293472, 0.882740152769023, 0) - [20250519_003549.]: Logging df_agg: CpG#2 - [20250519_003549.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003549.]: c(0.547766708541827, 11.5349537694669, 26.7737761718445, 35.875449331591, 48.7401699196391, 69.1560032174257, 78.551969470104, 88.2723976336729, 100)[20250519_003549.]: c(0.547766708541827, 0.965046230533099, 1.7737761718445, 1.624550668409, 1.2598300803609, 6.65600321742571, 3.551969470104, 0.772397633672895, 0)[20250519_003549.]: c(NA, 7.72036984426479, 7.095104687378, 4.33213511575733, 2.5196601607218, 10.6496051478811, 4.735959293472, 0.882740152769023, 0) - [20250519_003549.]: Entered 'hyperbolic_regression'-Function - [20250519_003549.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003549.]: Entered 'cubic_regression'-Function - [20250519_003549.]: 'cubic_regression': minmax = FALSE - [20250519_003549.]: # CpG-site: CpG#3 + [20250519_004317.]: Logging df_agg: CpG#2 + [20250519_004317.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004317.]: c(0.547766708541827, 11.5349537694669, 26.7737761718445, 35.875449331591, 48.7401699196391, 69.1560032174257, 78.551969470104, 88.2723976336729, 100)[20250519_004317.]: c(0.547766708541827, 0.965046230533099, 1.7737761718445, 1.624550668409, 1.2598300803609, 6.65600321742571, 3.551969470104, 0.772397633672895, 0)[20250519_004317.]: c(NA, 7.72036984426479, 7.095104687378, 4.33213511575733, 2.5196601607218, 10.6496051478811, 4.735959293472, 0.882740152769023, 0) + [20250519_004317.]: Entered 'hyperbolic_regression'-Function + [20250519_004317.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004317.]: Entered 'cubic_regression'-Function + [20250519_004317.]: 'cubic_regression': minmax = FALSE + [20250519_004317.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75)c(1.44120553086336, 10.6470702300701, 25.6131070939596, 38.3498594232174, 57.444937683935, 80.104724288839, 100)c(1.44120553086336, 1.8529297699299, 0.613107093959599, 0.849859423217403, 7.444937683935, 17.604724288839, 25)c(NA, 14.8234381594392, 2.45242837583839, 2.26629179524641, 14.88987536787, 28.1675588621424, 33.3333333333333) - [20250519_003549.]: Logging df_agg: CpG#3 - [20250519_003549.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75)[20250519_003549.]: c(1.44120553086336, 10.6470702300701, 25.6131070939596, 38.3498594232174, 57.444937683935, 80.104724288839, 100)[20250519_003549.]: c(1.44120553086336, 1.8529297699299, 0.613107093959599, 0.849859423217403, 7.444937683935, 17.604724288839, 25)[20250519_003549.]: c(NA, 14.8234381594392, 2.45242837583839, 2.26629179524641, 14.88987536787, 28.1675588621424, 33.3333333333333) - [20250519_003549.]: Entered 'hyperbolic_regression'-Function - [20250519_003549.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003549.]: Entered 'cubic_regression'-Function - [20250519_003549.]: 'cubic_regression': minmax = FALSE - [20250519_003549.]: # CpG-site: CpG#4 + [20250519_004317.]: Logging df_agg: CpG#3 + [20250519_004317.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75)[20250519_004317.]: c(1.44120553086336, 10.6470702300701, 25.6131070939596, 38.3498594232174, 57.444937683935, 80.104724288839, 100)[20250519_004317.]: c(1.44120553086336, 1.8529297699299, 0.613107093959599, 0.849859423217403, 7.444937683935, 17.604724288839, 25)[20250519_004317.]: c(NA, 14.8234381594392, 2.45242837583839, 2.26629179524641, 14.88987536787, 28.1675588621424, 33.3333333333333) + [20250519_004317.]: Entered 'hyperbolic_regression'-Function + [20250519_004317.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004318.]: Entered 'cubic_regression'-Function + [20250519_004318.]: 'cubic_regression': minmax = FALSE + [20250519_004318.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5)c(0.858643525693588, 12.2719279243844, 24.3781797668421, 41.5866572861968, 57.86781708526, 100)c(0.858643525693588, 0.2280720756156, 0.621820233157901, 4.0866572861968, 7.86781708526, 37.5)c(NA, 1.8245766049248, 2.48728093263161, 10.8977527631915, 15.73563417052, 60) - [20250519_003549.]: Logging df_agg: CpG#4 - [20250519_003549.]: c(0, 12.5, 25, 37.5, 50, 62.5)[20250519_003549.]: c(0.858643525693588, 12.2719279243844, 24.3781797668421, 41.5866572861968, 57.86781708526, 100)[20250519_003549.]: c(0.858643525693588, 0.2280720756156, 0.621820233157901, 4.0866572861968, 7.86781708526, 37.5)[20250519_003549.]: c(NA, 1.8245766049248, 2.48728093263161, 10.8977527631915, 15.73563417052, 60) - [20250519_003549.]: Entered 'hyperbolic_regression'-Function - [20250519_003549.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003550.]: Entered 'cubic_regression'-Function - [20250519_003550.]: 'cubic_regression': minmax = FALSE - [20250519_003550.]: # CpG-site: CpG#5 + [20250519_004318.]: Logging df_agg: CpG#4 + [20250519_004318.]: c(0, 12.5, 25, 37.5, 50, 62.5)[20250519_004318.]: c(0.858643525693588, 12.2719279243844, 24.3781797668421, 41.5866572861968, 57.86781708526, 100)[20250519_004318.]: c(0.858643525693588, 0.2280720756156, 0.621820233157901, 4.0866572861968, 7.86781708526, 37.5)[20250519_004318.]: c(NA, 1.8245766049248, 2.48728093263161, 10.8977527631915, 15.73563417052, 60) + [20250519_004318.]: Entered 'hyperbolic_regression'-Function + [20250519_004318.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004318.]: Entered 'cubic_regression'-Function + [20250519_004318.]: 'cubic_regression': minmax = FALSE + [20250519_004318.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.45850947960108, 10.2941313684311, 24.4773429557174, 37.0974401416089, 49.5067234953282, 59.9312428933937, 72.5628584782902, 81.5490289118528, 94.898442253889)c(1.45850947960108, 2.2058686315689, 0.5226570442826, 0.402559858391101, 0.493276504671798, 2.5687571066063, 2.4371415217098, 5.9509710881472, 5.101557746111)c(NA, 17.6469490525512, 2.0906281771304, 1.0734929557096, 0.986553009343595, 4.11001137057008, 3.24952202894641, 6.80110981502537, 5.101557746111) - [20250519_003550.]: Logging df_agg: CpG#5 - [20250519_003550.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003550.]: c(1.45850947960108, 10.2941313684311, 24.4773429557174, 37.0974401416089, 49.5067234953282, 59.9312428933937, 72.5628584782902, 81.5490289118528, 94.898442253889)[20250519_003550.]: c(1.45850947960108, 2.2058686315689, 0.5226570442826, 0.402559858391101, 0.493276504671798, 2.5687571066063, 2.4371415217098, 5.9509710881472, 5.101557746111)[20250519_003550.]: c(NA, 17.6469490525512, 2.0906281771304, 1.0734929557096, 0.986553009343595, 4.11001137057008, 3.24952202894641, 6.80110981502537, 5.101557746111) - [20250519_003550.]: Entered 'hyperbolic_regression'-Function - [20250519_003550.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003550.]: Entered 'cubic_regression'-Function - [20250519_003550.]: 'cubic_regression': minmax = FALSE - [20250519_003550.]: # CpG-site: CpG#6 + [20250519_004318.]: Logging df_agg: CpG#5 + [20250519_004318.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004318.]: c(1.45850947960108, 10.2941313684311, 24.4773429557174, 37.0974401416089, 49.5067234953282, 59.9312428933937, 72.5628584782902, 81.5490289118528, 94.898442253889)[20250519_004318.]: c(1.45850947960108, 2.2058686315689, 0.5226570442826, 0.402559858391101, 0.493276504671798, 2.5687571066063, 2.4371415217098, 5.9509710881472, 5.101557746111)[20250519_004318.]: c(NA, 17.6469490525512, 2.0906281771304, 1.0734929557096, 0.986553009343595, 4.11001137057008, 3.24952202894641, 6.80110981502537, 5.101557746111) + [20250519_004318.]: Entered 'hyperbolic_regression'-Function + [20250519_004318.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004318.]: Entered 'cubic_regression'-Function + [20250519_004318.]: 'cubic_regression': minmax = FALSE + [20250519_004318.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)c(0.349158802372062, 11.7651478823068, 27.019511576095, 36.8015127603397, 54.3503397041729, 73.8730379134395, 86.2591171327579, 100)c(0.349158802372062, 0.734852117693199, 2.019511576095, 0.698487239660302, 4.3503397041729, 11.3730379134395, 11.2591171327579, 12.5)c(NA, 5.87881694154559, 8.07804630438, 1.86263263909414, 8.70067940834581, 18.1968606615032, 15.0121561770105, 14.2857142857143) - [20250519_003550.]: Logging df_agg: CpG#6 - [20250519_003550.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)[20250519_003550.]: c(0.349158802372062, 11.7651478823068, 27.019511576095, 36.8015127603397, 54.3503397041729, 73.8730379134395, 86.2591171327579, 100)[20250519_003550.]: c(0.349158802372062, 0.734852117693199, 2.019511576095, 0.698487239660302, 4.3503397041729, 11.3730379134395, 11.2591171327579, 12.5)[20250519_003550.]: c(NA, 5.87881694154559, 8.07804630438, 1.86263263909414, 8.70067940834581, 18.1968606615032, 15.0121561770105, 14.2857142857143) - [20250519_003550.]: Entered 'hyperbolic_regression'-Function - [20250519_003550.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003550.]: Entered 'cubic_regression'-Function - [20250519_003550.]: 'cubic_regression': minmax = FALSE - [20250519_003550.]: # CpG-site: CpG#7 + [20250519_004318.]: Logging df_agg: CpG#6 + [20250519_004318.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)[20250519_004318.]: c(0.349158802372062, 11.7651478823068, 27.019511576095, 36.8015127603397, 54.3503397041729, 73.8730379134395, 86.2591171327579, 100)[20250519_004318.]: c(0.349158802372062, 0.734852117693199, 2.019511576095, 0.698487239660302, 4.3503397041729, 11.3730379134395, 11.2591171327579, 12.5)[20250519_004318.]: c(NA, 5.87881694154559, 8.07804630438, 1.86263263909414, 8.70067940834581, 18.1968606615032, 15.0121561770105, 14.2857142857143) + [20250519_004318.]: Entered 'hyperbolic_regression'-Function + [20250519_004318.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004318.]: Entered 'cubic_regression'-Function + [20250519_004318.]: 'cubic_regression': minmax = FALSE + [20250519_004318.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5)c(2.12599911899281, 9.99133393413891, 23.1036438590861, 42.6085296447297)c(2.12599911899281, 2.50866606586109, 1.8963561409139, 5.1085296447297)c(NA, 20.0693285268887, 7.58542456365559, 13.6227457192792) - [20250519_003550.]: Logging df_agg: CpG#7 - [20250519_003550.]: c(0, 12.5, 25, 37.5)[20250519_003550.]: c(2.12599911899281, 9.99133393413891, 23.1036438590861, 42.6085296447297)[20250519_003550.]: c(2.12599911899281, 2.50866606586109, 1.8963561409139, 5.1085296447297)[20250519_003550.]: c(NA, 20.0693285268887, 7.58542456365559, 13.6227457192792) - [20250519_003550.]: Entered 'hyperbolic_regression'-Function - [20250519_003550.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003550.]: Entered 'cubic_regression'-Function - [20250519_003550.]: 'cubic_regression': minmax = FALSE - [20250519_003550.]: # CpG-site: CpG#8 + [20250519_004318.]: Logging df_agg: CpG#7 + [20250519_004318.]: c(0, 12.5, 25, 37.5)[20250519_004318.]: c(2.12599911899281, 9.99133393413891, 23.1036438590861, 42.6085296447297)[20250519_004318.]: c(2.12599911899281, 2.50866606586109, 1.8963561409139, 5.1085296447297)[20250519_004318.]: c(NA, 20.0693285268887, 7.58542456365559, 13.6227457192792) + [20250519_004318.]: Entered 'hyperbolic_regression'-Function + [20250519_004318.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004319.]: Entered 'cubic_regression'-Function + [20250519_004319.]: 'cubic_regression': minmax = FALSE + [20250519_004319.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.0181464298155, 9.50518258466437, 26.4243334649289, 34.6620160335629, 50.9321119457565, 62.2712954366094, 74.1538645100711, 76.6511844577497, 94.3306625091067)c(2.0181464298155, 2.99481741533563, 1.4243334649289, 2.8379839664371, 0.932111945756503, 0.2287045633906, 0.846135489928898, 10.8488155422503, 5.6693374908933)c(NA, 23.958539322685, 5.6973338597156, 7.56795724383227, 1.86422389151301, 0.365927301424961, 1.12818065323853, 12.3986463340004, 5.6693374908933) - [20250519_003550.]: Logging df_agg: CpG#8 - [20250519_003550.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003550.]: c(2.0181464298155, 9.50518258466437, 26.4243334649289, 34.6620160335629, 50.9321119457565, 62.2712954366094, 74.1538645100711, 76.6511844577497, 94.3306625091067)[20250519_003550.]: c(2.0181464298155, 2.99481741533563, 1.4243334649289, 2.8379839664371, 0.932111945756503, 0.2287045633906, 0.846135489928898, 10.8488155422503, 5.6693374908933)[20250519_003550.]: c(NA, 23.958539322685, 5.6973338597156, 7.56795724383227, 1.86422389151301, 0.365927301424961, 1.12818065323853, 12.3986463340004, 5.6693374908933) - [20250519_003550.]: Entered 'hyperbolic_regression'-Function - [20250519_003550.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003550.]: Entered 'cubic_regression'-Function - [20250519_003550.]: 'cubic_regression': minmax = FALSE - [20250519_003551.]: # CpG-site: CpG#9 + [20250519_004319.]: Logging df_agg: CpG#8 + [20250519_004319.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004319.]: c(2.0181464298155, 9.50518258466437, 26.4243334649289, 34.6620160335629, 50.9321119457565, 62.2712954366094, 74.1538645100711, 76.6511844577497, 94.3306625091067)[20250519_004319.]: c(2.0181464298155, 2.99481741533563, 1.4243334649289, 2.8379839664371, 0.932111945756503, 0.2287045633906, 0.846135489928898, 10.8488155422503, 5.6693374908933)[20250519_004319.]: c(NA, 23.958539322685, 5.6973338597156, 7.56795724383227, 1.86422389151301, 0.365927301424961, 1.12818065323853, 12.3986463340004, 5.6693374908933) + [20250519_004319.]: Entered 'hyperbolic_regression'-Function + [20250519_004319.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004319.]: Entered 'cubic_regression'-Function + [20250519_004319.]: 'cubic_regression': minmax = FALSE + [20250519_004319.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5)c(1.47402616445883, 10.1833646164297, 27.0440334834678, 45.8634394698265)c(1.47402616445883, 2.3166353835703, 2.0440334834678, 8.3634394698265)c(NA, 18.5330830685624, 8.1761339338712, 22.3025052528707) - [20250519_003551.]: Logging df_agg: CpG#9 - [20250519_003551.]: c(0, 12.5, 25, 37.5)[20250519_003551.]: c(1.47402616445883, 10.1833646164297, 27.0440334834678, 45.8634394698265)[20250519_003551.]: c(1.47402616445883, 2.3166353835703, 2.0440334834678, 8.3634394698265)[20250519_003551.]: c(NA, 18.5330830685624, 8.1761339338712, 22.3025052528707) - [20250519_003551.]: Entered 'hyperbolic_regression'-Function - [20250519_003551.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003551.]: Entered 'cubic_regression'-Function - [20250519_003551.]: 'cubic_regression': minmax = FALSE - [20250519_003551.]: # CpG-site: row_means + [20250519_004319.]: Logging df_agg: CpG#9 + [20250519_004319.]: c(0, 12.5, 25, 37.5)[20250519_004319.]: c(1.47402616445883, 10.1833646164297, 27.0440334834678, 45.8634394698265)[20250519_004319.]: c(1.47402616445883, 2.3166353835703, 2.0440334834678, 8.3634394698265)[20250519_004319.]: c(NA, 18.5330830685624, 8.1761339338712, 22.3025052528707) + [20250519_004319.]: Entered 'hyperbolic_regression'-Function + [20250519_004319.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004319.]: Entered 'cubic_regression'-Function + [20250519_004319.]: 'cubic_regression': minmax = FALSE + [20250519_004319.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5)c(1.28794987665983, 10.9885302801468, 25.950937077156, 39.5179682033924, 60.2624119769469, 87.2132000752344)c(1.28794987665983, 1.5114697198532, 0.950937077155999, 2.0179682033924, 10.2624119769469, 24.7132000752344)c(NA, 12.0917577588256, 3.80374830862399, 5.38124854237973, 20.5248239538938, 39.541120120375) - [20250519_003551.]: Logging df_agg: row_means - [20250519_003551.]: c(0, 12.5, 25, 37.5, 50, 62.5)[20250519_003551.]: c(1.28794987665983, 10.9885302801468, 25.950937077156, 39.5179682033924, 60.2624119769469, 87.2132000752344)[20250519_003551.]: c(1.28794987665983, 1.5114697198532, 0.950937077155999, 2.0179682033924, 10.2624119769469, 24.7132000752344)[20250519_003551.]: c(NA, 12.0917577588256, 3.80374830862399, 5.38124854237973, 20.5248239538938, 39.541120120375) - [20250519_003551.]: Entered 'hyperbolic_regression'-Function - [20250519_003551.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003551.]: Entered 'cubic_regression'-Function - [20250519_003551.]: 'cubic_regression': minmax = FALSE - [20250519_003551.]: Entered 'solving_equations'-Function - [20250519_003552.]: Solving hyperbolic regression for CpG#1 + [20250519_004319.]: Logging df_agg: row_means + [20250519_004319.]: c(0, 12.5, 25, 37.5, 50, 62.5)[20250519_004319.]: c(1.28794987665983, 10.9885302801468, 25.950937077156, 39.5179682033924, 60.2624119769469, 87.2132000752344)[20250519_004319.]: c(1.28794987665983, 1.5114697198532, 0.950937077155999, 2.0179682033924, 10.2624119769469, 24.7132000752344)[20250519_004319.]: c(NA, 12.0917577588256, 3.80374830862399, 5.38124854237973, 20.5248239538938, 39.541120120375) + [20250519_004319.]: Entered 'hyperbolic_regression'-Function + [20250519_004319.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004319.]: Entered 'cubic_regression'-Function + [20250519_004319.]: 'cubic_regression': minmax = FALSE + [20250519_004320.]: Entered 'solving_equations'-Function + [20250519_004320.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 79.8672902891399 - [20250519_003552.]: Samplename: Sample#1 + [20250519_004320.]: Samplename: Sample#1 Root: 79.867 --> Root in between the borders! Added to results. Hyperbolic solved: 29.789995526108 - [20250519_003552.]: Samplename: Sample#10 + [20250519_004320.]: Samplename: Sample#10 Root: 29.79 --> Root in between the borders! Added to results. Hyperbolic solved: 41.6525103283956 - [20250519_003552.]: Samplename: Sample#2 + [20250519_004320.]: Samplename: Sample#2 Root: 41.653 --> Root in between the borders! Added to results. Hyperbolic solved: 57.4651672980431 - [20250519_003552.]: Samplename: Sample#3 + [20250519_004320.]: Samplename: Sample#3 Root: 57.465 --> Root in between the borders! Added to results. Hyperbolic solved: 9.20070553821487 - [20250519_003552.]: Samplename: Sample#4 + [20250519_004320.]: Samplename: Sample#4 Root: 9.201 --> Root in between the borders! Added to results. Hyperbolic solved: 21.8059429711637 - [20250519_003552.]: Samplename: Sample#5 + [20250519_004320.]: Samplename: Sample#5 Root: 21.806 --> Root in between the borders! Added to results. Hyperbolic solved: 23.0837787089758 - [20250519_003552.]: Samplename: Sample#6 + [20250519_004320.]: Samplename: Sample#6 Root: 23.084 --> Root in between the borders! Added to results. Hyperbolic solved: 45.5033907021902 - [20250519_003552.]: Samplename: Sample#7 + [20250519_004320.]: Samplename: Sample#7 Root: 45.503 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6987316769908 - [20250519_003552.]: Samplename: Sample#8 + [20250519_004320.]: Samplename: Sample#8 Root: 85.699 --> Root in between the borders! Added to results. Hyperbolic solved: -3.66512538562846 - [20250519_003552.]: Samplename: Sample#9 + [20250519_004320.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -3.665 --> '-10 < root < 0' --> substitute 0 - [20250519_003552.]: Solving hyperbolic regression for CpG#2 + [20250519_004320.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 76.7230240612756 - [20250519_003552.]: Samplename: Sample#1 + [20250519_004320.]: Samplename: Sample#1 Root: 76.723 --> Root in between the borders! Added to results. Hyperbolic solved: 30.992543297941 - [20250519_003552.]: Samplename: Sample#10 + [20250519_004320.]: Samplename: Sample#10 Root: 30.993 --> Root in between the borders! Added to results. Hyperbolic solved: 42.6812361010669 - [20250519_003552.]: Samplename: Sample#2 + [20250519_004320.]: Samplename: Sample#2 Root: 42.681 --> Root in between the borders! Added to results. Hyperbolic solved: 58.933980239834 - [20250519_003552.]: Samplename: Sample#3 + [20250519_004320.]: Samplename: Sample#3 Root: 58.934 --> Root in between the borders! Added to results. Hyperbolic solved: 4.98024265777963 - [20250519_003552.]: Samplename: Sample#4 + [20250519_004320.]: Samplename: Sample#4 Root: 4.98 --> Root in between the borders! Added to results. Hyperbolic solved: 20.2481111308688 - [20250519_003552.]: Samplename: Sample#5 + [20250519_004320.]: Samplename: Sample#5 Root: 20.248 --> Root in between the borders! Added to results. Hyperbolic solved: 17.813386088098 - [20250519_003552.]: Samplename: Sample#6 + [20250519_004320.]: Samplename: Sample#6 Root: 17.813 --> Root in between the borders! Added to results. Hyperbolic solved: 40.9178176381316 - [20250519_003552.]: Samplename: Sample#7 + [20250519_004320.]: Samplename: Sample#7 Root: 40.918 --> Root in between the borders! Added to results. Hyperbolic solved: 86.0453226524415 - [20250519_003552.]: Samplename: Sample#8 + [20250519_004320.]: Samplename: Sample#8 Root: 86.045 --> Root in between the borders! Added to results. Hyperbolic solved: 3.11582988095862 - [20250519_003552.]: Samplename: Sample#9 + [20250519_004320.]: Samplename: Sample#9 Root: 3.116 --> Root in between the borders! Added to results. - [20250519_003552.]: Solving hyperbolic regression for CpG#3 + [20250519_004320.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 74.934721803959 - [20250519_003552.]: Samplename: Sample#1 + [20250519_004320.]: Samplename: Sample#1 Root: 74.935 --> Root in between the borders! Added to results. Hyperbolic solved: 27.6843475257834 - [20250519_003552.]: Samplename: Sample#10 + [20250519_004320.]: Samplename: Sample#10 Root: 27.684 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8518905782652 - [20250519_003552.]: Samplename: Sample#2 + [20250519_004320.]: Samplename: Sample#2 Root: 41.852 --> Root in between the borders! Added to results. Hyperbolic solved: 55.8323554649537 - [20250519_003552.]: Samplename: Sample#3 + [20250519_004320.]: Samplename: Sample#3 Root: 55.832 --> Root in between the borders! Added to results. Hyperbolic solved: 8.03516016616019 - [20250519_003552.]: Samplename: Sample#4 + [20250519_004320.]: Samplename: Sample#4 Root: 8.035 --> Root in between the borders! Added to results. Hyperbolic solved: 24.1065510576898 - [20250519_003552.]: Samplename: Sample#5 + [20250519_004320.]: Samplename: Sample#5 Root: 24.107 --> Root in between the borders! Added to results. Hyperbolic solved: 26.2418954231184 - [20250519_003552.]: Samplename: Sample#6 + [20250519_004320.]: Samplename: Sample#6 Root: 26.242 --> Root in between the borders! Added to results. Hyperbolic solved: 44.0943580511304 - [20250519_003552.]: Samplename: Sample#7 + [20250519_004320.]: Samplename: Sample#7 Root: 44.094 --> Root in between the borders! Added to results. Hyperbolic solved: 85.8277140531902 - [20250519_003552.]: Samplename: Sample#8 + [20250519_004320.]: Samplename: Sample#8 Root: 85.828 --> Root in between the borders! Added to results. Hyperbolic solved: -0.666486811581751 - [20250519_003552.]: Samplename: Sample#9 + [20250519_004320.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.666 --> '-10 < root < 0' --> substitute 0 - [20250519_003552.]: Solving hyperbolic regression for CpG#4 + [20250519_004320.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 76.349372671628 - [20250519_003552.]: Samplename: Sample#1 + [20250519_004320.]: Samplename: Sample#1 Root: 76.349 --> Root in between the borders! Added to results. Hyperbolic solved: 28.2567843724913 - [20250519_003552.]: Samplename: Sample#10 + [20250519_004320.]: Samplename: Sample#10 Root: 28.257 --> Root in between the borders! Added to results. Hyperbolic solved: 43.4088841408467 - [20250519_003552.]: Samplename: Sample#2 + [20250519_004320.]: Samplename: Sample#2 Root: 43.409 --> Root in between the borders! Added to results. Hyperbolic solved: 58.5433972042602 - [20250519_003552.]: Samplename: Sample#3 + [20250519_004320.]: Samplename: Sample#3 Root: 58.543 --> Root in between the borders! Added to results. Hyperbolic solved: 10.3086704325003 - [20250519_003552.]: Samplename: Sample#4 + [20250519_004320.]: Samplename: Sample#4 Root: 10.309 --> Root in between the borders! Added to results. Hyperbolic solved: 22.182986320858 - [20250519_003552.]: Samplename: Sample#5 + [20250519_004320.]: Samplename: Sample#5 Root: 22.183 --> Root in between the borders! Added to results. Hyperbolic solved: 27.1337081900258 - [20250519_003552.]: Samplename: Sample#6 + [20250519_004320.]: Samplename: Sample#6 Root: 27.134 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8320127104856 - [20250519_003552.]: Samplename: Sample#7 + [20250519_004320.]: Samplename: Sample#7 Root: 41.832 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6888498156134 - [20250519_003552.]: Samplename: Sample#8 + [20250519_004320.]: Samplename: Sample#8 Root: 85.689 --> Root in between the borders! Added to results. Hyperbolic solved: 2.42230396811441 - [20250519_003552.]: Samplename: Sample#9 + [20250519_004320.]: Samplename: Sample#9 Root: 2.422 --> Root in between the borders! Added to results. - [20250519_003552.]: Solving hyperbolic regression for CpG#5 + [20250519_004320.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 72.8126507590781 - [20250519_003552.]: Samplename: Sample#1 + [20250519_004320.]: Samplename: Sample#1 Root: 72.813 --> Root in between the borders! Added to results. Hyperbolic solved: 26.4206774223762 - [20250519_003552.]: Samplename: Sample#10 + [20250519_004320.]: Samplename: Sample#10 Root: 26.421 --> Root in between the borders! Added to results. Hyperbolic solved: 44.2738516315831 - [20250519_003552.]: Samplename: Sample#2 + [20250519_004320.]: Samplename: Sample#2 Root: 44.274 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5324088748019 - [20250519_003552.]: Samplename: Sample#3 + [20250519_004320.]: Samplename: Sample#3 Root: 53.532 --> Root in between the borders! Added to results. Hyperbolic solved: 10.0373628417333 - [20250519_003552.]: Samplename: Sample#4 + [20250519_004320.]: Samplename: Sample#4 Root: 10.037 --> Root in between the borders! Added to results. Hyperbolic solved: 22.8732969704156 - [20250519_003552.]: Samplename: Sample#5 + [20250519_004320.]: Samplename: Sample#5 Root: 22.873 --> Root in between the borders! Added to results. Hyperbolic solved: 25.9417756406497 - [20250519_003552.]: Samplename: Sample#6 + [20250519_004320.]: Samplename: Sample#6 Root: 25.942 --> Root in between the borders! Added to results. Hyperbolic solved: 42.7944833740993 - [20250519_003552.]: Samplename: Sample#7 + [20250519_004320.]: Samplename: Sample#7 Root: 42.794 --> Root in between the borders! Added to results. Hyperbolic solved: 88.8740708807914 - [20250519_003552.]: Samplename: Sample#8 + [20250519_004320.]: Samplename: Sample#8 Root: 88.874 --> Root in between the borders! Added to results. Hyperbolic solved: 2.34646946564885 - [20250519_003552.]: Samplename: Sample#9 + [20250519_004320.]: Samplename: Sample#9 Root: 2.346 --> Root in between the borders! Added to results. - [20250519_003552.]: Solving hyperbolic regression for CpG#6 + [20250519_004320.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 79.2782328649447 - [20250519_003552.]: Samplename: Sample#1 + [20250519_004320.]: Samplename: Sample#1 Root: 79.278 --> Root in between the borders! Added to results. Hyperbolic solved: 30.2013285011234 - [20250519_003552.]: Samplename: Sample#10 + [20250519_004320.]: Samplename: Sample#10 Root: 30.201 --> Root in between the borders! Added to results. Hyperbolic solved: 41.847545497608 - [20250519_003552.]: Samplename: Sample#2 + [20250519_004320.]: Samplename: Sample#2 Root: 41.848 --> Root in between the borders! Added to results. Hyperbolic solved: 56.8424863688491 - [20250519_003552.]: Samplename: Sample#3 + [20250519_004320.]: Samplename: Sample#3 Root: 56.842 --> Root in between the borders! Added to results. Hyperbolic solved: 8.87859681588532 - [20250519_003552.]: Samplename: Sample#4 + [20250519_004320.]: Samplename: Sample#4 Root: 8.879 --> Root in between the borders! Added to results. Hyperbolic solved: 18.6902176438696 - [20250519_003552.]: Samplename: Sample#5 + [20250519_004320.]: Samplename: Sample#5 Root: 18.69 --> Root in between the borders! Added to results. Hyperbolic solved: 29.9310083954849 - [20250519_003552.]: Samplename: Sample#6 + [20250519_004320.]: Samplename: Sample#6 Root: 29.931 --> Root in between the borders! Added to results. Hyperbolic solved: 42.814964037839 - [20250519_003552.]: Samplename: Sample#7 + [20250519_004320.]: Samplename: Sample#7 Root: 42.815 --> Root in between the borders! Added to results. Hyperbolic solved: 86.7503685829287 - [20250519_003552.]: Samplename: Sample#8 + [20250519_004320.]: Samplename: Sample#8 Root: 86.75 --> Root in between the borders! Added to results. Hyperbolic solved: 1.51518138323643 - [20250519_003552.]: Samplename: Sample#9 + [20250519_004320.]: Samplename: Sample#9 Root: 1.515 --> Root in between the borders! Added to results. - [20250519_003552.]: Solving hyperbolic regression for CpG#7 + [20250519_004320.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 78.2568233815803 - [20250519_003552.]: Samplename: Sample#1 + [20250519_004320.]: Samplename: Sample#1 Root: 78.257 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4888594923858 - [20250519_003552.]: Samplename: Sample#10 + [20250519_004320.]: Samplename: Sample#10 Root: 25.489 --> Root in between the borders! Added to results. Hyperbolic solved: 47.3714242177043 - [20250519_003552.]: Samplename: Sample#2 + [20250519_004320.]: Samplename: Sample#2 Root: 47.371 --> Root in between the borders! Added to results. Hyperbolic solved: 58.3144949237501 - [20250519_003552.]: Samplename: Sample#3 + [20250519_004320.]: Samplename: Sample#3 Root: 58.314 --> Root in between the borders! Added to results. Hyperbolic solved: 11.7212806729981 - [20250519_003552.]: Samplename: Sample#4 + [20250519_004320.]: Samplename: Sample#4 Root: 11.721 --> Root in between the borders! Added to results. Hyperbolic solved: 25.3798682873976 - [20250519_003552.]: Samplename: Sample#5 + [20250519_004320.]: Samplename: Sample#5 Root: 25.38 --> Root in between the borders! Added to results. Hyperbolic solved: 29.4096493828253 - [20250519_003552.]: Samplename: Sample#6 + [20250519_004320.]: Samplename: Sample#6 Root: 29.41 --> Root in between the borders! Added to results. Hyperbolic solved: 44.5756969430888 - [20250519_003552.]: Samplename: Sample#7 + [20250519_004320.]: Samplename: Sample#7 Root: 44.576 --> Root in between the borders! Added to results. Hyperbolic solved: 85.9631455147929 - [20250519_003552.]: Samplename: Sample#8 + [20250519_004320.]: Samplename: Sample#8 Root: 85.963 --> Root in between the borders! Added to results. Hyperbolic solved: -4.16459229634232 - [20250519_003552.]: Samplename: Sample#9 + [20250519_004320.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -4.165 --> '-10 < root < 0' --> substitute 0 - [20250519_003552.]: Solving hyperbolic regression for CpG#8 + [20250519_004320.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 72.9696678780879 - [20250519_003552.]: Samplename: Sample#1 + [20250519_004320.]: Samplename: Sample#1 Root: 72.97 --> Root in between the borders! Added to results. Hyperbolic solved: 27.825096438612 - [20250519_003552.]: Samplename: Sample#10 + [20250519_004320.]: Samplename: Sample#10 Root: 27.825 --> Root in between the borders! Added to results. Hyperbolic solved: 34.9068412412515 - [20250519_003552.]: Samplename: Sample#2 + [20250519_004320.]: Samplename: Sample#2 Root: 34.907 --> Root in between the borders! Added to results. Hyperbolic solved: 59.1637501357335 - [20250519_003552.]: Samplename: Sample#3 + [20250519_004320.]: Samplename: Sample#3 Root: 59.164 --> Root in between the borders! Added to results. Hyperbolic solved: 9.6464397671905 - [20250519_003552.]: Samplename: Sample#4 + [20250519_004320.]: Samplename: Sample#4 Root: 9.646 --> Root in between the borders! Added to results. Hyperbolic solved: 19.3522593577541 - [20250519_003552.]: Samplename: Sample#5 + [20250519_004320.]: Samplename: Sample#5 Root: 19.352 --> Root in between the borders! Added to results. Hyperbolic solved: 38.5571468548369 - [20250519_003552.]: Samplename: Sample#6 + [20250519_004320.]: Samplename: Sample#6 Root: 38.557 --> Root in between the borders! Added to results. Hyperbolic solved: 44.775966890921 - [20250519_003552.]: Samplename: Sample#7 + [20250519_004320.]: Samplename: Sample#7 Root: 44.776 --> Root in between the borders! Added to results. Hyperbolic solved: 84.8599747802978 - [20250519_003552.]: Samplename: Sample#8 + [20250519_004320.]: Samplename: Sample#8 Root: 84.86 --> Root in between the borders! Added to results. Hyperbolic solved: -2.05045739684141 - [20250519_003552.]: Samplename: Sample#9 + [20250519_004320.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.05 --> '-10 < root < 0' --> substitute 0 - [20250519_003552.]: Solving hyperbolic regression for CpG#9 + [20250519_004320.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 81.0413625074508 - [20250519_003552.]: Samplename: Sample#1 + [20250519_004320.]: Samplename: Sample#1 Root: 81.041 --> Root in between the borders! Added to results. Hyperbolic solved: 26.6440232383638 - [20250519_003552.]: Samplename: Sample#10 + [20250519_004320.]: Samplename: Sample#10 Root: 26.644 --> Root in between the borders! Added to results. Hyperbolic solved: 45.704400902509 - [20250519_003552.]: Samplename: Sample#2 + [20250519_004320.]: Samplename: Sample#2 Root: 45.704 --> Root in between the borders! Added to results. Hyperbolic solved: 56.9765444860285 - [20250519_003552.]: Samplename: Sample#3 + [20250519_004320.]: Samplename: Sample#3 Root: 56.977 --> Root in between the borders! Added to results. Hyperbolic solved: 6.81226926030202 - [20250519_003552.]: Samplename: Sample#4 + [20250519_004320.]: Samplename: Sample#4 Root: 6.812 --> Root in between the borders! Added to results. Hyperbolic solved: 22.9283517775063 - [20250519_003552.]: Samplename: Sample#5 + [20250519_004320.]: Samplename: Sample#5 Root: 22.928 --> Root in between the borders! Added to results. Hyperbolic solved: 38.8651260750996 - [20250519_003552.]: Samplename: Sample#6 + [20250519_004320.]: Samplename: Sample#6 Root: 38.865 --> Root in between the borders! Added to results. Hyperbolic solved: 43.6853511825078 - [20250519_003552.]: Samplename: Sample#7 + [20250519_004320.]: Samplename: Sample#7 Root: 43.685 --> Root in between the borders! Added to results. Hyperbolic solved: 88.0152224353095 - [20250519_003552.]: Samplename: Sample#8 + [20250519_004320.]: Samplename: Sample#8 Root: 88.015 --> Root in between the borders! Added to results. Hyperbolic solved: -3.35608361520768 - [20250519_003552.]: Samplename: Sample#9 + [20250519_004320.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -3.356 --> '-10 < root < 0' --> substitute 0 - [20250519_003552.]: Solving hyperbolic regression for row_means + [20250519_004320.]: Solving hyperbolic regression for row_means Hyperbolic solved: 77.069275047056 - [20250519_003552.]: Samplename: Sample#1 + [20250519_004320.]: Samplename: Sample#1 Root: 77.069 --> Root in between the borders! Added to results. Hyperbolic solved: 28.3620003757916 - [20250519_003552.]: Samplename: Sample#10 + [20250519_004320.]: Samplename: Sample#10 Root: 28.362 --> Root in between the borders! Added to results. Hyperbolic solved: 42.5026135474906 - [20250519_003552.]: Samplename: Sample#2 + [20250519_004320.]: Samplename: Sample#2 Root: 42.503 --> Root in between the borders! Added to results. Hyperbolic solved: 57.2972007822813 - [20250519_003552.]: Samplename: Sample#3 + [20250519_004320.]: Samplename: Sample#3 Root: 57.297 --> Root in between the borders! Added to results. Hyperbolic solved: 8.82703592503651 - [20250519_003552.]: Samplename: Sample#4 + [20250519_004320.]: Samplename: Sample#4 Root: 8.827 --> Root in between the borders! Added to results. Hyperbolic solved: 21.8102552608375 - [20250519_003552.]: Samplename: Sample#5 + [20250519_004320.]: Samplename: Sample#5 Root: 21.81 --> Root in between the borders! Added to results. Hyperbolic solved: 28.7228620570755 - [20250519_003552.]: Samplename: Sample#6 + [20250519_004320.]: Samplename: Sample#6 Root: 28.723 --> Root in between the borders! Added to results. Hyperbolic solved: 43.4105062814289 - [20250519_003552.]: Samplename: Sample#7 + [20250519_004320.]: Samplename: Sample#7 Root: 43.411 --> Root in between the borders! Added to results. Hyperbolic solved: 86.4143497902066 - [20250519_003552.]: Samplename: Sample#8 + [20250519_004320.]: Samplename: Sample#8 Root: 86.414 --> Root in between the borders! Added to results. Hyperbolic solved: -0.237025016559672 - [20250519_003552.]: Samplename: Sample#9 + [20250519_004320.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.237 --> '-10 < root < 0' --> substitute 0 - [20250519_003552.]: Entered 'solving_equations'-Function - [20250519_003552.]: Solving hyperbolic regression for CpG#1 + [20250519_004320.]: Entered 'solving_equations'-Function + [20250519_004320.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: -2.23222837469517 - [20250519_003552.]: Samplename: 0 + [20250519_004320.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. @@ -5939,126 +5936,126 @@ Root: -2.232 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.1698391693126 - [20250519_003552.]: Samplename: 12.5 + [20250519_004320.]: Samplename: 12.5 Root: 12.17 --> Root in between the borders! Added to results. Hyperbolic solved: 24.4781729791561 - [20250519_003552.]: Samplename: 25 + [20250519_004320.]: Samplename: 25 Root: 24.478 --> Root in between the borders! Added to results. Hyperbolic solved: 38.1730159074047 - [20250519_003552.]: Samplename: 37.5 + [20250519_004320.]: Samplename: 37.5 Root: 38.173 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3348987967717 - [20250519_003552.]: Samplename: 50 + [20250519_004320.]: Samplename: 50 Root: 52.335 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4582305966058 - [20250519_003552.]: Samplename: 62.5 + [20250519_004320.]: Samplename: 62.5 Root: 65.458 --> Root in between the borders! Added to results. Hyperbolic solved: 75.0090269722885 - [20250519_003552.]: Samplename: 75 + [20250519_004320.]: Samplename: 75 Root: 75.009 --> Root in between the borders! Added to results. Hyperbolic solved: 81.527135738891 - [20250519_003552.]: Samplename: 87.5 + [20250519_004320.]: Samplename: 87.5 Root: 81.527 --> Root in between the borders! Added to results. Hyperbolic solved: 102.400825350044 - [20250519_003552.]: Samplename: 100 + [20250519_004320.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.401 --> '100 < root < 110' --> substitute 100 - [20250519_003552.]: Solving hyperbolic regression for CpG#2 + [20250519_004320.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 1.13663087266051 - [20250519_003552.]: Samplename: 0 + [20250519_004320.]: Samplename: 0 Root: 1.137 --> Root in between the borders! Added to results. Hyperbolic solved: 11.4130277963576 - [20250519_003552.]: Samplename: 12.5 + [20250519_004320.]: Samplename: 12.5 Root: 11.413 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1741025868157 - [20250519_003552.]: Samplename: 25 + [20250519_004320.]: Samplename: 25 Root: 26.174 --> Root in between the borders! Added to results. Hyperbolic solved: 35.1051721736724 - [20250519_003552.]: Samplename: 37.5 + [20250519_004320.]: Samplename: 37.5 Root: 35.105 --> Root in between the borders! Added to results. Hyperbolic solved: 47.6856613240114 - [20250519_003552.]: Samplename: 50 + [20250519_004320.]: Samplename: 50 Root: 47.686 --> Root in between the borders! Added to results. Hyperbolic solved: 67.1442585354696 - [20250519_003552.]: Samplename: 62.5 + [20250519_004320.]: Samplename: 62.5 Root: 67.144 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7646957109439 - [20250519_003552.]: Samplename: 75 + [20250519_004320.]: Samplename: 75 Root: 75.765 --> Root in between the borders! Added to results. Hyperbolic solved: 84.4056634834815 - [20250519_003552.]: Samplename: 87.5 + [20250519_004320.]: Samplename: 87.5 Root: 84.406 --> Root in between the borders! Added to results. Hyperbolic solved: 100.94855337669 - [20250519_003552.]: Samplename: 100 + [20250519_004320.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.949 --> '100 < root < 110' --> substitute 100 - [20250519_003552.]: Solving hyperbolic regression for CpG#3 + [20250519_004320.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 0.512348257854635 - [20250519_003552.]: Samplename: 0 + [20250519_004320.]: Samplename: 0 Root: 0.512 --> Root in between the borders! Added to results. Hyperbolic solved: 10.7523476241811 - [20250519_003552.]: Samplename: 12.5 + [20250519_004320.]: Samplename: 12.5 Root: 10.752 --> Root in between the borders! Added to results. Hyperbolic solved: 25.5218062516799 - [20250519_003552.]: Samplename: 25 + [20250519_004320.]: Samplename: 25 Root: 25.522 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5269315729339 - [20250519_003552.]: Samplename: 37.5 + [20250519_004320.]: Samplename: 37.5 Root: 36.527 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7907738274884 - [20250519_003552.]: Samplename: 50 + [20250519_004320.]: Samplename: 50 Root: 50.791 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8684489988589 - [20250519_003552.]: Samplename: 62.5 + [20250519_004320.]: Samplename: 62.5 Root: 64.868 --> Root in between the borders! Added to results. Hyperbolic solved: 77.5522101073979 - [20250519_003552.]: Samplename: 75 + [20250519_004320.]: Samplename: 75 Root: 77.552 --> Root in between the borders! Added to results. Hyperbolic solved: 80.4372474691229 - [20250519_003552.]: Samplename: 87.5 + [20250519_004320.]: Samplename: 87.5 Root: 80.437 --> Root in between the borders! Added to results. Hyperbolic solved: 102.703772810881 - [20250519_003552.]: Samplename: 100 + [20250519_004320.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.704 --> '100 < root < 110' --> substitute 100 - [20250519_003552.]: Solving hyperbolic regression for CpG#4 + [20250519_004320.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: -0.519513573026794 - [20250519_003552.]: Samplename: 0 + [20250519_004320.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. @@ -6066,126 +6063,126 @@ Root: -0.52 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.4933760051342 - [20250519_003552.]: Samplename: 12.5 + [20250519_004320.]: Samplename: 12.5 Root: 12.493 --> Root in between the borders! Added to results. Hyperbolic solved: 24.2684789512555 - [20250519_003552.]: Samplename: 25 + [20250519_004320.]: Samplename: 25 Root: 24.268 --> Root in between the borders! Added to results. Hyperbolic solved: 38.0816229393195 - [20250519_003552.]: Samplename: 37.5 + [20250519_004320.]: Samplename: 37.5 Root: 38.082 --> Root in between the borders! Added to results. Hyperbolic solved: 48.5842089589187 - [20250519_003552.]: Samplename: 50 + [20250519_004320.]: Samplename: 50 Root: 48.584 --> Root in between the borders! Added to results. Hyperbolic solved: 67.6720983549562 - [20250519_003552.]: Samplename: 62.5 + [20250519_004320.]: Samplename: 62.5 Root: 67.672 --> Root in between the borders! Added to results. Hyperbolic solved: 74.1547759694059 - [20250519_003552.]: Samplename: 75 + [20250519_004320.]: Samplename: 75 Root: 74.155 --> Root in between the borders! Added to results. Hyperbolic solved: 82.8820147905386 - [20250519_003552.]: Samplename: 87.5 + [20250519_004320.]: Samplename: 87.5 Root: 82.882 --> Root in between the borders! Added to results. Hyperbolic solved: 102.078935346876 - [20250519_003552.]: Samplename: 100 + [20250519_004320.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.079 --> '100 < root < 110' --> substitute 100 - [20250519_003552.]: Solving hyperbolic regression for CpG#5 + [20250519_004320.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 2.41558040143479 - [20250519_003552.]: Samplename: 0 + [20250519_004320.]: Samplename: 0 Root: 2.416 --> Root in between the borders! Added to results. Hyperbolic solved: 10.1649584830834 - [20250519_003552.]: Samplename: 12.5 + [20250519_004320.]: Samplename: 12.5 Root: 10.165 --> Root in between the borders! Added to results. Hyperbolic solved: 23.9830670020905 - [20250519_003552.]: Samplename: 25 + [20250519_004320.]: Samplename: 25 Root: 23.983 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2773406385708 - [20250519_003552.]: Samplename: 37.5 + [20250519_004320.]: Samplename: 37.5 Root: 37.277 --> Root in between the borders! Added to results. Hyperbolic solved: 50.8659103346102 - [20250519_003552.]: Samplename: 50 + [20250519_004320.]: Samplename: 50 Root: 50.866 --> Root in between the borders! Added to results. Hyperbolic solved: 62.4341926937382 - [20250519_003552.]: Samplename: 62.5 + [20250519_004320.]: Samplename: 62.5 Root: 62.434 --> Root in between the borders! Added to results. Hyperbolic solved: 76.3914832329149 - [20250519_003552.]: Samplename: 75 + [20250519_004320.]: Samplename: 75 Root: 76.391 --> Root in between the borders! Added to results. Hyperbolic solved: 86.1597399215782 - [20250519_003552.]: Samplename: 87.5 + [20250519_004320.]: Samplename: 87.5 Root: 86.16 --> Root in between the borders! Added to results. Hyperbolic solved: 100.267701841999 - [20250519_003552.]: Samplename: 100 + [20250519_004320.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.268 --> '100 < root < 110' --> substitute 100 - [20250519_003552.]: Solving hyperbolic regression for CpG#6 + [20250519_004320.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 0.138179963459196 - [20250519_003552.]: Samplename: 0 + [20250519_004320.]: Samplename: 0 Root: 0.138 --> Root in between the borders! Added to results. Hyperbolic solved: 11.8635989635202 - [20250519_003552.]: Samplename: 12.5 + [20250519_004320.]: Samplename: 12.5 Root: 11.864 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5108198460409 - [20250519_003552.]: Samplename: 25 + [20250519_004320.]: Samplename: 25 Root: 26.511 --> Root in between the borders! Added to results. Hyperbolic solved: 35.3206004021833 - [20250519_003552.]: Samplename: 37.5 + [20250519_004320.]: Samplename: 37.5 Root: 35.321 --> Root in between the borders! Added to results. Hyperbolic solved: 50.0571987473308 - [20250519_003552.]: Samplename: 50 + [20250519_004320.]: Samplename: 50 Root: 50.057 --> Root in between the borders! Added to results. Hyperbolic solved: 64.9604436607063 - [20250519_003552.]: Samplename: 62.5 + [20250519_004320.]: Samplename: 62.5 Root: 64.96 --> Root in between the borders! Added to results. Hyperbolic solved: 73.6690699266294 - [20250519_003552.]: Samplename: 75 + [20250519_004320.]: Samplename: 75 Root: 73.669 --> Root in between the borders! Added to results. Hyperbolic solved: 87.1267946879604 - [20250519_003552.]: Samplename: 87.5 + [20250519_004320.]: Samplename: 87.5 Root: 87.127 --> Root in between the borders! Added to results. Hyperbolic solved: 100.261842767865 - [20250519_003552.]: Samplename: 100 + [20250519_004320.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.262 --> '100 < root < 110' --> substitute 100 - [20250519_003552.]: Solving hyperbolic regression for CpG#7 + [20250519_004320.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: -1.37239249181669 - [20250519_003552.]: Samplename: 0 + [20250519_004320.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. @@ -6193,85 +6190,85 @@ Root: -1.372 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.199366215418 - [20250519_003552.]: Samplename: 12.5 + [20250519_004320.]: Samplename: 12.5 Root: 10.199 --> Root in between the borders! Added to results. Hyperbolic solved: 24.595295361327 - [20250519_003552.]: Samplename: 25 + [20250519_004320.]: Samplename: 25 Root: 24.595 --> Root in between the borders! Added to results. Hyperbolic solved: 37.8312095633391 - [20250519_003552.]: Samplename: 37.5 + [20250519_004320.]: Samplename: 37.5 Root: 37.831 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5590896582852 - [20250519_003552.]: Samplename: 50 + [20250519_004320.]: Samplename: 50 Root: 53.559 --> Root in between the borders! Added to results. Hyperbolic solved: 65.9367389282431 - [20250519_003552.]: Samplename: 62.5 + [20250519_004320.]: Samplename: 62.5 Root: 65.937 --> Root in between the borders! Added to results. Hyperbolic solved: 75.736370152841 - [20250519_003552.]: Samplename: 75 + [20250519_004320.]: Samplename: 75 Root: 75.736 --> Root in between the borders! Added to results. Hyperbolic solved: 79.433089359524 - [20250519_003552.]: Samplename: 87.5 + [20250519_004320.]: Samplename: 87.5 Root: 79.433 --> Root in between the borders! Added to results. Hyperbolic solved: 103.004516225662 - [20250519_003552.]: Samplename: 100 + [20250519_004320.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 103.005 --> '100 < root < 110' --> substitute 100 - [20250519_003552.]: Solving hyperbolic regression for CpG#8 + [20250519_004320.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 2.80067549526643 - [20250519_003552.]: Samplename: 0 + [20250519_004320.]: Samplename: 0 Root: 2.801 --> Root in between the borders! Added to results. Hyperbolic solved: 9.27534686003087 - [20250519_003552.]: Samplename: 12.5 + [20250519_004320.]: Samplename: 12.5 Root: 9.275 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4762624089952 - [20250519_003552.]: Samplename: 25 + [20250519_004320.]: Samplename: 25 Root: 25.476 --> Root in between the borders! Added to results. Hyperbolic solved: 34.0122098045585 - [20250519_003552.]: Samplename: 37.5 + [20250519_004320.]: Samplename: 37.5 Root: 34.012 --> Root in between the borders! Added to results. Hyperbolic solved: 51.784270967861 - [20250519_003552.]: Samplename: 50 + [20250519_004320.]: Samplename: 50 Root: 51.784 --> Root in between the borders! Added to results. Hyperbolic solved: 64.6732380258789 - [20250519_003552.]: Samplename: 62.5 + [20250519_004320.]: Samplename: 62.5 Root: 64.673 --> Root in between the borders! Added to results. Hyperbolic solved: 78.4327054483192 - [20250519_003552.]: Samplename: 75 + [20250519_004320.]: Samplename: 75 Root: 78.433 --> Root in between the borders! Added to results. Hyperbolic solved: 81.3427308951795 - [20250519_003552.]: Samplename: 87.5 + [20250519_004320.]: Samplename: 87.5 Root: 81.343 --> Root in between the borders! Added to results. Hyperbolic solved: 101.964413523995 - [20250519_003552.]: Samplename: 100 + [20250519_004320.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.964 --> '100 < root < 110' --> substitute 100 - [20250519_003552.]: Solving hyperbolic regression for CpG#9 + [20250519_004320.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: -2.13402763663736 - [20250519_003552.]: Samplename: 0 + [20250519_004320.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. @@ -6279,91 +6276,91 @@ Root: -2.134 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.5081558897243 - [20250519_003552.]: Samplename: 12.5 + [20250519_004320.]: Samplename: 12.5 Root: 10.508 --> Root in between the borders! Added to results. Hyperbolic solved: 26.9163121098627 - [20250519_003552.]: Samplename: 25 + [20250519_004320.]: Samplename: 25 Root: 26.916 --> Root in between the borders! Added to results. Hyperbolic solved: 36.8332915301825 - [20250519_003552.]: Samplename: 37.5 + [20250519_004320.]: Samplename: 37.5 Root: 36.833 --> Root in between the borders! Added to results. Hyperbolic solved: 52.00955 - [20250519_003552.]: Samplename: 50 + [20250519_004320.]: Samplename: 50 Root: 52.01 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8927781893359 - [20250519_003552.]: Samplename: 62.5 + [20250519_004320.]: Samplename: 62.5 Root: 64.893 --> Root in between the borders! Added to results. Hyperbolic solved: 74.566810799042 - [20250519_003552.]: Samplename: 75 + [20250519_004320.]: Samplename: 75 Root: 74.567 --> Root in between the borders! Added to results. Hyperbolic solved: 84.5291854856128 - [20250519_003552.]: Samplename: 87.5 + [20250519_004320.]: Samplename: 87.5 Root: 84.529 --> Root in between the borders! Added to results. Hyperbolic solved: 101.046823491232 - [20250519_003552.]: Samplename: 100 + [20250519_004320.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.047 --> '100 < root < 110' --> substitute 100 - [20250519_003552.]: Solving hyperbolic regression for row_means + [20250519_004320.]: Solving hyperbolic regression for row_means Hyperbolic solved: 0.290936038655697 - [20250519_003552.]: Samplename: 0 + [20250519_004320.]: Samplename: 0 Root: 0.291 --> Root in between the borders! Added to results. Hyperbolic solved: 11.0412364555656 - [20250519_003552.]: Samplename: 12.5 + [20250519_004320.]: Samplename: 12.5 Root: 11.041 --> Root in between the borders! Added to results. Hyperbolic solved: 25.408146358228 - [20250519_003552.]: Samplename: 25 + [20250519_004320.]: Samplename: 25 Root: 25.408 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5243683648753 - [20250519_003552.]: Samplename: 37.5 + [20250519_004320.]: Samplename: 37.5 Root: 36.524 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7348788337891 - [20250519_003552.]: Samplename: 50 + [20250519_004320.]: Samplename: 50 Root: 50.735 --> Root in between the borders! Added to results. Hyperbolic solved: 65.3135169308955 - [20250519_003552.]: Samplename: 62.5 + [20250519_004320.]: Samplename: 62.5 Root: 65.314 --> Root in between the borders! Added to results. Hyperbolic solved: 75.5342663138126 - [20250519_003552.]: Samplename: 75 + [20250519_004320.]: Samplename: 75 Root: 75.534 --> Root in between the borders! Added to results. Hyperbolic solved: 83.2411177153911 - [20250519_003552.]: Samplename: 87.5 + [20250519_004320.]: Samplename: 87.5 Root: 83.241 --> Root in between the borders! Added to results. Hyperbolic solved: 101.666935931185 - [20250519_003552.]: Samplename: 100 + [20250519_004320.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.667 --> '100 < root < 110' --> substitute 100 - [20250519_003552.]: Entered 'clean_dt'-Function - [20250519_003552.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_003552.]: got experimental data - [20250519_003552.]: Entered 'clean_dt'-Function - [20250519_003552.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_003552.]: got calibration data - [20250519_003552.]: + [20250519_004321.]: Entered 'clean_dt'-Function + [20250519_004321.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_004321.]: got experimental data + [20250519_004321.]: Entered 'clean_dt'-Function + [20250519_004321.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_004321.]: got calibration data + [20250519_004321.]: ### Starting with regression calculations ### - [20250519_003552.]: Entered 'regression_type1'-Function + [20250519_004321.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : @@ -6902,253 +6899,253 @@ singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient - [20250519_003552.]: # CpG-site: CpG#1 + [20250519_004321.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_003553.]: Logging df_agg: CpG#1 - [20250519_003553.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003553.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_003553.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_003553.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_003553.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_003553.]: Entered 'hyperbolic_regression'-Function - [20250519_003553.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003553.]: Entered 'cubic_regression'-Function - [20250519_003553.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003553.]: # CpG-site: CpG#2 + [20250519_004321.]: Logging df_agg: CpG#1 + [20250519_004321.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004321.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_004321.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_004321.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_004321.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) + [20250519_004321.]: Entered 'hyperbolic_regression'-Function + [20250519_004321.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004321.]: Entered 'cubic_regression'-Function + [20250519_004321.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004321.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_003553.]: Logging df_agg: CpG#2 - [20250519_003553.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003553.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_003553.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_003553.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_003553.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_003553.]: Entered 'hyperbolic_regression'-Function - [20250519_003553.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003553.]: Entered 'cubic_regression'-Function - [20250519_003553.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003553.]: # CpG-site: CpG#3 + [20250519_004321.]: Logging df_agg: CpG#2 + [20250519_004321.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004321.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_004321.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_004321.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_004321.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) + [20250519_004321.]: Entered 'hyperbolic_regression'-Function + [20250519_004321.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004321.]: Entered 'cubic_regression'-Function + [20250519_004321.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004321.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_003553.]: Logging df_agg: CpG#3 - [20250519_003553.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003553.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_003553.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_003553.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_003553.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_003553.]: Entered 'hyperbolic_regression'-Function - [20250519_003553.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003554.]: Entered 'cubic_regression'-Function - [20250519_003554.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003554.]: # CpG-site: CpG#4 + [20250519_004321.]: Logging df_agg: CpG#3 + [20250519_004321.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004321.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_004321.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_004321.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_004321.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) + [20250519_004321.]: Entered 'hyperbolic_regression'-Function + [20250519_004321.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004322.]: Entered 'cubic_regression'-Function + [20250519_004322.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004322.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_003554.]: Logging df_agg: CpG#4 - [20250519_003554.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003554.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_003554.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_003554.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_003554.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_003554.]: Entered 'hyperbolic_regression'-Function - [20250519_003554.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003554.]: Entered 'cubic_regression'-Function - [20250519_003554.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003554.]: # CpG-site: CpG#5 + [20250519_004322.]: Logging df_agg: CpG#4 + [20250519_004322.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004322.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_004322.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_004322.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_004322.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) + [20250519_004322.]: Entered 'hyperbolic_regression'-Function + [20250519_004322.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004322.]: Entered 'cubic_regression'-Function + [20250519_004322.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004322.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_003554.]: Logging df_agg: CpG#5 - [20250519_003554.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003554.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_003554.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_003554.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_003554.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_003554.]: Entered 'hyperbolic_regression'-Function - [20250519_003554.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003554.]: Entered 'cubic_regression'-Function - [20250519_003554.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003554.]: # CpG-site: CpG#6 + [20250519_004322.]: Logging df_agg: CpG#5 + [20250519_004322.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004322.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_004322.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_004322.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_004322.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) + [20250519_004322.]: Entered 'hyperbolic_regression'-Function + [20250519_004322.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004323.]: Entered 'cubic_regression'-Function + [20250519_004323.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004323.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_003555.]: Logging df_agg: CpG#6 - [20250519_003555.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003555.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_003555.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_003555.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_003555.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_003555.]: Entered 'hyperbolic_regression'-Function - [20250519_003555.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003555.]: Entered 'cubic_regression'-Function - [20250519_003555.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003555.]: # CpG-site: CpG#7 + [20250519_004323.]: Logging df_agg: CpG#6 + [20250519_004323.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004323.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_004323.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_004323.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_004323.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) + [20250519_004323.]: Entered 'hyperbolic_regression'-Function + [20250519_004323.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004323.]: Entered 'cubic_regression'-Function + [20250519_004323.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004323.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_003555.]: Logging df_agg: CpG#7 - [20250519_003555.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003555.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_003555.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_003555.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_003555.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_003555.]: Entered 'hyperbolic_regression'-Function - [20250519_003555.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003555.]: Entered 'cubic_regression'-Function - [20250519_003555.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003555.]: # CpG-site: CpG#8 + [20250519_004323.]: Logging df_agg: CpG#7 + [20250519_004323.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004323.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_004323.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_004323.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_004323.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) + [20250519_004323.]: Entered 'hyperbolic_regression'-Function + [20250519_004323.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004323.]: Entered 'cubic_regression'-Function + [20250519_004323.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004323.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_003555.]: Logging df_agg: CpG#8 - [20250519_003555.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003555.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_003555.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_003555.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_003555.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_003555.]: Entered 'hyperbolic_regression'-Function - [20250519_003555.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003556.]: Entered 'cubic_regression'-Function - [20250519_003556.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003556.]: # CpG-site: CpG#9 + [20250519_004323.]: Logging df_agg: CpG#8 + [20250519_004323.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004323.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_004323.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_004323.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_004323.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) + [20250519_004323.]: Entered 'hyperbolic_regression'-Function + [20250519_004323.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004324.]: Entered 'cubic_regression'-Function + [20250519_004324.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004324.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_003556.]: Logging df_agg: CpG#9 - [20250519_003556.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003556.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_003556.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_003556.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_003556.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_003556.]: Entered 'hyperbolic_regression'-Function - [20250519_003556.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003556.]: Entered 'cubic_regression'-Function - [20250519_003556.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003556.]: # CpG-site: row_means + [20250519_004324.]: Logging df_agg: CpG#9 + [20250519_004324.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004324.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_004324.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_004324.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_004324.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) + [20250519_004324.]: Entered 'hyperbolic_regression'-Function + [20250519_004324.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004324.]: Entered 'cubic_regression'-Function + [20250519_004324.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004324.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_003556.]: Logging df_agg: row_means - [20250519_003556.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003556.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_003556.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_003556.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_003556.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_003556.]: Entered 'hyperbolic_regression'-Function - [20250519_003556.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003556.]: Entered 'cubic_regression'-Function - [20250519_003556.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003557.]: + [20250519_004324.]: Logging df_agg: row_means + [20250519_004324.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004324.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_004324.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_004324.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_004324.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) + [20250519_004324.]: Entered 'hyperbolic_regression'-Function + [20250519_004324.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004324.]: Entered 'cubic_regression'-Function + [20250519_004324.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004325.]: ### Starting with regression calculations ### - [20250519_003557.]: Entered 'regression_type1'-Function - [20250519_003557.]: # CpG-site: CpG#1 + [20250519_004325.]: Entered 'regression_type1'-Function + [20250519_004325.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_003557.]: Logging df_agg: CpG#1 - [20250519_003557.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003557.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_003557.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_003557.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_003557.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_003557.]: Entered 'hyperbolic_regression'-Function - [20250519_003557.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003558.]: Entered 'cubic_regression'-Function - [20250519_003558.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003558.]: # CpG-site: CpG#2 + [20250519_004325.]: Logging df_agg: CpG#1 + [20250519_004325.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004325.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_004325.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_004325.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_004325.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) + [20250519_004325.]: Entered 'hyperbolic_regression'-Function + [20250519_004325.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004326.]: Entered 'cubic_regression'-Function + [20250519_004326.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004326.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_003558.]: Logging df_agg: CpG#2 - [20250519_003558.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003558.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_003558.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_003558.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_003558.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_003558.]: Entered 'hyperbolic_regression'-Function - [20250519_003558.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003558.]: Entered 'cubic_regression'-Function - [20250519_003558.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003558.]: # CpG-site: CpG#3 + [20250519_004326.]: Logging df_agg: CpG#2 + [20250519_004326.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004326.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_004326.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_004326.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_004326.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) + [20250519_004326.]: Entered 'hyperbolic_regression'-Function + [20250519_004326.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004326.]: Entered 'cubic_regression'-Function + [20250519_004326.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004326.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_003558.]: Logging df_agg: CpG#3 - [20250519_003558.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003558.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_003558.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_003558.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_003558.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_003558.]: Entered 'hyperbolic_regression'-Function - [20250519_003558.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003558.]: Entered 'cubic_regression'-Function - [20250519_003558.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003559.]: # CpG-site: CpG#4 + [20250519_004326.]: Logging df_agg: CpG#3 + [20250519_004326.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004326.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_004326.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_004326.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_004326.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) + [20250519_004326.]: Entered 'hyperbolic_regression'-Function + [20250519_004326.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004326.]: Entered 'cubic_regression'-Function + [20250519_004326.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004327.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_003559.]: Logging df_agg: CpG#4 - [20250519_003559.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003559.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_003559.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_003559.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_003559.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_003559.]: Entered 'hyperbolic_regression'-Function - [20250519_003559.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003559.]: Entered 'cubic_regression'-Function - [20250519_003559.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003559.]: # CpG-site: CpG#5 + [20250519_004327.]: Logging df_agg: CpG#4 + [20250519_004327.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004327.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_004327.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_004327.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_004327.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) + [20250519_004327.]: Entered 'hyperbolic_regression'-Function + [20250519_004327.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004327.]: Entered 'cubic_regression'-Function + [20250519_004327.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004327.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_003559.]: Logging df_agg: CpG#5 - [20250519_003559.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003559.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_003559.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_003559.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_003559.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_003559.]: Entered 'hyperbolic_regression'-Function - [20250519_003559.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003559.]: Entered 'cubic_regression'-Function - [20250519_003559.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003559.]: # CpG-site: CpG#6 + [20250519_004327.]: Logging df_agg: CpG#5 + [20250519_004327.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004327.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_004327.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_004327.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_004327.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) + [20250519_004327.]: Entered 'hyperbolic_regression'-Function + [20250519_004327.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004327.]: Entered 'cubic_regression'-Function + [20250519_004327.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004327.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_003559.]: Logging df_agg: CpG#6 - [20250519_003559.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003559.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_003559.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_003559.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_003559.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_003559.]: Entered 'hyperbolic_regression'-Function - [20250519_003559.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003600.]: Entered 'cubic_regression'-Function - [20250519_003600.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003600.]: # CpG-site: CpG#7 + [20250519_004327.]: Logging df_agg: CpG#6 + [20250519_004327.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004327.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_004327.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_004327.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_004327.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) + [20250519_004327.]: Entered 'hyperbolic_regression'-Function + [20250519_004327.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004328.]: Entered 'cubic_regression'-Function + [20250519_004328.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004328.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_003600.]: Logging df_agg: CpG#7 - [20250519_003600.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003600.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_003600.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_003600.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_003600.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_003600.]: Entered 'hyperbolic_regression'-Function - [20250519_003600.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003600.]: Entered 'cubic_regression'-Function - [20250519_003600.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003600.]: # CpG-site: CpG#8 + [20250519_004328.]: Logging df_agg: CpG#7 + [20250519_004328.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004328.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_004328.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_004328.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_004328.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) + [20250519_004328.]: Entered 'hyperbolic_regression'-Function + [20250519_004328.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004328.]: Entered 'cubic_regression'-Function + [20250519_004328.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004328.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_003600.]: Logging df_agg: CpG#8 - [20250519_003600.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003600.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_003600.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_003600.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_003600.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_003600.]: Entered 'hyperbolic_regression'-Function - [20250519_003600.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003600.]: Entered 'cubic_regression'-Function - [20250519_003600.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003600.]: # CpG-site: CpG#9 + [20250519_004328.]: Logging df_agg: CpG#8 + [20250519_004328.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004328.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_004328.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_004328.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_004328.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) + [20250519_004328.]: Entered 'hyperbolic_regression'-Function + [20250519_004328.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004328.]: Entered 'cubic_regression'-Function + [20250519_004328.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004328.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_003600.]: Logging df_agg: CpG#9 - [20250519_003600.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003600.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_003600.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_003600.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_003600.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_003600.]: Entered 'hyperbolic_regression'-Function - [20250519_003600.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003601.]: Entered 'cubic_regression'-Function - [20250519_003601.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003601.]: # CpG-site: row_means + [20250519_004328.]: Logging df_agg: CpG#9 + [20250519_004328.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004328.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_004328.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_004328.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_004328.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) + [20250519_004328.]: Entered 'hyperbolic_regression'-Function + [20250519_004328.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004329.]: Entered 'cubic_regression'-Function + [20250519_004329.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004329.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_003601.]: Logging df_agg: row_means - [20250519_003601.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003601.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_003601.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_003601.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_003601.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_003601.]: Entered 'hyperbolic_regression'-Function - [20250519_003601.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003601.]: Entered 'cubic_regression'-Function - [20250519_003601.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003602.]: + [20250519_004329.]: Logging df_agg: row_means + [20250519_004329.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004329.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_004329.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_004329.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_004329.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) + [20250519_004329.]: Entered 'hyperbolic_regression'-Function + [20250519_004329.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004329.]: Entered 'cubic_regression'-Function + [20250519_004329.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004330.]: ### Starting with regression calculations ### - [20250519_003602.]: Entered 'regression_type1'-Function - [20250519_003603.]: # CpG-site: CpG#1 + [20250519_004330.]: Entered 'regression_type1'-Function + [20250519_004331.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_003603.]: Logging df_agg: CpG#1 - [20250519_003603.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003603.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_003603.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_003603.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_003603.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_003603.]: Entered 'hyperbolic_regression'-Function - [20250519_003603.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003603.]: Entered 'cubic_regression'-Function - [20250519_003603.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003603.]: # CpG-site: CpG#2 + [20250519_004331.]: Logging df_agg: CpG#1 + [20250519_004331.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004331.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_004331.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_004331.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_004331.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) + [20250519_004331.]: Entered 'hyperbolic_regression'-Function + [20250519_004331.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004331.]: Entered 'cubic_regression'-Function + [20250519_004331.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004331.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_003603.]: Logging df_agg: CpG#2 - [20250519_003603.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003603.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_003603.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_003603.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_003603.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_003603.]: Entered 'hyperbolic_regression'-Function - [20250519_003603.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003603.]: Entered 'cubic_regression'-Function - [20250519_003603.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003603.]: # CpG-site: CpG#3 + [20250519_004331.]: Logging df_agg: CpG#2 + [20250519_004331.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004331.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_004331.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_004331.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_004331.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) + [20250519_004331.]: Entered 'hyperbolic_regression'-Function + [20250519_004331.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004331.]: Entered 'cubic_regression'-Function + [20250519_004331.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004331.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_003603.]: Logging df_agg: CpG#3 - [20250519_003603.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003603.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_003603.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_003603.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_003603.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_003603.]: Entered 'hyperbolic_regression'-Function - [20250519_003603.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003603.]: Entered 'cubic_regression'-Function - [20250519_003603.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003603.]: # CpG-site: CpG#4 + [20250519_004331.]: Logging df_agg: CpG#3 + [20250519_004331.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004331.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_004331.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_004331.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_004331.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) + [20250519_004331.]: Entered 'hyperbolic_regression'-Function + [20250519_004331.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004331.]: Entered 'cubic_regression'-Function + [20250519_004331.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004331.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_003603.]: Logging df_agg: CpG#4 - [20250519_003603.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003603.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_003603.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_003603.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_003603.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_003603.]: Entered 'hyperbolic_regression'-Function - [20250519_003603.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003603.]: Entered 'cubic_regression'-Function - [20250519_003603.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003603.]: # CpG-site: CpG#5 + [20250519_004331.]: Logging df_agg: CpG#4 + [20250519_004331.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004331.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_004331.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_004331.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_004331.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) + [20250519_004331.]: Entered 'hyperbolic_regression'-Function + [20250519_004331.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004331.]: Entered 'cubic_regression'-Function + [20250519_004331.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004331.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_003603.]: Logging df_agg: CpG#5 - [20250519_003603.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003603.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_003603.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_003603.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_003603.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_003603.]: Entered 'hyperbolic_regression'-Function - [20250519_003603.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003603.]: Entered 'cubic_regression'-Function - [20250519_003603.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003603.]: # CpG-site: CpG#6 + [20250519_004331.]: Logging df_agg: CpG#5 + [20250519_004331.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004331.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_004331.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_004331.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_004331.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) + [20250519_004331.]: Entered 'hyperbolic_regression'-Function + [20250519_004331.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004331.]: Entered 'cubic_regression'-Function + [20250519_004331.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004331.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_003603.]: Logging df_agg: CpG#6 - [20250519_003603.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003603.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_003603.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_003603.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_003603.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_003603.]: Entered 'hyperbolic_regression'-Function - [20250519_003603.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003603.]: Entered 'cubic_regression'-Function - [20250519_003603.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003603.]: # CpG-site: CpG#7 + [20250519_004331.]: Logging df_agg: CpG#6 + [20250519_004331.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004331.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_004331.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_004331.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_004331.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) + [20250519_004331.]: Entered 'hyperbolic_regression'-Function + [20250519_004331.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004331.]: Entered 'cubic_regression'-Function + [20250519_004331.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004331.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_003603.]: Logging df_agg: CpG#7 - [20250519_003603.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003603.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_003603.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_003603.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_003603.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_003603.]: Entered 'hyperbolic_regression'-Function - [20250519_003603.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003603.]: Entered 'cubic_regression'-Function - [20250519_003603.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003603.]: # CpG-site: CpG#8 + [20250519_004331.]: Logging df_agg: CpG#7 + [20250519_004331.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004331.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_004331.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_004331.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_004331.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) + [20250519_004331.]: Entered 'hyperbolic_regression'-Function + [20250519_004331.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004331.]: Entered 'cubic_regression'-Function + [20250519_004331.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004331.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_003603.]: Logging df_agg: CpG#8 - [20250519_003603.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003603.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_003603.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_003603.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_003603.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_003603.]: Entered 'hyperbolic_regression'-Function - [20250519_003603.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003603.]: Entered 'cubic_regression'-Function - [20250519_003603.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003603.]: # CpG-site: CpG#9 + [20250519_004331.]: Logging df_agg: CpG#8 + [20250519_004331.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004331.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_004331.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_004331.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_004331.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) + [20250519_004331.]: Entered 'hyperbolic_regression'-Function + [20250519_004331.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004331.]: Entered 'cubic_regression'-Function + [20250519_004331.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004331.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_003603.]: Logging df_agg: CpG#9 - [20250519_003603.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003603.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_003603.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_003603.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_003603.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_003603.]: Entered 'hyperbolic_regression'-Function - [20250519_003603.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003603.]: Entered 'cubic_regression'-Function - [20250519_003603.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003603.]: # CpG-site: row_means + [20250519_004331.]: Logging df_agg: CpG#9 + [20250519_004331.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004331.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_004331.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_004331.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_004331.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) + [20250519_004331.]: Entered 'hyperbolic_regression'-Function + [20250519_004331.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004331.]: Entered 'cubic_regression'-Function + [20250519_004331.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004331.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_003603.]: Logging df_agg: row_means - [20250519_003603.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003603.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_003603.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_003603.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_003603.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_003603.]: Entered 'hyperbolic_regression'-Function - [20250519_003603.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003603.]: Entered 'cubic_regression'-Function - [20250519_003603.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003605.]: Entered 'regression_type1'-Function + [20250519_004331.]: Logging df_agg: row_means + [20250519_004331.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004331.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_004331.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_004331.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_004331.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) + [20250519_004331.]: Entered 'hyperbolic_regression'-Function + [20250519_004331.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004331.]: Entered 'cubic_regression'-Function + [20250519_004331.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004332.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : @@ -7687,95 +7684,95 @@ singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient - [20250519_003605.]: # CpG-site: CpG#1 + [20250519_004333.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_003605.]: Logging df_agg: CpG#1 - [20250519_003605.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003605.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_003605.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_003605.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_003605.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_003605.]: Entered 'hyperbolic_regression'-Function - [20250519_003605.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003605.]: Entered 'cubic_regression'-Function - [20250519_003605.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003606.]: # CpG-site: CpG#2 + [20250519_004333.]: Logging df_agg: CpG#1 + [20250519_004333.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004333.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_004333.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_004333.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_004333.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) + [20250519_004333.]: Entered 'hyperbolic_regression'-Function + [20250519_004333.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004333.]: Entered 'cubic_regression'-Function + [20250519_004333.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004333.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_003606.]: Logging df_agg: CpG#2 - [20250519_003606.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003606.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_003606.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_003606.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_003606.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_003606.]: Entered 'hyperbolic_regression'-Function - [20250519_003606.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003606.]: Entered 'cubic_regression'-Function - [20250519_003606.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003606.]: # CpG-site: CpG#3 + [20250519_004333.]: Logging df_agg: CpG#2 + [20250519_004333.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004333.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_004333.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_004333.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_004333.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) + [20250519_004333.]: Entered 'hyperbolic_regression'-Function + [20250519_004333.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004333.]: Entered 'cubic_regression'-Function + [20250519_004333.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004334.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_003606.]: Logging df_agg: CpG#3 - [20250519_003606.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003606.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_003606.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_003606.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_003606.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_003606.]: Entered 'hyperbolic_regression'-Function - [20250519_003606.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003606.]: Entered 'cubic_regression'-Function - [20250519_003606.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003606.]: # CpG-site: CpG#4 + [20250519_004334.]: Logging df_agg: CpG#3 + [20250519_004334.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004334.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_004334.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_004334.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_004334.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) + [20250519_004334.]: Entered 'hyperbolic_regression'-Function + [20250519_004334.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004334.]: Entered 'cubic_regression'-Function + [20250519_004334.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004334.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_003606.]: Logging df_agg: CpG#4 - [20250519_003606.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003606.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_003606.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_003606.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_003606.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_003606.]: Entered 'hyperbolic_regression'-Function - [20250519_003606.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003607.]: Entered 'cubic_regression'-Function - [20250519_003607.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003607.]: # CpG-site: CpG#5 + [20250519_004334.]: Logging df_agg: CpG#4 + [20250519_004334.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004334.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_004334.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_004334.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_004334.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) + [20250519_004334.]: Entered 'hyperbolic_regression'-Function + [20250519_004334.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004334.]: Entered 'cubic_regression'-Function + [20250519_004334.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004334.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_003607.]: Logging df_agg: CpG#5 - [20250519_003607.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003607.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_003607.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_003607.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_003607.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_003607.]: Entered 'hyperbolic_regression'-Function - [20250519_003607.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003607.]: Entered 'cubic_regression'-Function - [20250519_003607.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003607.]: # CpG-site: CpG#6 + [20250519_004334.]: Logging df_agg: CpG#5 + [20250519_004334.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004334.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_004334.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_004334.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_004334.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) + [20250519_004334.]: Entered 'hyperbolic_regression'-Function + [20250519_004334.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004335.]: Entered 'cubic_regression'-Function + [20250519_004335.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004335.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_003607.]: Logging df_agg: CpG#6 - [20250519_003607.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003607.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_003607.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_003607.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_003607.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_003607.]: Entered 'hyperbolic_regression'-Function - [20250519_003607.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003607.]: Entered 'cubic_regression'-Function - [20250519_003607.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003607.]: # CpG-site: CpG#7 + [20250519_004335.]: Logging df_agg: CpG#6 + [20250519_004335.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004335.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_004335.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_004335.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_004335.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) + [20250519_004335.]: Entered 'hyperbolic_regression'-Function + [20250519_004335.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004335.]: Entered 'cubic_regression'-Function + [20250519_004335.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004335.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_003607.]: Logging df_agg: CpG#7 - [20250519_003607.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003607.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_003607.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_003607.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_003607.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_003607.]: Entered 'hyperbolic_regression'-Function - [20250519_003607.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003608.]: Entered 'cubic_regression'-Function - [20250519_003608.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003608.]: # CpG-site: CpG#8 + [20250519_004335.]: Logging df_agg: CpG#7 + [20250519_004335.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004335.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_004335.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_004335.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_004335.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) + [20250519_004335.]: Entered 'hyperbolic_regression'-Function + [20250519_004335.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004335.]: Entered 'cubic_regression'-Function + [20250519_004335.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004335.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_003608.]: Logging df_agg: CpG#8 - [20250519_003608.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003608.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_003608.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_003608.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_003608.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_003608.]: Entered 'hyperbolic_regression'-Function - [20250519_003608.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003608.]: Entered 'cubic_regression'-Function - [20250519_003608.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003608.]: # CpG-site: CpG#9 + [20250519_004335.]: Logging df_agg: CpG#8 + [20250519_004335.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004335.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_004335.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_004335.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_004335.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) + [20250519_004335.]: Entered 'hyperbolic_regression'-Function + [20250519_004335.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004336.]: Entered 'cubic_regression'-Function + [20250519_004336.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004336.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_003608.]: Logging df_agg: CpG#9 - [20250519_003608.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003608.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_003608.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_003608.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_003608.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_003608.]: Entered 'hyperbolic_regression'-Function - [20250519_003608.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003608.]: Entered 'cubic_regression'-Function - [20250519_003608.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003608.]: # CpG-site: row_means + [20250519_004336.]: Logging df_agg: CpG#9 + [20250519_004336.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004336.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_004336.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_004336.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_004336.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) + [20250519_004336.]: Entered 'hyperbolic_regression'-Function + [20250519_004336.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004336.]: Entered 'cubic_regression'-Function + [20250519_004336.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004336.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_003608.]: Logging df_agg: row_means - [20250519_003608.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003608.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_003608.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_003608.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_003608.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_003608.]: Entered 'hyperbolic_regression'-Function - [20250519_003608.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003609.]: Entered 'cubic_regression'-Function - [20250519_003609.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003609.]: Entered 'clean_dt'-Function - [20250519_003610.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_003610.]: got experimental data - [20250519_003610.]: Entered 'clean_dt'-Function - [20250519_003610.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_003610.]: got calibration data - [20250519_003610.]: + [20250519_004336.]: Logging df_agg: row_means + [20250519_004336.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004336.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_004336.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_004336.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_004336.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) + [20250519_004336.]: Entered 'hyperbolic_regression'-Function + [20250519_004336.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004336.]: Entered 'cubic_regression'-Function + [20250519_004336.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004337.]: Entered 'clean_dt'-Function + [20250519_004337.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_004337.]: got experimental data + [20250519_004337.]: Entered 'clean_dt'-Function + [20250519_004337.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_004337.]: got calibration data + [20250519_004337.]: ### Starting with regression calculations ### - [20250519_003610.]: Entered 'regression_type1'-Function + [20250519_004337.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : @@ -8314,87 +8311,87 @@ singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient - [20250519_003610.]: # CpG-site: CpG#1 + [20250519_004337.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_003610.]: Logging df_agg: CpG#1 - [20250519_003610.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003610.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_003610.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_003610.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_003610.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_003610.]: Entered 'hyperbolic_regression'-Function - [20250519_003610.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003610.]: Entered 'cubic_regression'-Function - [20250519_003610.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003610.]: # CpG-site: CpG#2 + [20250519_004337.]: Logging df_agg: CpG#1 + [20250519_004337.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004337.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_004337.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_004337.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_004337.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) + [20250519_004337.]: Entered 'hyperbolic_regression'-Function + [20250519_004337.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004337.]: Entered 'cubic_regression'-Function + [20250519_004337.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004338.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_003610.]: Logging df_agg: CpG#2 - [20250519_003610.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003610.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_003610.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_003610.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_003610.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_003610.]: Entered 'hyperbolic_regression'-Function - [20250519_003610.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003610.]: Entered 'cubic_regression'-Function - [20250519_003610.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003610.]: # CpG-site: CpG#3 + [20250519_004338.]: Logging df_agg: CpG#2 + [20250519_004338.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004338.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_004338.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_004338.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_004338.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) + [20250519_004338.]: Entered 'hyperbolic_regression'-Function + [20250519_004338.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004338.]: Entered 'cubic_regression'-Function + [20250519_004338.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004338.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_003610.]: Logging df_agg: CpG#3 - [20250519_003610.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003610.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_003610.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_003610.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_003610.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_003610.]: Entered 'hyperbolic_regression'-Function - [20250519_003610.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003611.]: Entered 'cubic_regression'-Function - [20250519_003611.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003611.]: # CpG-site: CpG#4 + [20250519_004338.]: Logging df_agg: CpG#3 + [20250519_004338.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004338.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_004338.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_004338.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_004338.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) + [20250519_004338.]: Entered 'hyperbolic_regression'-Function + [20250519_004338.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004338.]: Entered 'cubic_regression'-Function + [20250519_004338.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004338.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_003611.]: Logging df_agg: CpG#4 - [20250519_003611.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003611.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_003611.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_003611.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_003611.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_003611.]: Entered 'hyperbolic_regression'-Function - [20250519_003611.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003611.]: Entered 'cubic_regression'-Function - [20250519_003611.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003611.]: # CpG-site: CpG#5 + [20250519_004338.]: Logging df_agg: CpG#4 + [20250519_004338.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004338.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_004338.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_004338.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_004338.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) + [20250519_004338.]: Entered 'hyperbolic_regression'-Function + [20250519_004338.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004339.]: Entered 'cubic_regression'-Function + [20250519_004339.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004339.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_003611.]: Logging df_agg: CpG#5 - [20250519_003611.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003611.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_003611.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_003611.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_003611.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_003611.]: Entered 'hyperbolic_regression'-Function - [20250519_003611.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003611.]: Entered 'cubic_regression'-Function - [20250519_003611.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003612.]: # CpG-site: CpG#6 + [20250519_004339.]: Logging df_agg: CpG#5 + [20250519_004339.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004339.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_004339.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_004339.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_004339.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) + [20250519_004339.]: Entered 'hyperbolic_regression'-Function + [20250519_004339.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004339.]: Entered 'cubic_regression'-Function + [20250519_004339.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004339.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_003612.]: Logging df_agg: CpG#6 - [20250519_003612.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003612.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_003612.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_003612.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_003612.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_003612.]: Entered 'hyperbolic_regression'-Function - [20250519_003612.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003612.]: Entered 'cubic_regression'-Function - [20250519_003612.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003612.]: # CpG-site: CpG#7 + [20250519_004339.]: Logging df_agg: CpG#6 + [20250519_004339.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004339.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_004339.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_004339.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_004339.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) + [20250519_004339.]: Entered 'hyperbolic_regression'-Function + [20250519_004339.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004339.]: Entered 'cubic_regression'-Function + [20250519_004339.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004339.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_003612.]: Logging df_agg: CpG#7 - [20250519_003612.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003612.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_003612.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_003612.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_003612.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_003612.]: Entered 'hyperbolic_regression'-Function - [20250519_003612.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003612.]: Entered 'cubic_regression'-Function - [20250519_003612.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003612.]: # CpG-site: CpG#8 + [20250519_004339.]: Logging df_agg: CpG#7 + [20250519_004339.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004339.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_004339.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_004339.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_004339.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) + [20250519_004339.]: Entered 'hyperbolic_regression'-Function + [20250519_004339.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004340.]: Entered 'cubic_regression'-Function + [20250519_004340.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004340.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_003612.]: Logging df_agg: CpG#8 - [20250519_003612.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003612.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_003612.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_003612.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_003612.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_003612.]: Entered 'hyperbolic_regression'-Function - [20250519_003612.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003613.]: Entered 'cubic_regression'-Function - [20250519_003613.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003613.]: # CpG-site: CpG#9 + [20250519_004340.]: Logging df_agg: CpG#8 + [20250519_004340.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004340.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_004340.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_004340.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_004340.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) + [20250519_004340.]: Entered 'hyperbolic_regression'-Function + [20250519_004340.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004340.]: Entered 'cubic_regression'-Function + [20250519_004340.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004340.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_003613.]: Logging df_agg: CpG#9 - [20250519_003613.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003613.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_003613.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_003613.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_003613.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_003613.]: Entered 'hyperbolic_regression'-Function - [20250519_003613.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003613.]: Entered 'cubic_regression'-Function - [20250519_003613.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003613.]: # CpG-site: row_means + [20250519_004340.]: Logging df_agg: CpG#9 + [20250519_004340.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004340.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_004340.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_004340.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_004340.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) + [20250519_004340.]: Entered 'hyperbolic_regression'-Function + [20250519_004340.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004340.]: Entered 'cubic_regression'-Function + [20250519_004340.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004340.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_003613.]: Logging df_agg: row_means - [20250519_003613.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003613.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_003613.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_003613.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_003613.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_003613.]: Entered 'hyperbolic_regression'-Function - [20250519_003613.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003613.]: Entered 'cubic_regression'-Function - [20250519_003613.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003614.]: Entered 'regression_type1'-Function + [20250519_004341.]: Logging df_agg: row_means + [20250519_004341.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004341.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_004341.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_004341.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_004341.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) + [20250519_004341.]: Entered 'hyperbolic_regression'-Function + [20250519_004341.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004341.]: Entered 'cubic_regression'-Function + [20250519_004341.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004341.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : @@ -8933,464 +8930,464 @@ singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient - [20250519_003614.]: # CpG-site: CpG#1 + [20250519_004342.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_003614.]: Logging df_agg: CpG#1 - [20250519_003614.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003614.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_003614.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_003614.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_003614.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_003614.]: Entered 'hyperbolic_regression'-Function - [20250519_003614.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003615.]: Entered 'cubic_regression'-Function - [20250519_003615.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003615.]: # CpG-site: CpG#2 + [20250519_004342.]: Logging df_agg: CpG#1 + [20250519_004342.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004342.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_004342.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_004342.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_004342.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) + [20250519_004342.]: Entered 'hyperbolic_regression'-Function + [20250519_004342.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004342.]: Entered 'cubic_regression'-Function + [20250519_004342.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004342.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_003615.]: Logging df_agg: CpG#2 - [20250519_003615.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003615.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_003615.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_003615.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_003615.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_003615.]: Entered 'hyperbolic_regression'-Function - [20250519_003615.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003615.]: Entered 'cubic_regression'-Function - [20250519_003615.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003615.]: # CpG-site: CpG#3 + [20250519_004342.]: Logging df_agg: CpG#2 + [20250519_004342.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004342.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_004342.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_004342.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_004342.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) + [20250519_004342.]: Entered 'hyperbolic_regression'-Function + [20250519_004342.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004342.]: Entered 'cubic_regression'-Function + [20250519_004342.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004342.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_003615.]: Logging df_agg: CpG#3 - [20250519_003615.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003615.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_003615.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_003615.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_003615.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_003615.]: Entered 'hyperbolic_regression'-Function - [20250519_003615.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003615.]: Entered 'cubic_regression'-Function - [20250519_003615.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003615.]: # CpG-site: CpG#4 + [20250519_004342.]: Logging df_agg: CpG#3 + [20250519_004342.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004342.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_004342.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_004342.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_004342.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) + [20250519_004342.]: Entered 'hyperbolic_regression'-Function + [20250519_004342.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004343.]: Entered 'cubic_regression'-Function + [20250519_004343.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004343.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_003615.]: Logging df_agg: CpG#4 - [20250519_003615.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003615.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_003615.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_003615.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_003615.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_003615.]: Entered 'hyperbolic_regression'-Function - [20250519_003615.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003616.]: Entered 'cubic_regression'-Function - [20250519_003616.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003616.]: # CpG-site: CpG#5 + [20250519_004343.]: Logging df_agg: CpG#4 + [20250519_004343.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004343.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_004343.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_004343.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_004343.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) + [20250519_004343.]: Entered 'hyperbolic_regression'-Function + [20250519_004343.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004343.]: Entered 'cubic_regression'-Function + [20250519_004343.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004343.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_003616.]: Logging df_agg: CpG#5 - [20250519_003616.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003616.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_003616.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_003616.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_003616.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_003616.]: Entered 'hyperbolic_regression'-Function - [20250519_003616.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003616.]: Entered 'cubic_regression'-Function - [20250519_003616.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003616.]: # CpG-site: CpG#6 + [20250519_004343.]: Logging df_agg: CpG#5 + [20250519_004343.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004343.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_004343.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_004343.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_004343.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) + [20250519_004343.]: Entered 'hyperbolic_regression'-Function + [20250519_004343.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004343.]: Entered 'cubic_regression'-Function + [20250519_004343.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004344.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_003616.]: Logging df_agg: CpG#6 - [20250519_003616.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003616.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_003616.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_003616.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_003616.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_003616.]: Entered 'hyperbolic_regression'-Function - [20250519_003616.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003617.]: Entered 'cubic_regression'-Function - [20250519_003617.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003617.]: # CpG-site: CpG#7 + [20250519_004344.]: Logging df_agg: CpG#6 + [20250519_004344.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004344.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_004344.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_004344.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_004344.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) + [20250519_004344.]: Entered 'hyperbolic_regression'-Function + [20250519_004344.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004344.]: Entered 'cubic_regression'-Function + [20250519_004344.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004344.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_003617.]: Logging df_agg: CpG#7 - [20250519_003617.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003617.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_003617.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_003617.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_003617.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_003617.]: Entered 'hyperbolic_regression'-Function - [20250519_003617.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003617.]: Entered 'cubic_regression'-Function - [20250519_003617.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003617.]: # CpG-site: CpG#8 + [20250519_004344.]: Logging df_agg: CpG#7 + [20250519_004344.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004344.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_004344.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_004344.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_004344.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) + [20250519_004344.]: Entered 'hyperbolic_regression'-Function + [20250519_004344.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004344.]: Entered 'cubic_regression'-Function + [20250519_004344.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004344.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_003617.]: Logging df_agg: CpG#8 - [20250519_003617.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003617.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_003617.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_003617.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_003617.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_003617.]: Entered 'hyperbolic_regression'-Function - [20250519_003617.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003617.]: Entered 'cubic_regression'-Function - [20250519_003617.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003617.]: # CpG-site: CpG#9 + [20250519_004344.]: Logging df_agg: CpG#8 + [20250519_004344.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004344.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_004344.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_004344.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_004344.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) + [20250519_004344.]: Entered 'hyperbolic_regression'-Function + [20250519_004344.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004345.]: Entered 'cubic_regression'-Function + [20250519_004345.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004345.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_003617.]: Logging df_agg: CpG#9 - [20250519_003617.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003617.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_003617.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_003617.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_003617.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_003617.]: Entered 'hyperbolic_regression'-Function - [20250519_003617.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003618.]: Entered 'cubic_regression'-Function - [20250519_003618.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003618.]: # CpG-site: row_means + [20250519_004345.]: Logging df_agg: CpG#9 + [20250519_004345.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004345.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_004345.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_004345.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_004345.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) + [20250519_004345.]: Entered 'hyperbolic_regression'-Function + [20250519_004345.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004345.]: Entered 'cubic_regression'-Function + [20250519_004345.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004345.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_003618.]: Logging df_agg: row_means - [20250519_003618.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003618.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_003618.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_003618.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_003618.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_003618.]: Entered 'hyperbolic_regression'-Function - [20250519_003618.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003618.]: Entered 'cubic_regression'-Function - [20250519_003618.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003619.]: Entered 'solving_equations'-Function - [20250519_003619.]: Solving hyperbolic regression for CpG#1 + [20250519_004345.]: Logging df_agg: row_means + [20250519_004345.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004345.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_004345.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_004345.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_004345.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) + [20250519_004345.]: Entered 'hyperbolic_regression'-Function + [20250519_004345.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004345.]: Entered 'cubic_regression'-Function + [20250519_004345.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004346.]: Entered 'solving_equations'-Function + [20250519_004346.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 0 - [20250519_003619.]: Samplename: 0 + [20250519_004346.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 14.1480167109277 - [20250519_003619.]: Samplename: 12.5 + [20250519_004346.]: Samplename: 12.5 Root: 14.148 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1398443790446 - [20250519_003619.]: Samplename: 25 + [20250519_004346.]: Samplename: 25 Root: 26.14 --> Root in between the borders! Added to results. Hyperbolic solved: 39.3762039523382 - [20250519_003619.]: Samplename: 37.5 + [20250519_004346.]: Samplename: 37.5 Root: 39.376 --> Root in between the borders! Added to results. Hyperbolic solved: 52.9476244028783 - [20250519_003619.]: Samplename: 50 + [20250519_004346.]: Samplename: 50 Root: 52.948 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4195105873419 - [20250519_003619.]: Samplename: 62.5 + [20250519_004346.]: Samplename: 62.5 Root: 65.42 --> Root in between the borders! Added to results. Hyperbolic solved: 74.433837698331 - [20250519_003619.]: Samplename: 75 + [20250519_004346.]: Samplename: 75 Root: 74.434 --> Root in between the borders! Added to results. Hyperbolic solved: 80.5559264925808 - [20250519_003619.]: Samplename: 87.5 + [20250519_004346.]: Samplename: 87.5 Root: 80.556 --> Root in between the borders! Added to results. Hyperbolic solved: 100 - [20250519_003619.]: Samplename: 100 + [20250519_004346.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. - [20250519_003619.]: Solving hyperbolic regression for CpG#2 + [20250519_004346.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 0 - [20250519_003619.]: Samplename: 0 + [20250519_004346.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 10.7812303822548 - [20250519_003619.]: Samplename: 12.5 + [20250519_004346.]: Samplename: 12.5 Root: 10.781 --> Root in between the borders! Added to results. Hyperbolic solved: 26.0648314401535 - [20250519_003619.]: Samplename: 25 + [20250519_004346.]: Samplename: 25 Root: 26.065 --> Root in between the borders! Added to results. Hyperbolic solved: 35.1980950192158 - [20250519_003619.]: Samplename: 37.5 + [20250519_004346.]: Samplename: 37.5 Root: 35.198 --> Root in between the borders! Added to results. Hyperbolic solved: 47.9203836090837 - [20250519_003619.]: Samplename: 50 + [20250519_004346.]: Samplename: 50 Root: 47.92 --> Root in between the borders! Added to results. Hyperbolic solved: 67.275750545092 - [20250519_003619.]: Samplename: 62.5 + [20250519_004346.]: Samplename: 62.5 Root: 67.276 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7278143684244 - [20250519_003619.]: Samplename: 75 + [20250519_004346.]: Samplename: 75 Root: 75.728 --> Root in between the borders! Added to results. Hyperbolic solved: 84.1258428690749 - [20250519_003619.]: Samplename: 87.5 + [20250519_004346.]: Samplename: 87.5 Root: 84.126 --> Root in between the borders! Added to results. Hyperbolic solved: 100 - [20250519_003619.]: Samplename: 100 + [20250519_004346.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. - [20250519_003619.]: Solving hyperbolic regression for CpG#3 + [20250519_004346.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 0 - [20250519_003619.]: Samplename: 0 + [20250519_004346.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 10.8467198180169 - [20250519_003619.]: Samplename: 12.5 + [20250519_004346.]: Samplename: 12.5 Root: 10.847 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1449700612555 - [20250519_003619.]: Samplename: 25 + [20250519_004346.]: Samplename: 25 Root: 26.145 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2865760013773 - [20250519_003619.]: Samplename: 37.5 + [20250519_004346.]: Samplename: 37.5 Root: 37.287 --> Root in between the borders! Added to results. Hyperbolic solved: 51.4114078772697 - [20250519_003619.]: Samplename: 50 + [20250519_004346.]: Samplename: 50 Root: 51.411 --> Root in between the borders! Added to results. Hyperbolic solved: 65.0139634978039 - [20250519_003619.]: Samplename: 62.5 + [20250519_004346.]: Samplename: 62.5 Root: 65.014 --> Root in between the borders! Added to results. Hyperbolic solved: 76.9921394830877 - [20250519_003619.]: Samplename: 75 + [20250519_004346.]: Samplename: 75 Root: 76.992 --> Root in between the borders! Added to results. Hyperbolic solved: 79.6808818589171 - [20250519_003619.]: Samplename: 87.5 + [20250519_004346.]: Samplename: 87.5 Root: 79.681 --> Root in between the borders! Added to results. Hyperbolic solved: 100 - [20250519_003619.]: Samplename: 100 + [20250519_004346.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. - [20250519_003619.]: Solving hyperbolic regression for CpG#4 + [20250519_004346.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 0 - [20250519_003619.]: Samplename: 0 + [20250519_004346.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 13.2494913489925 - [20250519_003619.]: Samplename: 12.5 + [20250519_004346.]: Samplename: 12.5 Root: 13.249 --> Root in between the borders! Added to results. Hyperbolic solved: 25.0914701740206 - [20250519_003619.]: Samplename: 25 + [20250519_004346.]: Samplename: 25 Root: 25.091 --> Root in between the borders! Added to results. Hyperbolic solved: 38.8081740444332 - [20250519_003619.]: Samplename: 37.5 + [20250519_004346.]: Samplename: 37.5 Root: 38.808 --> Root in between the borders! Added to results. Hyperbolic solved: 49.1133034150896 - [20250519_003619.]: Samplename: 50 + [20250519_004346.]: Samplename: 50 Root: 49.113 --> Root in between the borders! Added to results. Hyperbolic solved: 67.573566028728 - [20250519_003619.]: Samplename: 62.5 + [20250519_004346.]: Samplename: 62.5 Root: 67.574 --> Root in between the borders! Added to results. Hyperbolic solved: 73.7655861480302 - [20250519_003619.]: Samplename: 75 + [20250519_004346.]: Samplename: 75 Root: 73.766 --> Root in between the borders! Added to results. Hyperbolic solved: 82.0404940898918 - [20250519_003619.]: Samplename: 87.5 + [20250519_004346.]: Samplename: 87.5 Root: 82.04 --> Root in between the borders! Added to results. Hyperbolic solved: 100 - [20250519_003619.]: Samplename: 100 + [20250519_004346.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. - [20250519_003619.]: Solving hyperbolic regression for CpG#5 + [20250519_004346.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 0 - [20250519_003619.]: Samplename: 0 + [20250519_004346.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 8.36101271071658 - [20250519_003619.]: Samplename: 12.5 + [20250519_004346.]: Samplename: 12.5 Root: 8.361 --> Root in between the borders! Added to results. Hyperbolic solved: 23.0724671456609 - [20250519_003619.]: Samplename: 25 + [20250519_004346.]: Samplename: 25 Root: 23.072 --> Root in between the borders! Added to results. Hyperbolic solved: 36.9926902689013 - [20250519_003619.]: Samplename: 37.5 + [20250519_004346.]: Samplename: 37.5 Root: 36.993 --> Root in between the borders! Added to results. Hyperbolic solved: 50.9902012556783 - [20250519_003619.]: Samplename: 50 + [20250519_004346.]: Samplename: 50 Root: 50.99 --> Root in between the borders! Added to results. Hyperbolic solved: 62.7269420395599 - [20250519_003619.]: Samplename: 62.5 + [20250519_004346.]: Samplename: 62.5 Root: 62.727 --> Root in between the borders! Added to results. Hyperbolic solved: 76.6726263557269 - [20250519_003619.]: Samplename: 75 + [20250519_004346.]: Samplename: 75 Root: 76.673 --> Root in between the borders! Added to results. Hyperbolic solved: 86.2959101486824 - [20250519_003619.]: Samplename: 87.5 + [20250519_004346.]: Samplename: 87.5 Root: 86.296 --> Root in between the borders! Added to results. Hyperbolic solved: 100 - [20250519_003619.]: Samplename: 100 + [20250519_004346.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. - [20250519_003619.]: Solving hyperbolic regression for CpG#6 + [20250519_004346.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 0 - [20250519_003619.]: Samplename: 0 + [20250519_004346.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 11.8139244542506 - [20250519_003619.]: Samplename: 12.5 + [20250519_004346.]: Samplename: 12.5 Root: 11.814 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5330423964606 - [20250519_003619.]: Samplename: 25 + [20250519_004346.]: Samplename: 25 Root: 26.533 --> Root in between the borders! Added to results. Hyperbolic solved: 35.365455365759 - [20250519_003619.]: Samplename: 37.5 + [20250519_004346.]: Samplename: 37.5 Root: 35.365 --> Root in between the borders! Added to results. Hyperbolic solved: 50.1054345020203 - [20250519_003619.]: Samplename: 50 + [20250519_004346.]: Samplename: 50 Root: 50.105 --> Root in between the borders! Added to results. Hyperbolic solved: 64.9683746571925 - [20250519_003619.]: Samplename: 62.5 + [20250519_004346.]: Samplename: 62.5 Root: 64.968 --> Root in between the borders! Added to results. Hyperbolic solved: 73.6331726521876 - [20250519_003619.]: Samplename: 75 + [20250519_004346.]: Samplename: 75 Root: 73.633 --> Root in between the borders! Added to results. Hyperbolic solved: 86.9938602772804 - [20250519_003619.]: Samplename: 87.5 + [20250519_004346.]: Samplename: 87.5 Root: 86.994 --> Root in between the borders! Added to results. Hyperbolic solved: 100 - [20250519_003619.]: Samplename: 100 + [20250519_004346.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. - [20250519_003619.]: Solving hyperbolic regression for CpG#7 + [20250519_004346.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 0 - [20250519_003619.]: Samplename: 0 + [20250519_004346.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 11.7880491100164 - [20250519_003619.]: Samplename: 12.5 + [20250519_004346.]: Samplename: 12.5 Root: 11.788 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1959234034328 - [20250519_003619.]: Samplename: 25 + [20250519_004346.]: Samplename: 25 Root: 26.196 --> Root in between the borders! Added to results. Hyperbolic solved: 39.1978567589215 - [20250519_003619.]: Samplename: 37.5 + [20250519_004346.]: Samplename: 37.5 Root: 39.198 --> Root in between the borders! Added to results. Hyperbolic solved: 54.3513505291649 - [20250519_003619.]: Samplename: 50 + [20250519_004346.]: Samplename: 50 Root: 54.351 --> Root in between the borders! Added to results. Hyperbolic solved: 66.0567953704493 - [20250519_003619.]: Samplename: 62.5 + [20250519_004346.]: Samplename: 62.5 Root: 66.057 --> Root in between the borders! Added to results. Hyperbolic solved: 75.1900867269869 - [20250519_003619.]: Samplename: 75 + [20250519_004346.]: Samplename: 75 Root: 75.19 --> Root in between the borders! Added to results. Hyperbolic solved: 78.6051660349008 - [20250519_003619.]: Samplename: 87.5 + [20250519_004346.]: Samplename: 87.5 Root: 78.605 --> Root in between the borders! Added to results. Hyperbolic solved: 100 - [20250519_003619.]: Samplename: 100 + [20250519_004346.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. - [20250519_003619.]: Solving hyperbolic regression for CpG#8 + [20250519_004346.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 0 - [20250519_003619.]: Samplename: 0 + [20250519_004346.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 7.27382484148278 - [20250519_003619.]: Samplename: 12.5 + [20250519_004346.]: Samplename: 12.5 Root: 7.274 --> Root in between the borders! Added to results. Hyperbolic solved: 24.9765598282779 - [20250519_003619.]: Samplename: 25 + [20250519_004346.]: Samplename: 25 Root: 24.977 --> Root in between the borders! Added to results. Hyperbolic solved: 34.028313807905 - [20250519_003619.]: Samplename: 37.5 + [20250519_004346.]: Samplename: 37.5 Root: 34.028 --> Root in between the borders! Added to results. Hyperbolic solved: 52.2946423509726 - [20250519_003619.]: Samplename: 50 + [20250519_004346.]: Samplename: 50 Root: 52.295 --> Root in between the borders! Added to results. Hyperbolic solved: 65.0742432627639 - [20250519_003619.]: Samplename: 62.5 + [20250519_004346.]: Samplename: 62.5 Root: 65.074 --> Root in between the borders! Added to results. Hyperbolic solved: 78.3047550247862 - [20250519_003619.]: Samplename: 75 + [20250519_004346.]: Samplename: 75 Root: 78.305 --> Root in between the borders! Added to results. Hyperbolic solved: 81.0501991298613 - [20250519_003619.]: Samplename: 87.5 + [20250519_004346.]: Samplename: 87.5 Root: 81.05 --> Root in between the borders! Added to results. Hyperbolic solved: 100 - [20250519_003619.]: Samplename: 100 + [20250519_004346.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. - [20250519_003619.]: Solving hyperbolic regression for CpG#9 + [20250519_004346.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 0 - [20250519_003619.]: Samplename: 0 + [20250519_004346.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 12.2068234201026 - [20250519_003619.]: Samplename: 12.5 + [20250519_004346.]: Samplename: 12.5 Root: 12.207 --> Root in between the borders! Added to results. Hyperbolic solved: 28.0688737636578 - [20250519_003619.]: Samplename: 25 + [20250519_004346.]: Samplename: 25 Root: 28.069 --> Root in between the borders! Added to results. Hyperbolic solved: 37.6661823177513 - [20250519_003619.]: Samplename: 37.5 + [20250519_004346.]: Samplename: 37.5 Root: 37.666 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3684233460174 - [20250519_003619.]: Samplename: 50 + [20250519_004346.]: Samplename: 50 Root: 52.368 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8636916649907 - [20250519_003619.]: Samplename: 62.5 + [20250519_004346.]: Samplename: 62.5 Root: 64.864 --> Root in between the borders! Added to results. Hyperbolic solved: 74.2551330907062 - [20250519_003619.]: Samplename: 75 + [20250519_004346.]: Samplename: 75 Root: 74.255 --> Root in between the borders! Added to results. Hyperbolic solved: 83.9343288872042 - [20250519_003619.]: Samplename: 87.5 + [20250519_004346.]: Samplename: 87.5 Root: 83.934 --> Root in between the borders! Added to results. Hyperbolic solved: 100 - [20250519_003619.]: Samplename: 100 + [20250519_004346.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. - [20250519_003619.]: Solving hyperbolic regression for row_means + [20250519_004346.]: Solving hyperbolic regression for row_means Hyperbolic solved: 0.000228160853766634 - [20250519_003619.]: Samplename: 0 + [20250519_004346.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 11.1587839347378 - [20250519_003619.]: Samplename: 12.5 + [20250519_004346.]: Samplename: 12.5 Root: 11.159 --> Root in between the borders! Added to results. Hyperbolic solved: 25.8570776700364 - [20250519_003619.]: Samplename: 25 + [20250519_004346.]: Samplename: 25 Root: 25.857 --> Root in between the borders! Added to results. Hyperbolic solved: 37.065004454281 - [20250519_003619.]: Samplename: 37.5 + [20250519_004346.]: Samplename: 37.5 Root: 37.065 --> Root in between the borders! Added to results. Hyperbolic solved: 51.1881763075298 - [20250519_003619.]: Samplename: 50 + [20250519_004346.]: Samplename: 50 Root: 51.188 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4439754555105 - [20250519_003619.]: Samplename: 62.5 + [20250519_004346.]: Samplename: 62.5 Root: 65.444 --> Root in between the borders! Added to results. Hyperbolic solved: 75.3005883283082 - [20250519_003619.]: Samplename: 75 + [20250519_004346.]: Samplename: 75 Root: 75.301 --> Root in between the borders! Added to results. Hyperbolic solved: 82.6591025877361 - [20250519_003619.]: Samplename: 87.5 + [20250519_004346.]: Samplename: 87.5 Root: 82.659 --> Root in between the borders! Added to results. Hyperbolic solved: 100.000153506599 - [20250519_003619.]: Samplename: 100 + [20250519_004346.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 - [20250519_003619.]: + [20250519_004346.]: ### Starting with regression calculations ### - [20250519_003619.]: Entered 'regression_type1'-Function + [20250519_004346.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : @@ -9931,476 +9928,476 @@ singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient - [20250519_003619.]: # CpG-site: CpG#1 + [20250519_004347.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 14.1480167109277, 26.1398443790446, 39.3762039523382, 52.9476244028783, 65.4195105873419, 74.433837698331, 80.5559264925808, 100)c(0, 1.6480167109277, 1.1398443790446, 1.8762039523382, 2.9476244028783, 2.9195105873419, 0.566162301668996, 6.9440735074192, 0)c(NA, 13.1841336874216, 4.5593775161784, 5.00321053956854, 5.89524880575659, 4.67121693974705, 0.754883068891995, 7.93608400847909, 0) - [20250519_003620.]: Logging df_agg: CpG#1 - [20250519_003620.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003620.]: c(0, 14.1480167109277, 26.1398443790446, 39.3762039523382, 52.9476244028783, 65.4195105873419, 74.433837698331, 80.5559264925808, 100)[20250519_003620.]: c(0, 1.6480167109277, 1.1398443790446, 1.8762039523382, 2.9476244028783, 2.9195105873419, 0.566162301668996, 6.9440735074192, 0)[20250519_003620.]: c(NA, 13.1841336874216, 4.5593775161784, 5.00321053956854, 5.89524880575659, 4.67121693974705, 0.754883068891995, 7.93608400847909, 0) - [20250519_003620.]: Entered 'hyperbolic_regression'-Function - [20250519_003620.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003620.]: Entered 'cubic_regression'-Function - [20250519_003620.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003620.]: # CpG-site: CpG#2 + [20250519_004347.]: Logging df_agg: CpG#1 + [20250519_004347.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004347.]: c(0, 14.1480167109277, 26.1398443790446, 39.3762039523382, 52.9476244028783, 65.4195105873419, 74.433837698331, 80.5559264925808, 100)[20250519_004347.]: c(0, 1.6480167109277, 1.1398443790446, 1.8762039523382, 2.9476244028783, 2.9195105873419, 0.566162301668996, 6.9440735074192, 0)[20250519_004347.]: c(NA, 13.1841336874216, 4.5593775161784, 5.00321053956854, 5.89524880575659, 4.67121693974705, 0.754883068891995, 7.93608400847909, 0) + [20250519_004347.]: Entered 'hyperbolic_regression'-Function + [20250519_004347.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004347.]: Entered 'cubic_regression'-Function + [20250519_004347.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004347.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.7812303822548, 26.0648314401535, 35.1980950192158, 47.9203836090837, 67.275750545092, 75.7278143684244, 84.1258428690749, 100)c(0, 1.7187696177452, 1.0648314401535, 2.3019049807842, 2.0796163909163, 4.775750545092, 0.727814368424404, 3.3741571309251, 0)c(NA, 13.7501569419616, 4.259325760614, 6.1384132820912, 4.1592327818326, 7.64120087214719, 0.970419157899206, 3.85617957820011, 0) - [20250519_003620.]: Logging df_agg: CpG#2 - [20250519_003620.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003620.]: c(0, 10.7812303822548, 26.0648314401535, 35.1980950192158, 47.9203836090837, 67.275750545092, 75.7278143684244, 84.1258428690749, 100)[20250519_003620.]: c(0, 1.7187696177452, 1.0648314401535, 2.3019049807842, 2.0796163909163, 4.775750545092, 0.727814368424404, 3.3741571309251, 0)[20250519_003620.]: c(NA, 13.7501569419616, 4.259325760614, 6.1384132820912, 4.1592327818326, 7.64120087214719, 0.970419157899206, 3.85617957820011, 0) - [20250519_003620.]: Entered 'hyperbolic_regression'-Function - [20250519_003620.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003620.]: Entered 'cubic_regression'-Function - [20250519_003620.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003620.]: # CpG-site: CpG#3 + [20250519_004347.]: Logging df_agg: CpG#2 + [20250519_004347.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004347.]: c(0, 10.7812303822548, 26.0648314401535, 35.1980950192158, 47.9203836090837, 67.275750545092, 75.7278143684244, 84.1258428690749, 100)[20250519_004347.]: c(0, 1.7187696177452, 1.0648314401535, 2.3019049807842, 2.0796163909163, 4.775750545092, 0.727814368424404, 3.3741571309251, 0)[20250519_004347.]: c(NA, 13.7501569419616, 4.259325760614, 6.1384132820912, 4.1592327818326, 7.64120087214719, 0.970419157899206, 3.85617957820011, 0) + [20250519_004347.]: Entered 'hyperbolic_regression'-Function + [20250519_004347.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004347.]: Entered 'cubic_regression'-Function + [20250519_004347.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004347.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.8467198180169, 26.1449700612555, 37.2865760013773, 51.4114078772697, 65.0139634978039, 76.9921394830877, 79.6808818589171, 100)c(0, 1.6532801819831, 1.1449700612555, 0.213423998622702, 1.4114078772697, 2.5139634978039, 1.9921394830877, 7.8191181410829, 0)c(NA, 13.2262414558648, 4.579880245022, 0.569130662993871, 2.82281575453941, 4.02234159648624, 2.65618597745026, 8.93613501838045, 0) - [20250519_003620.]: Logging df_agg: CpG#3 - [20250519_003620.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003620.]: c(0, 10.8467198180169, 26.1449700612555, 37.2865760013773, 51.4114078772697, 65.0139634978039, 76.9921394830877, 79.6808818589171, 100)[20250519_003620.]: c(0, 1.6532801819831, 1.1449700612555, 0.213423998622702, 1.4114078772697, 2.5139634978039, 1.9921394830877, 7.8191181410829, 0)[20250519_003620.]: c(NA, 13.2262414558648, 4.579880245022, 0.569130662993871, 2.82281575453941, 4.02234159648624, 2.65618597745026, 8.93613501838045, 0) - [20250519_003620.]: Entered 'hyperbolic_regression'-Function - [20250519_003620.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003621.]: Entered 'cubic_regression'-Function - [20250519_003621.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003621.]: # CpG-site: CpG#4 + [20250519_004347.]: Logging df_agg: CpG#3 + [20250519_004347.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004347.]: c(0, 10.8467198180169, 26.1449700612555, 37.2865760013773, 51.4114078772697, 65.0139634978039, 76.9921394830877, 79.6808818589171, 100)[20250519_004347.]: c(0, 1.6532801819831, 1.1449700612555, 0.213423998622702, 1.4114078772697, 2.5139634978039, 1.9921394830877, 7.8191181410829, 0)[20250519_004347.]: c(NA, 13.2262414558648, 4.579880245022, 0.569130662993871, 2.82281575453941, 4.02234159648624, 2.65618597745026, 8.93613501838045, 0) + [20250519_004347.]: Entered 'hyperbolic_regression'-Function + [20250519_004348.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004348.]: Entered 'cubic_regression'-Function + [20250519_004348.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004348.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 13.2494913489925, 25.0914701740206, 38.8081740444332, 49.1133034150896, 67.573566028728, 73.7655861480302, 82.0404940898918, 100)c(0, 0.7494913489925, 0.0914701740205999, 1.3081740444332, 0.886696584910403, 5.073566028728, 1.2344138519698, 5.45950591010821, 0)c(NA, 5.99593079194, 0.365880696082399, 3.48846411848854, 1.77339316982081, 8.11770564596479, 1.64588513595974, 6.23943532583795, 0) - [20250519_003621.]: Logging df_agg: CpG#4 - [20250519_003621.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003621.]: c(0, 13.2494913489925, 25.0914701740206, 38.8081740444332, 49.1133034150896, 67.573566028728, 73.7655861480302, 82.0404940898918, 100)[20250519_003621.]: c(0, 0.7494913489925, 0.0914701740205999, 1.3081740444332, 0.886696584910403, 5.073566028728, 1.2344138519698, 5.45950591010821, 0)[20250519_003621.]: c(NA, 5.99593079194, 0.365880696082399, 3.48846411848854, 1.77339316982081, 8.11770564596479, 1.64588513595974, 6.23943532583795, 0) - [20250519_003621.]: Entered 'hyperbolic_regression'-Function - [20250519_003621.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003621.]: Entered 'cubic_regression'-Function - [20250519_003621.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003621.]: # CpG-site: CpG#5 + [20250519_004348.]: Logging df_agg: CpG#4 + [20250519_004348.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004348.]: c(0, 13.2494913489925, 25.0914701740206, 38.8081740444332, 49.1133034150896, 67.573566028728, 73.7655861480302, 82.0404940898918, 100)[20250519_004348.]: c(0, 0.7494913489925, 0.0914701740205999, 1.3081740444332, 0.886696584910403, 5.073566028728, 1.2344138519698, 5.45950591010821, 0)[20250519_004348.]: c(NA, 5.99593079194, 0.365880696082399, 3.48846411848854, 1.77339316982081, 8.11770564596479, 1.64588513595974, 6.23943532583795, 0) + [20250519_004348.]: Entered 'hyperbolic_regression'-Function + [20250519_004348.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004348.]: Entered 'cubic_regression'-Function + [20250519_004348.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004348.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.36101271071658, 23.0724671456609, 36.9926902689013, 50.9902012556783, 62.7269420395599, 76.6726263557269, 86.2959101486824, 100)c(0, 4.13898728928342, 1.9275328543391, 0.507309731098701, 0.990201255678301, 0.226942039559901, 1.6726263557269, 1.2040898513176, 0)c(NA, 33.1118983142674, 7.71013141735639, 1.35282594959654, 1.9804025113566, 0.363107263295842, 2.23016847430254, 1.37610268722012, 0) - [20250519_003621.]: Logging df_agg: CpG#5 - [20250519_003621.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003621.]: c(0, 8.36101271071658, 23.0724671456609, 36.9926902689013, 50.9902012556783, 62.7269420395599, 76.6726263557269, 86.2959101486824, 100)[20250519_003621.]: c(0, 4.13898728928342, 1.9275328543391, 0.507309731098701, 0.990201255678301, 0.226942039559901, 1.6726263557269, 1.2040898513176, 0)[20250519_003621.]: c(NA, 33.1118983142674, 7.71013141735639, 1.35282594959654, 1.9804025113566, 0.363107263295842, 2.23016847430254, 1.37610268722012, 0) - [20250519_003621.]: Entered 'hyperbolic_regression'-Function - [20250519_003621.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003621.]: Entered 'cubic_regression'-Function - [20250519_003621.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003622.]: # CpG-site: CpG#6 + [20250519_004348.]: Logging df_agg: CpG#5 + [20250519_004348.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004348.]: c(0, 8.36101271071658, 23.0724671456609, 36.9926902689013, 50.9902012556783, 62.7269420395599, 76.6726263557269, 86.2959101486824, 100)[20250519_004348.]: c(0, 4.13898728928342, 1.9275328543391, 0.507309731098701, 0.990201255678301, 0.226942039559901, 1.6726263557269, 1.2040898513176, 0)[20250519_004348.]: c(NA, 33.1118983142674, 7.71013141735639, 1.35282594959654, 1.9804025113566, 0.363107263295842, 2.23016847430254, 1.37610268722012, 0) + [20250519_004348.]: Entered 'hyperbolic_regression'-Function + [20250519_004348.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004349.]: Entered 'cubic_regression'-Function + [20250519_004349.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004349.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.8139244542506, 26.5330423964606, 35.365455365759, 50.1054345020203, 64.9683746571925, 73.6331726521876, 86.9938602772804, 100)c(0, 0.6860755457494, 1.5330423964606, 2.134544634241, 0.105434502020302, 2.4683746571925, 1.3668273478124, 0.506139722719595, 0)c(NA, 5.4886043659952, 6.13216958584241, 5.69211902464267, 0.210869004040603, 3.949399451508, 1.82243646374987, 0.578445397393823, 0) - [20250519_003622.]: Logging df_agg: CpG#6 - [20250519_003622.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003622.]: c(0, 11.8139244542506, 26.5330423964606, 35.365455365759, 50.1054345020203, 64.9683746571925, 73.6331726521876, 86.9938602772804, 100)[20250519_003622.]: c(0, 0.6860755457494, 1.5330423964606, 2.134544634241, 0.105434502020302, 2.4683746571925, 1.3668273478124, 0.506139722719595, 0)[20250519_003622.]: c(NA, 5.4886043659952, 6.13216958584241, 5.69211902464267, 0.210869004040603, 3.949399451508, 1.82243646374987, 0.578445397393823, 0) - [20250519_003622.]: Entered 'hyperbolic_regression'-Function - [20250519_003622.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003622.]: Entered 'cubic_regression'-Function - [20250519_003622.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003622.]: # CpG-site: CpG#7 + [20250519_004349.]: Logging df_agg: CpG#6 + [20250519_004349.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004349.]: c(0, 11.8139244542506, 26.5330423964606, 35.365455365759, 50.1054345020203, 64.9683746571925, 73.6331726521876, 86.9938602772804, 100)[20250519_004349.]: c(0, 0.6860755457494, 1.5330423964606, 2.134544634241, 0.105434502020302, 2.4683746571925, 1.3668273478124, 0.506139722719595, 0)[20250519_004349.]: c(NA, 5.4886043659952, 6.13216958584241, 5.69211902464267, 0.210869004040603, 3.949399451508, 1.82243646374987, 0.578445397393823, 0) + [20250519_004349.]: Entered 'hyperbolic_regression'-Function + [20250519_004349.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004349.]: Entered 'cubic_regression'-Function + [20250519_004349.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004349.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.7880491100164, 26.1959234034328, 39.1978567589215, 54.3513505291649, 66.0567953704493, 75.1900867269869, 78.6051660349008, 100)c(0, 0.7119508899836, 1.1959234034328, 1.6978567589215, 4.3513505291649, 3.55679537044929, 0.190086726986905, 8.8948339650992, 0)c(NA, 5.6956071198688, 4.7836936137312, 4.52761802379068, 8.70270105832979, 5.69087259271887, 0.253448969315874, 10.1655245315419, 0) - [20250519_003622.]: Logging df_agg: CpG#7 - [20250519_003622.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003622.]: c(0, 11.7880491100164, 26.1959234034328, 39.1978567589215, 54.3513505291649, 66.0567953704493, 75.1900867269869, 78.6051660349008, 100)[20250519_003622.]: c(0, 0.7119508899836, 1.1959234034328, 1.6978567589215, 4.3513505291649, 3.55679537044929, 0.190086726986905, 8.8948339650992, 0)[20250519_003622.]: c(NA, 5.6956071198688, 4.7836936137312, 4.52761802379068, 8.70270105832979, 5.69087259271887, 0.253448969315874, 10.1655245315419, 0) - [20250519_003622.]: Entered 'hyperbolic_regression'-Function - [20250519_003622.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003622.]: Entered 'cubic_regression'-Function - [20250519_003622.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003622.]: # CpG-site: CpG#8 + [20250519_004349.]: Logging df_agg: CpG#7 + [20250519_004349.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004349.]: c(0, 11.7880491100164, 26.1959234034328, 39.1978567589215, 54.3513505291649, 66.0567953704493, 75.1900867269869, 78.6051660349008, 100)[20250519_004349.]: c(0, 0.7119508899836, 1.1959234034328, 1.6978567589215, 4.3513505291649, 3.55679537044929, 0.190086726986905, 8.8948339650992, 0)[20250519_004349.]: c(NA, 5.6956071198688, 4.7836936137312, 4.52761802379068, 8.70270105832979, 5.69087259271887, 0.253448969315874, 10.1655245315419, 0) + [20250519_004349.]: Entered 'hyperbolic_regression'-Function + [20250519_004349.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004349.]: Entered 'cubic_regression'-Function + [20250519_004349.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004349.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 7.27382484148278, 24.9765598282779, 34.028313807905, 52.2946423509726, 65.0742432627639, 78.3047550247862, 81.0501991298613, 100)c(0, 5.22617515851722, 0.0234401717221004, 3.471686192095, 2.2946423509726, 2.5742432627639, 3.3047550247862, 6.4498008701387, 0)c(NA, 41.8094012681378, 0.0937606868884018, 9.25782984558666, 4.5892847019452, 4.11878922042224, 4.40634003304827, 7.37120099444423, 0) - [20250519_003622.]: Logging df_agg: CpG#8 - [20250519_003622.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003622.]: c(0, 7.27382484148278, 24.9765598282779, 34.028313807905, 52.2946423509726, 65.0742432627639, 78.3047550247862, 81.0501991298613, 100)[20250519_003622.]: c(0, 5.22617515851722, 0.0234401717221004, 3.471686192095, 2.2946423509726, 2.5742432627639, 3.3047550247862, 6.4498008701387, 0)[20250519_003622.]: c(NA, 41.8094012681378, 0.0937606868884018, 9.25782984558666, 4.5892847019452, 4.11878922042224, 4.40634003304827, 7.37120099444423, 0) - [20250519_003622.]: Entered 'hyperbolic_regression'-Function - [20250519_003622.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003623.]: Entered 'cubic_regression'-Function - [20250519_003623.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003623.]: # CpG-site: CpG#9 + [20250519_004349.]: Logging df_agg: CpG#8 + [20250519_004349.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004349.]: c(0, 7.27382484148278, 24.9765598282779, 34.028313807905, 52.2946423509726, 65.0742432627639, 78.3047550247862, 81.0501991298613, 100)[20250519_004349.]: c(0, 5.22617515851722, 0.0234401717221004, 3.471686192095, 2.2946423509726, 2.5742432627639, 3.3047550247862, 6.4498008701387, 0)[20250519_004349.]: c(NA, 41.8094012681378, 0.0937606868884018, 9.25782984558666, 4.5892847019452, 4.11878922042224, 4.40634003304827, 7.37120099444423, 0) + [20250519_004349.]: Entered 'hyperbolic_regression'-Function + [20250519_004349.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004350.]: Entered 'cubic_regression'-Function + [20250519_004350.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004350.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.2068234201026, 28.0688737636578, 37.6661823177513, 52.3684233460174, 64.8636916649907, 74.2551330907062, 83.9343288872042, 100)c(0, 0.2931765798974, 3.0688737636578, 0.1661823177513, 2.3684233460174, 2.3636916649907, 0.744866909293805, 3.5656711127958, 0)c(NA, 2.3454126391792, 12.2754950546312, 0.4431528473368, 4.7368466920348, 3.78190666398511, 0.993155879058406, 4.07505270033805, 0) - [20250519_003623.]: Logging df_agg: CpG#9 - [20250519_003623.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003623.]: c(0, 12.2068234201026, 28.0688737636578, 37.6661823177513, 52.3684233460174, 64.8636916649907, 74.2551330907062, 83.9343288872042, 100)[20250519_003623.]: c(0, 0.2931765798974, 3.0688737636578, 0.1661823177513, 2.3684233460174, 2.3636916649907, 0.744866909293805, 3.5656711127958, 0)[20250519_003623.]: c(NA, 2.3454126391792, 12.2754950546312, 0.4431528473368, 4.7368466920348, 3.78190666398511, 0.993155879058406, 4.07505270033805, 0) - [20250519_003623.]: Entered 'hyperbolic_regression'-Function - [20250519_003623.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003623.]: Entered 'cubic_regression'-Function - [20250519_003623.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003623.]: # CpG-site: row_means + [20250519_004350.]: Logging df_agg: CpG#9 + [20250519_004350.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004350.]: c(0, 12.2068234201026, 28.0688737636578, 37.6661823177513, 52.3684233460174, 64.8636916649907, 74.2551330907062, 83.9343288872042, 100)[20250519_004350.]: c(0, 0.2931765798974, 3.0688737636578, 0.1661823177513, 2.3684233460174, 2.3636916649907, 0.744866909293805, 3.5656711127958, 0)[20250519_004350.]: c(NA, 2.3454126391792, 12.2754950546312, 0.4431528473368, 4.7368466920348, 3.78190666398511, 0.993155879058406, 4.07505270033805, 0) + [20250519_004350.]: Entered 'hyperbolic_regression'-Function + [20250519_004350.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004350.]: Entered 'cubic_regression'-Function + [20250519_004350.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004350.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.000228160853766634, 11.1587839347378, 25.8570776700364, 37.065004454281, 51.1881763075298, 65.4439754555105, 75.3005883283082, 82.6591025877361, 100)c(0.000228160853766634, 1.3412160652622, 0.8570776700364, 0.434995545718998, 1.1881763075298, 2.9439754555105, 0.300588328308194, 4.8408974122639, 0)c(NA, 10.7297285220976, 3.4283106801456, 1.15998812191733, 2.3763526150596, 4.7103607288168, 0.400784437744259, 5.53245418544446, 0) - [20250519_003623.]: Logging df_agg: row_means - [20250519_003623.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003623.]: c(0.000228160853766634, 11.1587839347378, 25.8570776700364, 37.065004454281, 51.1881763075298, 65.4439754555105, 75.3005883283082, 82.6591025877361, 100)[20250519_003623.]: c(0.000228160853766634, 1.3412160652622, 0.8570776700364, 0.434995545718998, 1.1881763075298, 2.9439754555105, 0.300588328308194, 4.8408974122639, 0)[20250519_003623.]: c(NA, 10.7297285220976, 3.4283106801456, 1.15998812191733, 2.3763526150596, 4.7103607288168, 0.400784437744259, 5.53245418544446, 0) - [20250519_003623.]: Entered 'hyperbolic_regression'-Function - [20250519_003623.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003623.]: Entered 'cubic_regression'-Function - [20250519_003623.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003624.]: Entered 'solving_equations'-Function - [20250519_003624.]: Solving cubic regression for CpG#1 + [20250519_004350.]: Logging df_agg: row_means + [20250519_004350.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004350.]: c(0.000228160853766634, 11.1587839347378, 25.8570776700364, 37.065004454281, 51.1881763075298, 65.4439754555105, 75.3005883283082, 82.6591025877361, 100)[20250519_004350.]: c(0.000228160853766634, 1.3412160652622, 0.8570776700364, 0.434995545718998, 1.1881763075298, 2.9439754555105, 0.300588328308194, 4.8408974122639, 0)[20250519_004350.]: c(NA, 10.7297285220976, 3.4283106801456, 1.15998812191733, 2.3763526150596, 4.7103607288168, 0.400784437744259, 5.53245418544446, 0) + [20250519_004350.]: Entered 'hyperbolic_regression'-Function + [20250519_004350.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004350.]: Entered 'cubic_regression'-Function + [20250519_004350.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004351.]: Entered 'solving_equations'-Function + [20250519_004351.]: Solving cubic regression for CpG#1 Coefficients: 0Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 - [20250519_003624.]: Samplename: 0 + [20250519_004351.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -7.30533333333333Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 - [20250519_003624.]: Samplename: 12.5 + [20250519_004351.]: Samplename: 12.5 Root: 4.661 --> Root in between the borders! Added to results. Coefficients: -14.352Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 - [20250519_003624.]: Samplename: 25 + [20250519_004351.]: Samplename: 25 Root: 9.352 --> Root in between the borders! Added to results. Coefficients: -23.244Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 - [20250519_003624.]: Samplename: 37.5 + [20250519_004351.]: Samplename: 37.5 Root: 15.59 --> Root in between the borders! Added to results. Coefficients: -33.8645Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 - [20250519_003624.]: Samplename: 50 + [20250519_004351.]: Samplename: 50 Root: 23.601 --> Root in between the borders! Added to results. Coefficients: -45.318Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 - [20250519_003624.]: Samplename: 62.5 + [20250519_004351.]: Samplename: 62.5 Root: 33.121 --> Root in between the borders! Added to results. Coefficients: -54.857Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 - [20250519_003624.]: Samplename: 75 + [20250519_004351.]: Samplename: 75 Root: 42.001 --> Root in between the borders! Added to results. Coefficients: -62.062Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 - [20250519_003624.]: Samplename: 87.5 + [20250519_004351.]: Samplename: 87.5 Root: 49.51 --> Root in between the borders! Added to results. Coefficients: -90.01Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 - [20250519_003624.]: Samplename: 100 + [20250519_004351.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 - [20250519_003624.]: Solving cubic regression for CpG#2 + [20250519_004351.]: Solving cubic regression for CpG#2 Coefficients: 0Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 - [20250519_003624.]: Samplename: 0 + [20250519_004351.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -6.05666666666666Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 - [20250519_003624.]: Samplename: 12.5 + [20250519_004351.]: Samplename: 12.5 Root: 8.415 --> Root in between the borders! Added to results. Coefficients: -15.656Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 - [20250519_003624.]: Samplename: 25 + [20250519_004351.]: Samplename: 25 Root: 21.016 --> Root in between the borders! Added to results. Coefficients: -22.054Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 - [20250519_003624.]: Samplename: 37.5 + [20250519_004351.]: Samplename: 37.5 Root: 28.984 --> Root in between the borders! Added to results. Coefficients: -31.945Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 - [20250519_003624.]: Samplename: 50 + [20250519_004351.]: Samplename: 50 Root: 40.726 --> Root in between the borders! Added to results. Coefficients: -49.68Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 - [20250519_003624.]: Samplename: 62.5 + [20250519_004351.]: Samplename: 62.5 Root: 60.322 --> Root in between the borders! Added to results. Coefficients: -58.6825Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 - [20250519_003624.]: Samplename: 75 + [20250519_004351.]: Samplename: 75 Root: 69.671 --> Root in between the borders! Added to results. Coefficients: -68.5533333333333Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 - [20250519_003624.]: Samplename: 87.5 + [20250519_004351.]: Samplename: 87.5 Root: 79.529 --> Root in between the borders! Added to results. Coefficients: -90.294Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 - [20250519_003624.]: Samplename: 100 + [20250519_004351.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 - [20250519_003624.]: Solving cubic regression for CpG#3 + [20250519_004351.]: Solving cubic regression for CpG#3 Coefficients: 0Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 - [20250519_003624.]: Samplename: 0 + [20250519_004351.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -5.67Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 - [20250519_003624.]: Samplename: 12.5 + [20250519_004351.]: Samplename: 12.5 Root: 5.75 --> Root in between the borders! Added to results. Coefficients: -14.526Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 - [20250519_003624.]: Samplename: 25 + [20250519_004351.]: Samplename: 25 Root: 15.012 --> Root in between the borders! Added to results. Coefficients: -21.71Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 - [20250519_003624.]: Samplename: 37.5 + [20250519_004351.]: Samplename: 37.5 Root: 22.804 --> Root in between the borders! Added to results. Coefficients: -31.8725Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 - [20250519_003624.]: Samplename: 50 + [20250519_004351.]: Samplename: 50 Root: 34.308 --> Root in between the borders! Added to results. Coefficients: -42.986Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 - [20250519_003624.]: Samplename: 62.5 + [20250519_004351.]: Samplename: 62.5 Root: 47.638 --> Root in between the borders! Added to results. Coefficients: -54.0725Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 - [20250519_003624.]: Samplename: 75 + [20250519_004351.]: Samplename: 75 Root: 61.877 --> Root in between the borders! Added to results. Coefficients: -56.7533333333333Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 - [20250519_003624.]: Samplename: 87.5 + [20250519_004351.]: Samplename: 87.5 Root: 65.486 --> Root in between the borders! Added to results. Coefficients: -79.762Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 - [20250519_003624.]: Samplename: 100 + [20250519_004351.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 - [20250519_003624.]: Solving cubic regression for CpG#4 + [20250519_004351.]: Solving cubic regression for CpG#4 Coefficients: 0Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 - [20250519_003624.]: Samplename: 0 + [20250519_004351.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -7.65533333333333Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 - [20250519_003624.]: Samplename: 12.5 + [20250519_004351.]: Samplename: 12.5 Root: 6.614 --> Root in between the borders! Added to results. Coefficients: -15.206Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 - [20250519_003624.]: Samplename: 25 + [20250519_004351.]: Samplename: 25 Root: 13.372 --> Root in between the borders! Added to results. Coefficients: -24.93Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 - [20250519_003624.]: Samplename: 37.5 + [20250519_004351.]: Samplename: 37.5 Root: 22.462 --> Root in between the borders! Added to results. Coefficients: -33.0395Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 - [20250519_003624.]: Samplename: 50 + [20250519_004351.]: Samplename: 50 Root: 30.424 --> Root in between the borders! Added to results. Coefficients: -49.658Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 - [20250519_003624.]: Samplename: 62.5 + [20250519_004351.]: Samplename: 62.5 Root: 48.07 --> Root in between the borders! Added to results. Coefficients: -55.942Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 - [20250519_003624.]: Samplename: 75 + [20250519_004351.]: Samplename: 75 Root: 55.318 --> Root in between the borders! Added to results. Coefficients: -64.9953333333333Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 - [20250519_003624.]: Samplename: 87.5 + [20250519_004351.]: Samplename: 87.5 Root: 66.469 --> Root in between the borders! Added to results. Coefficients: -87.724Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 - [20250519_003624.]: Samplename: 100 + [20250519_004351.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. - [20250519_003624.]: Solving cubic regression for CpG#5 + [20250519_004351.]: Solving cubic regression for CpG#5 Coefficients: 0Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_003624.]: Samplename: 0 + [20250519_004351.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -4.144Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_003624.]: Samplename: 12.5 + [20250519_004351.]: Samplename: 12.5 Root: 10.457 --> Root in between the borders! Added to results. Coefficients: -12.102Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_003624.]: Samplename: 25 + [20250519_004351.]: Samplename: 25 Root: 26.323 --> Root in between the borders! Added to results. Coefficients: -20.536Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_003624.]: Samplename: 37.5 + [20250519_004351.]: Samplename: 37.5 Root: 39.937 --> Root in between the borders! Added to results. Coefficients: -30.0715Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_003624.]: Samplename: 50 + [20250519_004351.]: Samplename: 50 Root: 53.063 --> Root in between the borders! Added to results. Coefficients: -39.034Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_003624.]: Samplename: 62.5 + [20250519_004351.]: Samplename: 62.5 Root: 63.959 --> Root in between the borders! Added to results. Coefficients: -51.059Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_003624.]: Samplename: 75 + [20250519_004351.]: Samplename: 75 Root: 77.051 --> Root in between the borders! Added to results. Coefficients: -60.3906666666667Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_003624.]: Samplename: 87.5 + [20250519_004351.]: Samplename: 87.5 Root: 86.31 --> Root in between the borders! Added to results. Coefficients: -75.446Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_003624.]: Samplename: 100 + [20250519_004351.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. - [20250519_003624.]: Solving cubic regression for CpG#6 + [20250519_004351.]: Solving cubic regression for CpG#6 Coefficients: 0Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 - [20250519_003624.]: Samplename: 0 + [20250519_004351.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -6.54266666666667Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 - [20250519_003624.]: Samplename: 12.5 + [20250519_004351.]: Samplename: 12.5 Root: 8.15 --> Root in between the borders! Added to results. Coefficients: -15.692Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 - [20250519_003624.]: Samplename: 25 + [20250519_004351.]: Samplename: 25 Root: 19.28 --> Root in between the borders! Added to results. Coefficients: -21.804Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 - [20250519_003624.]: Samplename: 37.5 + [20250519_004351.]: Samplename: 37.5 Root: 26.553 --> Root in between the borders! Added to results. Coefficients: -33.2485Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 - [20250519_003624.]: Samplename: 50 + [20250519_004351.]: Samplename: 50 Root: 39.845 --> Root in between the borders! Added to results. Coefficients: -46.704Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 - [20250519_003624.]: Samplename: 62.5 + [20250519_004351.]: Samplename: 62.5 Root: 54.973 --> Root in between the borders! Added to results. Coefficients: -55.636Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 - [20250519_003624.]: Samplename: 75 + [20250519_004351.]: Samplename: 75 Root: 64.743 --> Root in between the borders! Added to results. Coefficients: -71.3493333333333Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 - [20250519_003624.]: Samplename: 87.5 + [20250519_004351.]: Samplename: 87.5 Root: 81.445 --> Root in between the borders! Added to results. Coefficients: -89.46Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 - [20250519_003624.]: Samplename: 100 + [20250519_004351.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. - [20250519_003624.]: Solving cubic regression for CpG#7 + [20250519_004351.]: Solving cubic regression for CpG#7 Coefficients: 0Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 - [20250519_003624.]: Samplename: 0 + [20250519_004351.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -4.18066666666667Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 - [20250519_003624.]: Samplename: 12.5 + [20250519_004351.]: Samplename: 12.5 Root: 3.662 --> Root in between the borders! Added to results. Coefficients: -10.05Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 - [20250519_003624.]: Samplename: 25 + [20250519_004351.]: Samplename: 25 Root: 9.013 --> Root in between the borders! Added to results. Coefficients: -16.236Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 - [20250519_003624.]: Samplename: 37.5 + [20250519_004351.]: Samplename: 37.5 Root: 14.96 --> Root in between the borders! Added to results. Coefficients: -24.8165Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 - [20250519_003624.]: Samplename: 50 + [20250519_004351.]: Samplename: 50 Root: 23.842 --> Root in between the borders! Added to results. Coefficients: -32.75Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 - [20250519_003624.]: Samplename: 62.5 + [20250519_004351.]: Samplename: 62.5 Root: 32.894 --> Root in between the borders! Added to results. Coefficients: -39.954Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 - [20250519_003624.]: Samplename: 75 + [20250519_004351.]: Samplename: 75 Root: 42.068 --> Root in between the borders! Added to results. Coefficients: -42.9206666666667Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 - [20250519_003624.]: Samplename: 87.5 + [20250519_004351.]: Samplename: 87.5 Root: 46.196 --> Root in between the borders! Added to results. Coefficients: -66.008Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 - [20250519_003624.]: Samplename: 100 + [20250519_004351.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. - [20250519_003624.]: Solving cubic regression for CpG#8 + [20250519_004351.]: Solving cubic regression for CpG#8 Coefficients: 0Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_003624.]: Samplename: 0 + [20250519_004351.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -4.35066666666667Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_003624.]: Samplename: 12.5 + [20250519_004351.]: Samplename: 12.5 Root: 8.961 --> Root in between the borders! Added to results. Coefficients: -15.834Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_003624.]: Samplename: 25 + [20250519_004351.]: Samplename: 25 Root: 28.16 --> Root in between the borders! Added to results. Coefficients: -22.254Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_003624.]: Samplename: 37.5 + [20250519_004351.]: Samplename: 37.5 Root: 37.189 --> Root in between the borders! Added to results. Coefficients: -36.529Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_003624.]: Samplename: 50 + [20250519_004351.]: Samplename: 50 Root: 54.661 --> Root in between the borders! Added to results. Coefficients: -47.73Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_003624.]: Samplename: 62.5 + [20250519_004351.]: Samplename: 62.5 Root: 66.642 --> Root in between the borders! Added to results. Coefficients: -60.5715Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_003624.]: Samplename: 75 + [20250519_004351.]: Samplename: 75 Root: 79.079 --> Root in between the borders! Added to results. Coefficients: -63.414Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_003624.]: Samplename: 87.5 + [20250519_004351.]: Samplename: 87.5 Root: 81.681 --> Root in between the borders! Added to results. Coefficients: -84.964Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_003624.]: Samplename: 100 + [20250519_004351.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. - [20250519_003624.]: Solving cubic regression for CpG#9 + [20250519_004351.]: Solving cubic regression for CpG#9 Coefficients: 0Coefficients: 1.52Coefficients: -0.006Coefficients: 0 - [20250519_003624.]: Samplename: 0 + [20250519_004351.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -5.406Coefficients: 1.52Coefficients: -0.006Coefficients: 0 - [20250519_003624.]: Samplename: 12.5 + [20250519_004351.]: Samplename: 12.5 Root: 3.608 --> Root in between the borders! Added to results. Coefficients: -13.716Coefficients: 1.52Coefficients: -0.006Coefficients: 0 - [20250519_003624.]: Samplename: 25 + [20250519_004351.]: Samplename: 25 Root: 9.37 --> Root in between the borders! Added to results. Coefficients: -19.634Coefficients: 1.52Coefficients: -0.006Coefficients: 0 - [20250519_003624.]: Samplename: 37.5 + [20250519_004351.]: Samplename: 37.5 Root: 13.653 --> Root in between the borders! Added to results. Coefficients: -30.406Coefficients: 1.52Coefficients: -0.006Coefficients: 0 - [20250519_003624.]: Samplename: 50 + [20250519_004351.]: Samplename: 50 Root: 21.897 --> Root in between the borders! Added to results. Coefficients: -41.696Coefficients: 1.52Coefficients: -0.006Coefficients: 0 - [20250519_003624.]: Samplename: 62.5 + [20250519_004351.]: Samplename: 62.5 Root: 31.298 --> Root in between the borders! Added to results. Coefficients: -51.9135Coefficients: 1.52Coefficients: -0.006Coefficients: 0 - [20250519_003624.]: Samplename: 75 + [20250519_004351.]: Samplename: 75 Root: 40.689 --> Root in between the borders! Added to results. Coefficients: -64.5026666666667Coefficients: 1.52Coefficients: -0.006Coefficients: 0 - [20250519_003624.]: Samplename: 87.5 + [20250519_004351.]: Samplename: 87.5 Root: 53.907 --> Root in between the borders! Added to results. Coefficients: -92Coefficients: 1.52Coefficients: -0.006Coefficients: 0 - [20250519_003624.]: Samplename: 100 + [20250519_004351.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. - [20250519_003624.]: Solving cubic regression for row_means + [20250519_004351.]: Solving cubic regression for row_means Coefficients: -0.000111111111110951Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 - [20250519_003624.]: Samplename: 0 + [20250519_004351.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -5.70137037037037Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 - [20250519_003624.]: Samplename: 12.5 + [20250519_004351.]: Samplename: 12.5 Root: 6.107 --> Root in between the borders! Added to results. Coefficients: -14.1261111111111Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 - [20250519_003624.]: Samplename: 25 + [20250519_004351.]: Samplename: 25 Root: 15.282 --> Root in between the borders! Added to results. Coefficients: -21.3781111111111Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 - [20250519_003624.]: Samplename: 37.5 + [20250519_004351.]: Samplename: 37.5 Root: 23.331 --> Root in between the borders! Added to results. Coefficients: -31.7548888888889Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 - [20250519_003624.]: Samplename: 50 + [20250519_004351.]: Samplename: 50 Root: 35.107 --> Root in between the borders! Added to results. Coefficients: -43.9507777777778Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 - [20250519_003624.]: Samplename: 62.5 + [20250519_004351.]: Samplename: 62.5 Root: 49.368 --> Root in between the borders! Added to results. Coefficients: -53.6321111111111Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 - [20250519_003624.]: Samplename: 75 + [20250519_004351.]: Samplename: 75 Root: 61.044 --> Root in between the borders! Added to results. Coefficients: -61.6602592592593Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 - [20250519_003624.]: Samplename: 87.5 + [20250519_004351.]: Samplename: 87.5 Root: 70.984 --> Root in between the borders! Added to results. Coefficients: -83.9632222222222Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 - [20250519_003624.]: Samplename: 100 + [20250519_004351.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 - [20250519_003624.]: + [20250519_004351.]: ### Starting with regression calculations ### - [20250519_003624.]: Entered 'regression_type1'-Function + [20250519_004351.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : @@ -10943,910 +10940,910 @@ singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 - [20250519_003625.]: # CpG-site: CpG#1 + [20250519_004352.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 4.66057108506072, 9.3520572622014, 15.5898391276712, 23.6006773278817, 33.1210812975699, 42.000790440049, 49.5097019721065, 100)c(0, 7.83942891493928, 15.6479427377986, 21.9101608723288, 26.3993226721183, 29.3789187024301, 32.999209559951, 37.9902980278935, 0)c(NA, 62.7154313195142, 62.5917709511944, 58.4270956595435, 52.7986453442366, 47.0062699238882, 43.9989460799347, 43.4174834604497, 0) - [20250519_003625.]: Logging df_agg: CpG#1 - [20250519_003625.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003625.]: c(0, 4.66057108506072, 9.3520572622014, 15.5898391276712, 23.6006773278817, 33.1210812975699, 42.000790440049, 49.5097019721065, 100)[20250519_003625.]: c(0, 7.83942891493928, 15.6479427377986, 21.9101608723288, 26.3993226721183, 29.3789187024301, 32.999209559951, 37.9902980278935, 0)[20250519_003625.]: c(NA, 62.7154313195142, 62.5917709511944, 58.4270956595435, 52.7986453442366, 47.0062699238882, 43.9989460799347, 43.4174834604497, 0) - [20250519_003625.]: Entered 'hyperbolic_regression'-Function - [20250519_003625.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003625.]: Entered 'cubic_regression'-Function - [20250519_003625.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003625.]: # CpG-site: CpG#2 + [20250519_004352.]: Logging df_agg: CpG#1 + [20250519_004352.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004352.]: c(0, 4.66057108506072, 9.3520572622014, 15.5898391276712, 23.6006773278817, 33.1210812975699, 42.000790440049, 49.5097019721065, 100)[20250519_004352.]: c(0, 7.83942891493928, 15.6479427377986, 21.9101608723288, 26.3993226721183, 29.3789187024301, 32.999209559951, 37.9902980278935, 0)[20250519_004352.]: c(NA, 62.7154313195142, 62.5917709511944, 58.4270956595435, 52.7986453442366, 47.0062699238882, 43.9989460799347, 43.4174834604497, 0) + [20250519_004352.]: Entered 'hyperbolic_regression'-Function + [20250519_004352.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004352.]: Entered 'cubic_regression'-Function + [20250519_004352.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004352.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.41473135558285, 21.0155787157944, 28.98380769729, 40.7258283847219, 60.321752127534, 69.670868358929, 79.5285048174076, 100)c(0, 4.08526864441715, 3.9844212842056, 8.51619230271, 9.2741716152781, 2.178247872466, 5.329131641071, 7.97149518259241, 0)c(NA, 32.6821491553372, 15.9376851368224, 22.70984614056, 18.5483432305562, 3.4851965959456, 7.10550885476133, 9.11028020867704, 0) - [20250519_003625.]: Logging df_agg: CpG#2 - [20250519_003625.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003625.]: c(0, 8.41473135558285, 21.0155787157944, 28.98380769729, 40.7258283847219, 60.321752127534, 69.670868358929, 79.5285048174076, 100)[20250519_003625.]: c(0, 4.08526864441715, 3.9844212842056, 8.51619230271, 9.2741716152781, 2.178247872466, 5.329131641071, 7.97149518259241, 0)[20250519_003625.]: c(NA, 32.6821491553372, 15.9376851368224, 22.70984614056, 18.5483432305562, 3.4851965959456, 7.10550885476133, 9.11028020867704, 0) - [20250519_003625.]: Entered 'hyperbolic_regression'-Function - [20250519_003625.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003626.]: Entered 'cubic_regression'-Function - [20250519_003626.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003626.]: # CpG-site: CpG#3 + [20250519_004352.]: Logging df_agg: CpG#2 + [20250519_004352.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004352.]: c(0, 8.41473135558285, 21.0155787157944, 28.98380769729, 40.7258283847219, 60.321752127534, 69.670868358929, 79.5285048174076, 100)[20250519_004352.]: c(0, 4.08526864441715, 3.9844212842056, 8.51619230271, 9.2741716152781, 2.178247872466, 5.329131641071, 7.97149518259241, 0)[20250519_004352.]: c(NA, 32.6821491553372, 15.9376851368224, 22.70984614056, 18.5483432305562, 3.4851965959456, 7.10550885476133, 9.11028020867704, 0) + [20250519_004352.]: Entered 'hyperbolic_regression'-Function + [20250519_004352.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004352.]: Entered 'cubic_regression'-Function + [20250519_004352.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004353.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 5.74980505230347, 15.0124787349221, 22.8043512255536, 34.3082682140428, 47.6381691654944, 61.8773912993076, 65.4860304554385, 100)c(0, 6.75019494769653, 9.9875212650779, 14.6956487744464, 15.6917317859572, 14.8618308345056, 13.1226087006924, 22.0139695445615, 0)c(NA, 54.0015595815722, 39.9500850603116, 39.1883967318571, 31.3834635719144, 23.778929335209, 17.4968116009232, 25.1588223366417, 0) - [20250519_003626.]: Logging df_agg: CpG#3 - [20250519_003626.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003626.]: c(0, 5.74980505230347, 15.0124787349221, 22.8043512255536, 34.3082682140428, 47.6381691654944, 61.8773912993076, 65.4860304554385, 100)[20250519_003626.]: c(0, 6.75019494769653, 9.9875212650779, 14.6956487744464, 15.6917317859572, 14.8618308345056, 13.1226087006924, 22.0139695445615, 0)[20250519_003626.]: c(NA, 54.0015595815722, 39.9500850603116, 39.1883967318571, 31.3834635719144, 23.778929335209, 17.4968116009232, 25.1588223366417, 0) - [20250519_003626.]: Entered 'hyperbolic_regression'-Function - [20250519_003626.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003626.]: Entered 'cubic_regression'-Function - [20250519_003626.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003626.]: # CpG-site: CpG#4 + [20250519_004353.]: Logging df_agg: CpG#3 + [20250519_004353.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004353.]: c(0, 5.74980505230347, 15.0124787349221, 22.8043512255536, 34.3082682140428, 47.6381691654944, 61.8773912993076, 65.4860304554385, 100)[20250519_004353.]: c(0, 6.75019494769653, 9.9875212650779, 14.6956487744464, 15.6917317859572, 14.8618308345056, 13.1226087006924, 22.0139695445615, 0)[20250519_004353.]: c(NA, 54.0015595815722, 39.9500850603116, 39.1883967318571, 31.3834635719144, 23.778929335209, 17.4968116009232, 25.1588223366417, 0) + [20250519_004353.]: Entered 'hyperbolic_regression'-Function + [20250519_004353.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004353.]: Entered 'cubic_regression'-Function + [20250519_004353.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004353.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 6.6142663267093, 13.3723444572473, 22.4624407354305, 30.4240096190196, 48.070278909378, 55.3176226954937, 66.4686809672836, 100)c(0, 5.8857336732907, 11.6276555427527, 15.0375592645695, 19.5759903809804, 14.429721090622, 19.6823773045063, 21.0313190327164, 0)c(NA, 47.0858693863256, 46.5106221710108, 40.100158038852, 39.1519807619608, 23.0875537449952, 26.2431697393417, 24.0357931802473, 0) - [20250519_003626.]: Logging df_agg: CpG#4 - [20250519_003626.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003626.]: c(0, 6.6142663267093, 13.3723444572473, 22.4624407354305, 30.4240096190196, 48.070278909378, 55.3176226954937, 66.4686809672836, 100)[20250519_003626.]: c(0, 5.8857336732907, 11.6276555427527, 15.0375592645695, 19.5759903809804, 14.429721090622, 19.6823773045063, 21.0313190327164, 0)[20250519_003626.]: c(NA, 47.0858693863256, 46.5106221710108, 40.100158038852, 39.1519807619608, 23.0875537449952, 26.2431697393417, 24.0357931802473, 0) - [20250519_003626.]: Entered 'hyperbolic_regression'-Function - [20250519_003626.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003626.]: Entered 'cubic_regression'-Function - [20250519_003626.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003627.]: # CpG-site: CpG#5 + [20250519_004353.]: Logging df_agg: CpG#4 + [20250519_004353.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004353.]: c(0, 6.6142663267093, 13.3723444572473, 22.4624407354305, 30.4240096190196, 48.070278909378, 55.3176226954937, 66.4686809672836, 100)[20250519_004353.]: c(0, 5.8857336732907, 11.6276555427527, 15.0375592645695, 19.5759903809804, 14.429721090622, 19.6823773045063, 21.0313190327164, 0)[20250519_004353.]: c(NA, 47.0858693863256, 46.5106221710108, 40.100158038852, 39.1519807619608, 23.0875537449952, 26.2431697393417, 24.0357931802473, 0) + [20250519_004353.]: Entered 'hyperbolic_regression'-Function + [20250519_004353.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004353.]: Entered 'cubic_regression'-Function + [20250519_004353.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004353.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.4570373964758, 26.3229081557653, 39.9371114374682, 53.0630784939681, 63.9590902125397, 77.051078420522, 86.3095786035144, 100)c(0, 2.0429626035242, 1.3229081557653, 2.4371114374682, 3.0630784939681, 1.4590902125397, 2.051078420522, 1.1904213964856, 0)c(NA, 16.3437008281936, 5.2916326230612, 6.49896383324852, 6.1261569879362, 2.33454434006352, 2.73477122736267, 1.36048159598355, 0) - [20250519_003627.]: Logging df_agg: CpG#5 - [20250519_003627.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003627.]: c(0, 10.4570373964758, 26.3229081557653, 39.9371114374682, 53.0630784939681, 63.9590902125397, 77.051078420522, 86.3095786035144, 100)[20250519_003627.]: c(0, 2.0429626035242, 1.3229081557653, 2.4371114374682, 3.0630784939681, 1.4590902125397, 2.051078420522, 1.1904213964856, 0)[20250519_003627.]: c(NA, 16.3437008281936, 5.2916326230612, 6.49896383324852, 6.1261569879362, 2.33454434006352, 2.73477122736267, 1.36048159598355, 0) - [20250519_003627.]: Entered 'hyperbolic_regression'-Function - [20250519_003627.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003627.]: Entered 'cubic_regression'-Function - [20250519_003627.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003627.]: # CpG-site: CpG#6 + [20250519_004353.]: Logging df_agg: CpG#5 + [20250519_004353.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004353.]: c(0, 10.4570373964758, 26.3229081557653, 39.9371114374682, 53.0630784939681, 63.9590902125397, 77.051078420522, 86.3095786035144, 100)[20250519_004353.]: c(0, 2.0429626035242, 1.3229081557653, 2.4371114374682, 3.0630784939681, 1.4590902125397, 2.051078420522, 1.1904213964856, 0)[20250519_004353.]: c(NA, 16.3437008281936, 5.2916326230612, 6.49896383324852, 6.1261569879362, 2.33454434006352, 2.73477122736267, 1.36048159598355, 0) + [20250519_004353.]: Entered 'hyperbolic_regression'-Function + [20250519_004353.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004354.]: Entered 'cubic_regression'-Function + [20250519_004354.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004354.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.15031343762292, 19.2804724179951, 26.5529108075111, 39.8450445790276, 54.9734681583699, 64.742519688394, 81.444841573602, 100)c(0, 4.34968656237708, 5.7195275820049, 10.9470891924889, 10.1549554209724, 7.5265318416301, 10.257480311606, 6.05515842639799, 0)c(NA, 34.7974924990166, 22.8781103280196, 29.1922378466371, 20.3099108419448, 12.0424509466082, 13.6766404154747, 6.92018105874057, 0) - [20250519_003627.]: Logging df_agg: CpG#6 - [20250519_003627.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003627.]: c(0, 8.15031343762292, 19.2804724179951, 26.5529108075111, 39.8450445790276, 54.9734681583699, 64.742519688394, 81.444841573602, 100)[20250519_003627.]: c(0, 4.34968656237708, 5.7195275820049, 10.9470891924889, 10.1549554209724, 7.5265318416301, 10.257480311606, 6.05515842639799, 0)[20250519_003627.]: c(NA, 34.7974924990166, 22.8781103280196, 29.1922378466371, 20.3099108419448, 12.0424509466082, 13.6766404154747, 6.92018105874057, 0) - [20250519_003627.]: Entered 'hyperbolic_regression'-Function - [20250519_003627.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003627.]: Entered 'cubic_regression'-Function - [20250519_003627.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003627.]: # CpG-site: CpG#7 + [20250519_004354.]: Logging df_agg: CpG#6 + [20250519_004354.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004354.]: c(0, 8.15031343762292, 19.2804724179951, 26.5529108075111, 39.8450445790276, 54.9734681583699, 64.742519688394, 81.444841573602, 100)[20250519_004354.]: c(0, 4.34968656237708, 5.7195275820049, 10.9470891924889, 10.1549554209724, 7.5265318416301, 10.257480311606, 6.05515842639799, 0)[20250519_004354.]: c(NA, 34.7974924990166, 22.8781103280196, 29.1922378466371, 20.3099108419448, 12.0424509466082, 13.6766404154747, 6.92018105874057, 0) + [20250519_004354.]: Entered 'hyperbolic_regression'-Function + [20250519_004354.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004354.]: Entered 'cubic_regression'-Function + [20250519_004354.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004354.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 3.66155933245296, 9.01334562885863, 14.9602090097958, 23.8420840440459, 32.8944868748769, 42.0684566960277, 46.1959470960303, 99.9999999999999)c(0, 8.83844066754704, 15.9866543711414, 22.5397909902042, 26.1579159559541, 29.6055131251231, 32.9315433039723, 41.3040529039697, 9.9475983006414e-14)c(NA, 70.7075253403763, 63.9466174845655, 60.1061093072112, 52.3158319119082, 47.368821000197, 43.9087244052964, 47.2046318902511, 9.9475983006414e-14) - [20250519_003627.]: Logging df_agg: CpG#7 - [20250519_003627.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003627.]: c(0, 3.66155933245296, 9.01334562885863, 14.9602090097958, 23.8420840440459, 32.8944868748769, 42.0684566960277, 46.1959470960303, 99.9999999999999)[20250519_003627.]: c(0, 8.83844066754704, 15.9866543711414, 22.5397909902042, 26.1579159559541, 29.6055131251231, 32.9315433039723, 41.3040529039697, 9.9475983006414e-14)[20250519_003627.]: c(NA, 70.7075253403763, 63.9466174845655, 60.1061093072112, 52.3158319119082, 47.368821000197, 43.9087244052964, 47.2046318902511, 9.9475983006414e-14) - [20250519_003627.]: Entered 'hyperbolic_regression'-Function - [20250519_003627.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003628.]: Entered 'cubic_regression'-Function - [20250519_003628.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003628.]: # CpG-site: CpG#8 + [20250519_004354.]: Logging df_agg: CpG#7 + [20250519_004354.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004354.]: c(0, 3.66155933245296, 9.01334562885863, 14.9602090097958, 23.8420840440459, 32.8944868748769, 42.0684566960277, 46.1959470960303, 99.9999999999999)[20250519_004354.]: c(0, 8.83844066754704, 15.9866543711414, 22.5397909902042, 26.1579159559541, 29.6055131251231, 32.9315433039723, 41.3040529039697, 9.9475983006414e-14)[20250519_004354.]: c(NA, 70.7075253403763, 63.9466174845655, 60.1061093072112, 52.3158319119082, 47.368821000197, 43.9087244052964, 47.2046318902511, 9.9475983006414e-14) + [20250519_004354.]: Entered 'hyperbolic_regression'-Function + [20250519_004354.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004355.]: Entered 'cubic_regression'-Function + [20250519_004355.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004355.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.96146907472175, 28.1602892189673, 37.1893361505397, 54.6609191320759, 66.6424746382684, 79.0794300908275, 81.6808296744085, 100)c(0, 3.53853092527825, 3.1602892189673, 0.310663849460298, 4.6609191320759, 4.1424746382684, 4.0794300908275, 5.8191703255915, 0)c(NA, 28.308247402226, 12.6411568758692, 0.828436931894127, 9.32183826415179, 6.62795942122943, 5.43924012110333, 6.65048037210457, 0) - [20250519_003628.]: Logging df_agg: CpG#8 - [20250519_003628.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003628.]: c(0, 8.96146907472175, 28.1602892189673, 37.1893361505397, 54.6609191320759, 66.6424746382684, 79.0794300908275, 81.6808296744085, 100)[20250519_003628.]: c(0, 3.53853092527825, 3.1602892189673, 0.310663849460298, 4.6609191320759, 4.1424746382684, 4.0794300908275, 5.8191703255915, 0)[20250519_003628.]: c(NA, 28.308247402226, 12.6411568758692, 0.828436931894127, 9.32183826415179, 6.62795942122943, 5.43924012110333, 6.65048037210457, 0) - [20250519_003628.]: Entered 'hyperbolic_regression'-Function - [20250519_003628.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003628.]: Entered 'cubic_regression'-Function - [20250519_003628.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003628.]: # CpG-site: CpG#9 + [20250519_004355.]: Logging df_agg: CpG#8 + [20250519_004355.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004355.]: c(0, 8.96146907472175, 28.1602892189673, 37.1893361505397, 54.6609191320759, 66.6424746382684, 79.0794300908275, 81.6808296744085, 100)[20250519_004355.]: c(0, 3.53853092527825, 3.1602892189673, 0.310663849460298, 4.6609191320759, 4.1424746382684, 4.0794300908275, 5.8191703255915, 0)[20250519_004355.]: c(NA, 28.308247402226, 12.6411568758692, 0.828436931894127, 9.32183826415179, 6.62795942122943, 5.43924012110333, 6.65048037210457, 0) + [20250519_004355.]: Entered 'hyperbolic_regression'-Function + [20250519_004355.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004355.]: Entered 'cubic_regression'-Function + [20250519_004355.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004355.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 3.60796342124625, 9.37027096567147, 13.6529015267019, 21.8965479410597, 31.2983747080124, 40.6887948365007, 53.9067876123417, 100)c(0, 8.89203657875375, 15.6297290343285, 23.8470984732981, 28.1034520589403, 31.2016252919876, 34.3112051634993, 33.5932123876583, 0)c(NA, 71.13629263003, 62.5189161373141, 63.5922625954616, 56.2069041178806, 49.9226004671802, 45.7482735513324, 38.3922427287523, 0) - [20250519_003628.]: Logging df_agg: CpG#9 - [20250519_003628.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003628.]: c(0, 3.60796342124625, 9.37027096567147, 13.6529015267019, 21.8965479410597, 31.2983747080124, 40.6887948365007, 53.9067876123417, 100)[20250519_003628.]: c(0, 8.89203657875375, 15.6297290343285, 23.8470984732981, 28.1034520589403, 31.2016252919876, 34.3112051634993, 33.5932123876583, 0)[20250519_003628.]: c(NA, 71.13629263003, 62.5189161373141, 63.5922625954616, 56.2069041178806, 49.9226004671802, 45.7482735513324, 38.3922427287523, 0) - [20250519_003628.]: Entered 'hyperbolic_regression'-Function - [20250519_003628.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003629.]: Entered 'cubic_regression'-Function - [20250519_003629.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003629.]: # CpG-site: row_means + [20250519_004355.]: Logging df_agg: CpG#9 + [20250519_004355.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004355.]: c(0, 3.60796342124625, 9.37027096567147, 13.6529015267019, 21.8965479410597, 31.2983747080124, 40.6887948365007, 53.9067876123417, 100)[20250519_004355.]: c(0, 8.89203657875375, 15.6297290343285, 23.8470984732981, 28.1034520589403, 31.2016252919876, 34.3112051634993, 33.5932123876583, 0)[20250519_004355.]: c(NA, 71.13629263003, 62.5189161373141, 63.5922625954616, 56.2069041178806, 49.9226004671802, 45.7482735513324, 38.3922427287523, 0) + [20250519_004355.]: Entered 'hyperbolic_regression'-Function + [20250519_004355.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004355.]: Entered 'cubic_regression'-Function + [20250519_004355.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004355.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.000118249869729556, 6.10737244290489, 15.2822474794705, 23.3309318476314, 35.1067698776441, 49.3683845454072, 61.0436420088989, 70.9843655625978, 100)c(0.000118249869729556, 6.39262755709511, 9.7177525205295, 14.1690681523686, 14.8932301223559, 13.1316154545928, 13.9563579911011, 16.5156344374022, 0)c(NA, 51.1410204567609, 38.871010082118, 37.7841817396496, 29.7864602447118, 21.0105847273485, 18.6084773214681, 18.8750107856025, 0) - [20250519_003629.]: Logging df_agg: row_means - [20250519_003629.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003629.]: c(0.000118249869729556, 6.10737244290489, 15.2822474794705, 23.3309318476314, 35.1067698776441, 49.3683845454072, 61.0436420088989, 70.9843655625978, 100)[20250519_003629.]: c(0.000118249869729556, 6.39262755709511, 9.7177525205295, 14.1690681523686, 14.8932301223559, 13.1316154545928, 13.9563579911011, 16.5156344374022, 0)[20250519_003629.]: c(NA, 51.1410204567609, 38.871010082118, 37.7841817396496, 29.7864602447118, 21.0105847273485, 18.6084773214681, 18.8750107856025, 0) - [20250519_003629.]: Entered 'hyperbolic_regression'-Function - [20250519_003629.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003629.]: Entered 'cubic_regression'-Function - [20250519_003629.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_003630.]: Entered 'solving_equations'-Function - [20250519_003630.]: Solving hyperbolic regression for CpG#1 + [20250519_004355.]: Logging df_agg: row_means + [20250519_004355.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004355.]: c(0.000118249869729556, 6.10737244290489, 15.2822474794705, 23.3309318476314, 35.1067698776441, 49.3683845454072, 61.0436420088989, 70.9843655625978, 100)[20250519_004355.]: c(0.000118249869729556, 6.39262755709511, 9.7177525205295, 14.1690681523686, 14.8932301223559, 13.1316154545928, 13.9563579911011, 16.5156344374022, 0)[20250519_004355.]: c(NA, 51.1410204567609, 38.871010082118, 37.7841817396496, 29.7864602447118, 21.0105847273485, 18.6084773214681, 18.8750107856025, 0) + [20250519_004355.]: Entered 'hyperbolic_regression'-Function + [20250519_004355.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004356.]: Entered 'cubic_regression'-Function + [20250519_004356.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004356.]: Entered 'solving_equations'-Function + [20250519_004356.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 78.9992198550223 - [20250519_003630.]: Samplename: Sample#1 + [20250519_004356.]: Samplename: Sample#1 Root: 78.999 --> Root in between the borders! Added to results. Hyperbolic solved: 31.2870579236875 - [20250519_003630.]: Samplename: Sample#10 + [20250519_004356.]: Samplename: Sample#10 Root: 31.287 --> Root in between the borders! Added to results. Hyperbolic solved: 42.7215290847412 - [20250519_003630.]: Samplename: Sample#2 + [20250519_004356.]: Samplename: Sample#2 Root: 42.722 --> Root in between the borders! Added to results. Hyperbolic solved: 57.8350974981729 - [20250519_003630.]: Samplename: Sample#3 + [20250519_004356.]: Samplename: Sample#3 Root: 57.835 --> Root in between the borders! Added to results. Hyperbolic solved: 11.2415690196975 - [20250519_003630.]: Samplename: Sample#4 + [20250519_004356.]: Samplename: Sample#4 Root: 11.242 --> Root in between the borders! Added to results. Hyperbolic solved: 23.5440570752846 - [20250519_003630.]: Samplename: Sample#5 + [20250519_004356.]: Samplename: Sample#5 Root: 23.544 --> Root in between the borders! Added to results. Hyperbolic solved: 24.7858714956021 - [20250519_003630.]: Samplename: Sample#6 + [20250519_004356.]: Samplename: Sample#6 Root: 24.786 --> Root in between the borders! Added to results. Hyperbolic solved: 46.4156211496502 - [20250519_003630.]: Samplename: Sample#7 + [20250519_004356.]: Samplename: Sample#7 Root: 46.416 --> Root in between the borders! Added to results. Hyperbolic solved: 84.4614182359021 - [20250519_003630.]: Samplename: Sample#8 + [20250519_004356.]: Samplename: Sample#8 Root: 84.461 --> Root in between the borders! Added to results. Hyperbolic solved: -1.41454023853738 - [20250519_003630.]: Samplename: Sample#9 + [20250519_004356.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -1.415 --> '-10 < root < 0' --> substitute 0 - [20250519_003630.]: Solving hyperbolic regression for CpG#2 + [20250519_004356.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 76.6628425084681 - [20250519_003630.]: Samplename: Sample#1 + [20250519_004356.]: Samplename: Sample#1 Root: 76.663 --> Root in between the borders! Added to results. Hyperbolic solved: 31.002922067723 - [20250519_003630.]: Samplename: Sample#10 + [20250519_004356.]: Samplename: Sample#10 Root: 31.003 --> Root in between the borders! Added to results. Hyperbolic solved: 42.8794162472613 - [20250519_003630.]: Samplename: Sample#2 + [20250519_004356.]: Samplename: Sample#2 Root: 42.879 --> Root in between the borders! Added to results. Hyperbolic solved: 59.1562251576939 - [20250519_003630.]: Samplename: Sample#3 + [20250519_004356.]: Samplename: Sample#3 Root: 59.156 --> Root in between the borders! Added to results. Hyperbolic solved: 4.04613842773285 - [20250519_003630.]: Samplename: Sample#4 + [20250519_004356.]: Samplename: Sample#4 Root: 4.046 --> Root in between the borders! Added to results. Hyperbolic solved: 19.9574827771543 - [20250519_003630.]: Samplename: Sample#5 + [20250519_004356.]: Samplename: Sample#5 Root: 19.957 --> Root in between the borders! Added to results. Hyperbolic solved: 17.4372350357645 - [20250519_003630.]: Samplename: Sample#6 + [20250519_004356.]: Samplename: Sample#6 Root: 17.437 --> Root in between the borders! Added to results. Hyperbolic solved: 41.0968872148931 - [20250519_003630.]: Samplename: Sample#7 + [20250519_004356.]: Samplename: Sample#7 Root: 41.097 --> Root in between the borders! Added to results. Hyperbolic solved: 85.7111018674234 - [20250519_003630.]: Samplename: Sample#8 + [20250519_004356.]: Samplename: Sample#8 Root: 85.711 --> Root in between the borders! Added to results. Hyperbolic solved: 2.08554114625032 - [20250519_003630.]: Samplename: Sample#9 + [20250519_004356.]: Samplename: Sample#9 Root: 2.086 --> Root in between the borders! Added to results. - [20250519_003630.]: Solving hyperbolic regression for CpG#3 + [20250519_004356.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 74.5413598656994 - [20250519_003630.]: Samplename: Sample#1 + [20250519_004356.]: Samplename: Sample#1 Root: 74.541 --> Root in between the borders! Added to results. Hyperbolic solved: 28.3514323412579 - [20250519_003630.]: Samplename: Sample#10 + [20250519_004356.]: Samplename: Sample#10 Root: 28.351 --> Root in between the borders! Added to results. Hyperbolic solved: 42.6007641487945 - [20250519_003630.]: Samplename: Sample#2 + [20250519_004356.]: Samplename: Sample#2 Root: 42.601 --> Root in between the borders! Added to results. Hyperbolic solved: 56.3207791919885 - [20250519_003630.]: Samplename: Sample#3 + [20250519_004356.]: Samplename: Sample#3 Root: 56.321 --> Root in between the borders! Added to results. Hyperbolic solved: 7.98800399083 - [20250519_003630.]: Samplename: Sample#4 + [20250519_004356.]: Samplename: Sample#4 Root: 7.988 --> Root in between the borders! Added to results. Hyperbolic solved: 24.6963927948962 - [20250519_003630.]: Samplename: Sample#5 + [20250519_004356.]: Samplename: Sample#5 Root: 24.696 --> Root in between the borders! Added to results. Hyperbolic solved: 26.8806215804592 - [20250519_003630.]: Samplename: Sample#6 + [20250519_004356.]: Samplename: Sample#6 Root: 26.881 --> Root in between the borders! Added to results. Hyperbolic solved: 44.8239489304102 - [20250519_003630.]: Samplename: Sample#7 + [20250519_004356.]: Samplename: Sample#7 Root: 44.824 --> Root in between the borders! Added to results. Hyperbolic solved: 84.669654917361 - [20250519_003630.]: Samplename: Sample#8 + [20250519_004356.]: Samplename: Sample#8 Root: 84.67 --> Root in between the borders! Added to results. Hyperbolic solved: -1.26160051687973 - [20250519_003630.]: Samplename: Sample#9 + [20250519_004356.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -1.262 --> '-10 < root < 0' --> substitute 0 - [20250519_003630.]: Solving hyperbolic regression for CpG#4 + [20250519_004356.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 75.8530019120299 - [20250519_003630.]: Samplename: Sample#1 + [20250519_004356.]: Samplename: Sample#1 Root: 75.853 --> Root in between the borders! Added to results. Hyperbolic solved: 29.0711677507654 - [20250519_003630.]: Samplename: Sample#10 + [20250519_004356.]: Samplename: Sample#10 Root: 29.071 --> Root in between the borders! Added to results. Hyperbolic solved: 44.0485537238583 - [20250519_003630.]: Samplename: Sample#2 + [20250519_004356.]: Samplename: Sample#2 Root: 44.049 --> Root in between the borders! Added to results. Hyperbolic solved: 58.7878536128746 - [20250519_003630.]: Samplename: Sample#3 + [20250519_004356.]: Samplename: Sample#3 Root: 58.788 --> Root in between the borders! Added to results. Hyperbolic solved: 11.0370782280591 - [20250519_003630.]: Samplename: Sample#4 + [20250519_004356.]: Samplename: Sample#4 Root: 11.037 --> Root in between the borders! Added to results. Hyperbolic solved: 23.0042107924666 - [20250519_003630.]: Samplename: Sample#5 + [20250519_004356.]: Samplename: Sample#5 Root: 23.004 --> Root in between the borders! Added to results. Hyperbolic solved: 27.9521038368967 - [20250519_003630.]: Samplename: Sample#6 + [20250519_004356.]: Samplename: Sample#6 Root: 27.952 --> Root in between the borders! Added to results. Hyperbolic solved: 42.50025622677 - [20250519_003630.]: Samplename: Sample#7 + [20250519_004356.]: Samplename: Sample#7 Root: 42.5 --> Root in between the borders! Added to results. Hyperbolic solved: 84.6870942263369 - [20250519_003630.]: Samplename: Sample#8 + [20250519_004356.]: Samplename: Sample#8 Root: 84.687 --> Root in between the borders! Added to results. Hyperbolic solved: 3.01041300417756 - [20250519_003630.]: Samplename: Sample#9 + [20250519_004356.]: Samplename: Sample#9 Root: 3.01 --> Root in between the borders! Added to results. - [20250519_003630.]: Solving cubic regression for CpG#5 + [20250519_004357.]: Solving cubic regression for CpG#5 Coefficients: -47.8373333333333Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_003630.]: Samplename: Sample#1 + [20250519_004357.]: Samplename: Sample#1 Root: 73.686 --> Root in between the borders! Added to results. Coefficients: -13.588Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_003630.]: Samplename: Sample#10 + [20250519_004357.]: Samplename: Sample#10 Root: 28.906 --> Root in between the borders! Added to results. Coefficients: -25.3211428571429Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_003630.]: Samplename: Sample#2 + [20250519_004357.]: Samplename: Sample#2 Root: 46.761 --> Root in between the borders! Added to results. Coefficients: -32.064Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_003630.]: Samplename: Sample#3 + [20250519_004357.]: Samplename: Sample#3 Root: 55.588 --> Root in between the borders! Added to results. Coefficients: -4.074Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_003630.]: Samplename: Sample#4 + [20250519_004357.]: Samplename: Sample#4 Root: 10.297 --> Root in between the borders! Added to results. Coefficients: -11.434Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_003630.]: Samplename: Sample#5 + [20250519_004357.]: Samplename: Sample#5 Root: 25.131 --> Root in between the borders! Added to results. Coefficients: -13.294Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_003630.]: Samplename: Sample#6 + [20250519_004357.]: Samplename: Sample#6 Root: 28.402 --> Root in between the borders! Added to results. Coefficients: -24.288Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_003630.]: Samplename: Sample#7 + [20250519_004357.]: Samplename: Sample#7 Root: 45.332 --> Root in between the borders! Added to results. Coefficients: -63.134Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_003630.]: Samplename: Sample#8 + [20250519_004357.]: Samplename: Sample#8 Root: 88.909 --> Root in between the borders! Added to results. Coefficients: 0.0360000000000005Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_003630.]: Samplename: Sample#9 + [20250519_004357.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.102 --> '-10 < root < 0' --> substitute 0 - [20250519_003630.]: Solving hyperbolic regression for CpG#6 + [20250519_004357.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 79.2062097176894 - [20250519_003630.]: Samplename: Sample#1 + [20250519_004357.]: Samplename: Sample#1 Root: 79.206 --> Root in between the borders! Added to results. Hyperbolic solved: 30.234912908094 - [20250519_003630.]: Samplename: Sample#10 + [20250519_004357.]: Samplename: Sample#10 Root: 30.235 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8992139020538 - [20250519_003630.]: Samplename: Sample#2 + [20250519_004357.]: Samplename: Sample#2 Root: 41.899 --> Root in between the borders! Added to results. Hyperbolic solved: 56.8778124304882 - [20250519_003630.]: Samplename: Sample#3 + [20250519_004357.]: Samplename: Sample#3 Root: 56.878 --> Root in between the borders! Added to results. Hyperbolic solved: 8.80900690226663 - [20250519_003630.]: Samplename: Sample#4 + [20250519_004357.]: Samplename: Sample#4 Root: 8.809 --> Root in between the borders! Added to results. Hyperbolic solved: 18.6793858198319 - [20250519_003630.]: Samplename: Sample#5 + [20250519_004357.]: Samplename: Sample#5 Root: 18.679 --> Root in between the borders! Added to results. Hyperbolic solved: 29.9638526705022 - [20250519_003630.]: Samplename: Sample#6 + [20250519_004357.]: Samplename: Sample#6 Root: 29.964 --> Root in between the borders! Added to results. Hyperbolic solved: 42.8669225173731 - [20250519_003630.]: Samplename: Sample#7 + [20250519_004357.]: Samplename: Sample#7 Root: 42.867 --> Root in between the borders! Added to results. Hyperbolic solved: 86.6206311389839 - [20250519_003630.]: Samplename: Sample#8 + [20250519_004357.]: Samplename: Sample#8 Root: 86.621 --> Root in between the borders! Added to results. Hyperbolic solved: 1.38882504570857 - [20250519_003630.]: Samplename: Sample#9 + [20250519_004357.]: Samplename: Sample#9 Root: 1.389 --> Root in between the borders! Added to results. - [20250519_003630.]: Solving hyperbolic regression for CpG#7 + [20250519_004357.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 77.5203002291111 - [20250519_003630.]: Samplename: Sample#1 + [20250519_004357.]: Samplename: Sample#1 Root: 77.52 --> Root in between the borders! Added to results. Hyperbolic solved: 27.0810020209347 - [20250519_003630.]: Samplename: Sample#10 + [20250519_004357.]: Samplename: Sample#10 Root: 27.081 --> Root in between the borders! Added to results. Hyperbolic solved: 48.4274819445682 - [20250519_003630.]: Samplename: Sample#2 + [20250519_004357.]: Samplename: Sample#2 Root: 48.427 --> Root in between the borders! Added to results. Hyperbolic solved: 58.8711296078074 - [20250519_003630.]: Samplename: Sample#3 + [20250519_004357.]: Samplename: Sample#3 Root: 58.871 --> Root in between the borders! Added to results. Hyperbolic solved: 13.3245830390029 - [20250519_003630.]: Samplename: Sample#4 + [20250519_004357.]: Samplename: Sample#4 Root: 13.325 --> Root in between the borders! Added to results. Hyperbolic solved: 26.9731029843141 - [20250519_003630.]: Samplename: Sample#5 + [20250519_004357.]: Samplename: Sample#5 Root: 26.973 --> Root in between the borders! Added to results. Hyperbolic solved: 30.9519756409676 - [20250519_003630.]: Samplename: Sample#6 + [20250519_004357.]: Samplename: Sample#6 Root: 30.952 --> Root in between the borders! Added to results. Hyperbolic solved: 45.7349250650843 - [20250519_003630.]: Samplename: Sample#7 + [20250519_004357.]: Samplename: Sample#7 Root: 45.735 --> Root in between the borders! Added to results. Hyperbolic solved: 84.5977214697661 - [20250519_003630.]: Samplename: Sample#8 + [20250519_004357.]: Samplename: Sample#8 Root: 84.598 --> Root in between the borders! Added to results. Hyperbolic solved: -2.87252278311384 - [20250519_003630.]: Samplename: Sample#9 + [20250519_004357.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.873 --> '-10 < root < 0' --> substitute 0 - [20250519_003630.]: Solving cubic regression for CpG#8 + [20250519_004357.]: Solving cubic regression for CpG#8 Coefficients: -55.3573333333333Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_003630.]: Samplename: Sample#1 + [20250519_004357.]: Samplename: Sample#1 Root: 74.173 --> Root in between the borders! Added to results. Coefficients: -17.574Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_003630.]: Samplename: Sample#10 + [20250519_004357.]: Samplename: Sample#10 Root: 30.7 --> Root in between the borders! Added to results. Coefficients: -22.9425714285714Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_003630.]: Samplename: Sample#2 + [20250519_004357.]: Samplename: Sample#2 Root: 38.106 --> Root in between the borders! Added to results. Coefficients: -42.849Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_003630.]: Samplename: Sample#3 + [20250519_004357.]: Samplename: Sample#3 Root: 61.571 --> Root in between the borders! Added to results. Coefficients: -4.604Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_003630.]: Samplename: Sample#4 + [20250519_004357.]: Samplename: Sample#4 Root: 9.446 --> Root in between the borders! Added to results. Coefficients: -11.389Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_003630.]: Samplename: Sample#5 + [20250519_004357.]: Samplename: Sample#5 Root: 21.295 --> Root in between the borders! Added to results. Coefficients: -25.784Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_003630.]: Samplename: Sample#6 + [20250519_004357.]: Samplename: Sample#6 Root: 41.8 --> Root in between the borders! Added to results. Coefficients: -30.746Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_003630.]: Samplename: Sample#7 + [20250519_004357.]: Samplename: Sample#7 Root: 47.937 --> Root in between the borders! Added to results. Coefficients: -66.912Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_003630.]: Samplename: Sample#8 + [20250519_004357.]: Samplename: Sample#8 Root: 84.816 --> Root in between the borders! Added to results. Coefficients: 3.176Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_003630.]: Samplename: Sample#9 + [20250519_004357.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -7.574 --> '-10 < root < 0' --> substitute 0 - [20250519_003630.]: Solving hyperbolic regression for CpG#9 + [20250519_004357.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 80.5447416574246 - [20250519_003630.]: Samplename: Sample#1 + [20250519_004357.]: Samplename: Sample#1 Root: 80.545 --> Root in between the borders! Added to results. Hyperbolic solved: 27.8054725532271 - [20250519_003630.]: Samplename: Sample#10 + [20250519_004357.]: Samplename: Sample#10 Root: 27.805 --> Root in between the borders! Added to results. Hyperbolic solved: 46.257978181877 - [20250519_003630.]: Samplename: Sample#2 + [20250519_004357.]: Samplename: Sample#2 Root: 46.258 --> Root in between the borders! Added to results. Hyperbolic solved: 57.1842723611193 - [20250519_003630.]: Samplename: Sample#3 + [20250519_004357.]: Samplename: Sample#3 Root: 57.184 --> Root in between the borders! Added to results. Hyperbolic solved: 8.63689944440251 - [20250519_003630.]: Samplename: Sample#4 + [20250519_004357.]: Samplename: Sample#4 Root: 8.637 --> Root in between the borders! Added to results. Hyperbolic solved: 24.2116725772085 - [20250519_003630.]: Samplename: Sample#5 + [20250519_004357.]: Samplename: Sample#5 Root: 24.212 --> Root in between the borders! Added to results. Hyperbolic solved: 39.6334888107959 - [20250519_003630.]: Samplename: Sample#6 + [20250519_004357.]: Samplename: Sample#6 Root: 39.633 --> Root in between the borders! Added to results. Hyperbolic solved: 44.3019478766814 - [20250519_003630.]: Samplename: Sample#7 + [20250519_004357.]: Samplename: Sample#7 Root: 44.302 --> Root in between the borders! Added to results. Hyperbolic solved: 87.3231552867261 - [20250519_003630.]: Samplename: Sample#8 + [20250519_004357.]: Samplename: Sample#8 Root: 87.323 --> Root in between the borders! Added to results. Hyperbolic solved: -1.1792994162273 - [20250519_003630.]: Samplename: Sample#9 + [20250519_004357.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -1.179 --> '-10 < root < 0' --> substitute 0 - [20250519_003630.]: Solving hyperbolic regression for row_means + [20250519_004357.]: Solving hyperbolic regression for row_means Hyperbolic solved: 76.7712420256811 - [20250519_003630.]: Samplename: Sample#1 + [20250519_004357.]: Samplename: Sample#1 Root: 76.771 --> Root in between the borders! Added to results. Hyperbolic solved: 28.8491793177963 - [20250519_003630.]: Samplename: Sample#10 + [20250519_004357.]: Samplename: Sample#10 Root: 28.849 --> Root in between the borders! Added to results. Hyperbolic solved: 43.0342096405004 - [20250519_003630.]: Samplename: Sample#2 + [20250519_004357.]: Samplename: Sample#2 Root: 43.034 --> Root in between the borders! Added to results. Hyperbolic solved: 57.634070615517 - [20250519_003630.]: Samplename: Sample#3 + [20250519_004357.]: Samplename: Sample#3 Root: 57.634 --> Root in between the borders! Added to results. Hyperbolic solved: 8.87182398849477 - [20250519_003630.]: Samplename: Sample#4 + [20250519_004357.]: Samplename: Sample#4 Root: 8.872 --> Root in between the borders! Added to results. Hyperbolic solved: 22.1989155573519 - [20250519_003630.]: Samplename: Sample#5 + [20250519_004357.]: Samplename: Sample#5 Root: 22.199 --> Root in between the borders! Added to results. Hyperbolic solved: 29.2140219298625 - [20250519_003630.]: Samplename: Sample#6 + [20250519_004357.]: Samplename: Sample#6 Root: 29.214 --> Root in between the borders! Added to results. Hyperbolic solved: 43.9371957594463 - [20250519_003630.]: Samplename: Sample#7 + [20250519_004357.]: Samplename: Sample#7 Root: 43.937 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6706924479325 - [20250519_003630.]: Samplename: Sample#8 + [20250519_004357.]: Samplename: Sample#8 Root: 85.671 --> Root in between the borders! Added to results. Hyperbolic solved: -0.55137023392917 - [20250519_003630.]: Samplename: Sample#9 + [20250519_004357.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.551 --> '-10 < root < 0' --> substitute 0 - [20250519_003630.]: Entered 'solving_equations'-Function - [20250519_003630.]: Solving hyperbolic regression for CpG#1 + [20250519_004357.]: Entered 'solving_equations'-Function + [20250519_004357.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 0 - [20250519_003630.]: Samplename: 0 + [20250519_004357.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 14.1480167109277 - [20250519_003630.]: Samplename: 12.5 + [20250519_004357.]: Samplename: 12.5 Root: 14.148 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1398443790446 - [20250519_003630.]: Samplename: 25 + [20250519_004357.]: Samplename: 25 Root: 26.14 --> Root in between the borders! Added to results. Hyperbolic solved: 39.3762039523382 - [20250519_003630.]: Samplename: 37.5 + [20250519_004357.]: Samplename: 37.5 Root: 39.376 --> Root in between the borders! Added to results. Hyperbolic solved: 52.9476244028783 - [20250519_003630.]: Samplename: 50 + [20250519_004357.]: Samplename: 50 Root: 52.948 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4195105873419 - [20250519_003630.]: Samplename: 62.5 + [20250519_004357.]: Samplename: 62.5 Root: 65.42 --> Root in between the borders! Added to results. Hyperbolic solved: 74.433837698331 - [20250519_003630.]: Samplename: 75 + [20250519_004357.]: Samplename: 75 Root: 74.434 --> Root in between the borders! Added to results. Hyperbolic solved: 80.5559264925808 - [20250519_003630.]: Samplename: 87.5 + [20250519_004357.]: Samplename: 87.5 Root: 80.556 --> Root in between the borders! Added to results. Hyperbolic solved: 100 - [20250519_003630.]: Samplename: 100 + [20250519_004357.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. - [20250519_003630.]: Solving hyperbolic regression for CpG#2 + [20250519_004357.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 0 - [20250519_003630.]: Samplename: 0 + [20250519_004357.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 10.7812303822548 - [20250519_003630.]: Samplename: 12.5 + [20250519_004357.]: Samplename: 12.5 Root: 10.781 --> Root in between the borders! Added to results. Hyperbolic solved: 26.0648314401535 - [20250519_003630.]: Samplename: 25 + [20250519_004357.]: Samplename: 25 Root: 26.065 --> Root in between the borders! Added to results. Hyperbolic solved: 35.1980950192158 - [20250519_003630.]: Samplename: 37.5 + [20250519_004357.]: Samplename: 37.5 Root: 35.198 --> Root in between the borders! Added to results. Hyperbolic solved: 47.9203836090837 - [20250519_003630.]: Samplename: 50 + [20250519_004357.]: Samplename: 50 Root: 47.92 --> Root in between the borders! Added to results. Hyperbolic solved: 67.275750545092 - [20250519_003630.]: Samplename: 62.5 + [20250519_004357.]: Samplename: 62.5 Root: 67.276 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7278143684244 - [20250519_003630.]: Samplename: 75 + [20250519_004357.]: Samplename: 75 Root: 75.728 --> Root in between the borders! Added to results. Hyperbolic solved: 84.1258428690749 - [20250519_003630.]: Samplename: 87.5 + [20250519_004357.]: Samplename: 87.5 Root: 84.126 --> Root in between the borders! Added to results. Hyperbolic solved: 100 - [20250519_003630.]: Samplename: 100 + [20250519_004357.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. - [20250519_003630.]: Solving hyperbolic regression for CpG#3 + [20250519_004357.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 0 - [20250519_003630.]: Samplename: 0 + [20250519_004357.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 10.8467198180169 - [20250519_003630.]: Samplename: 12.5 + [20250519_004357.]: Samplename: 12.5 Root: 10.847 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1449700612555 - [20250519_003630.]: Samplename: 25 + [20250519_004357.]: Samplename: 25 Root: 26.145 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2865760013773 - [20250519_003630.]: Samplename: 37.5 + [20250519_004357.]: Samplename: 37.5 Root: 37.287 --> Root in between the borders! Added to results. Hyperbolic solved: 51.4114078772697 - [20250519_003630.]: Samplename: 50 + [20250519_004357.]: Samplename: 50 Root: 51.411 --> Root in between the borders! Added to results. Hyperbolic solved: 65.0139634978039 - [20250519_003630.]: Samplename: 62.5 + [20250519_004357.]: Samplename: 62.5 Root: 65.014 --> Root in between the borders! Added to results. Hyperbolic solved: 76.9921394830877 - [20250519_003630.]: Samplename: 75 + [20250519_004357.]: Samplename: 75 Root: 76.992 --> Root in between the borders! Added to results. Hyperbolic solved: 79.6808818589171 - [20250519_003630.]: Samplename: 87.5 + [20250519_004357.]: Samplename: 87.5 Root: 79.681 --> Root in between the borders! Added to results. Hyperbolic solved: 100 - [20250519_003630.]: Samplename: 100 + [20250519_004357.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. - [20250519_003630.]: Solving hyperbolic regression for CpG#4 + [20250519_004357.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 0 - [20250519_003630.]: Samplename: 0 + [20250519_004357.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 13.2494913489925 - [20250519_003630.]: Samplename: 12.5 + [20250519_004357.]: Samplename: 12.5 Root: 13.249 --> Root in between the borders! Added to results. Hyperbolic solved: 25.0914701740206 - [20250519_003630.]: Samplename: 25 + [20250519_004357.]: Samplename: 25 Root: 25.091 --> Root in between the borders! Added to results. Hyperbolic solved: 38.8081740444332 - [20250519_003630.]: Samplename: 37.5 + [20250519_004357.]: Samplename: 37.5 Root: 38.808 --> Root in between the borders! Added to results. Hyperbolic solved: 49.1133034150896 - [20250519_003630.]: Samplename: 50 + [20250519_004357.]: Samplename: 50 Root: 49.113 --> Root in between the borders! Added to results. Hyperbolic solved: 67.573566028728 - [20250519_003630.]: Samplename: 62.5 + [20250519_004357.]: Samplename: 62.5 Root: 67.574 --> Root in between the borders! Added to results. Hyperbolic solved: 73.7655861480302 - [20250519_003630.]: Samplename: 75 + [20250519_004357.]: Samplename: 75 Root: 73.766 --> Root in between the borders! Added to results. Hyperbolic solved: 82.0404940898918 - [20250519_003630.]: Samplename: 87.5 + [20250519_004357.]: Samplename: 87.5 Root: 82.04 --> Root in between the borders! Added to results. Hyperbolic solved: 100 - [20250519_003630.]: Samplename: 100 + [20250519_004357.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. - [20250519_003630.]: Solving cubic regression for CpG#5 + [20250519_004357.]: Solving cubic regression for CpG#5 Coefficients: 0Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_003630.]: Samplename: 0 + [20250519_004357.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -4.144Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_003630.]: Samplename: 12.5 + [20250519_004357.]: Samplename: 12.5 Root: 10.457 --> Root in between the borders! Added to results. Coefficients: -12.102Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_003630.]: Samplename: 25 + [20250519_004357.]: Samplename: 25 Root: 26.323 --> Root in between the borders! Added to results. Coefficients: -20.536Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_003630.]: Samplename: 37.5 + [20250519_004357.]: Samplename: 37.5 Root: 39.937 --> Root in between the borders! Added to results. Coefficients: -30.0715Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_003630.]: Samplename: 50 + [20250519_004357.]: Samplename: 50 Root: 53.063 --> Root in between the borders! Added to results. Coefficients: -39.034Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_003630.]: Samplename: 62.5 + [20250519_004357.]: Samplename: 62.5 Root: 63.959 --> Root in between the borders! Added to results. Coefficients: -51.059Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_003630.]: Samplename: 75 + [20250519_004357.]: Samplename: 75 Root: 77.051 --> Root in between the borders! Added to results. Coefficients: -60.3906666666667Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_003630.]: Samplename: 87.5 + [20250519_004357.]: Samplename: 87.5 Root: 86.31 --> Root in between the borders! Added to results. Coefficients: -75.446Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_003630.]: Samplename: 100 + [20250519_004357.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. - [20250519_003630.]: Solving hyperbolic regression for CpG#6 + [20250519_004357.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 0 - [20250519_003630.]: Samplename: 0 + [20250519_004357.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 11.8139244542506 - [20250519_003630.]: Samplename: 12.5 + [20250519_004357.]: Samplename: 12.5 Root: 11.814 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5330423964606 - [20250519_003630.]: Samplename: 25 + [20250519_004357.]: Samplename: 25 Root: 26.533 --> Root in between the borders! Added to results. Hyperbolic solved: 35.365455365759 - [20250519_003630.]: Samplename: 37.5 + [20250519_004357.]: Samplename: 37.5 Root: 35.365 --> Root in between the borders! Added to results. Hyperbolic solved: 50.1054345020203 - [20250519_003630.]: Samplename: 50 + [20250519_004357.]: Samplename: 50 Root: 50.105 --> Root in between the borders! Added to results. Hyperbolic solved: 64.9683746571925 - [20250519_003630.]: Samplename: 62.5 + [20250519_004357.]: Samplename: 62.5 Root: 64.968 --> Root in between the borders! Added to results. Hyperbolic solved: 73.6331726521876 - [20250519_003630.]: Samplename: 75 + [20250519_004357.]: Samplename: 75 Root: 73.633 --> Root in between the borders! Added to results. Hyperbolic solved: 86.9938602772804 - [20250519_003630.]: Samplename: 87.5 + [20250519_004357.]: Samplename: 87.5 Root: 86.994 --> Root in between the borders! Added to results. Hyperbolic solved: 100 - [20250519_003630.]: Samplename: 100 + [20250519_004357.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. - [20250519_003630.]: Solving hyperbolic regression for CpG#7 + [20250519_004357.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 0 - [20250519_003630.]: Samplename: 0 + [20250519_004357.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 11.7880491100164 - [20250519_003630.]: Samplename: 12.5 + [20250519_004357.]: Samplename: 12.5 Root: 11.788 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1959234034328 - [20250519_003630.]: Samplename: 25 + [20250519_004357.]: Samplename: 25 Root: 26.196 --> Root in between the borders! Added to results. Hyperbolic solved: 39.1978567589215 - [20250519_003630.]: Samplename: 37.5 + [20250519_004357.]: Samplename: 37.5 Root: 39.198 --> Root in between the borders! Added to results. Hyperbolic solved: 54.3513505291649 - [20250519_003630.]: Samplename: 50 + [20250519_004357.]: Samplename: 50 Root: 54.351 --> Root in between the borders! Added to results. Hyperbolic solved: 66.0567953704493 - [20250519_003630.]: Samplename: 62.5 + [20250519_004357.]: Samplename: 62.5 Root: 66.057 --> Root in between the borders! Added to results. Hyperbolic solved: 75.1900867269869 - [20250519_003630.]: Samplename: 75 + [20250519_004357.]: Samplename: 75 Root: 75.19 --> Root in between the borders! Added to results. Hyperbolic solved: 78.6051660349008 - [20250519_003630.]: Samplename: 87.5 + [20250519_004357.]: Samplename: 87.5 Root: 78.605 --> Root in between the borders! Added to results. Hyperbolic solved: 100 - [20250519_003630.]: Samplename: 100 + [20250519_004357.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. - [20250519_003630.]: Solving cubic regression for CpG#8 + [20250519_004357.]: Solving cubic regression for CpG#8 Coefficients: 0Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_003630.]: Samplename: 0 + [20250519_004357.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -4.35066666666667Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_003630.]: Samplename: 12.5 + [20250519_004357.]: Samplename: 12.5 Root: 8.961 --> Root in between the borders! Added to results. Coefficients: -15.834Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_003630.]: Samplename: 25 + [20250519_004357.]: Samplename: 25 Root: 28.16 --> Root in between the borders! Added to results. Coefficients: -22.254Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_003630.]: Samplename: 37.5 + [20250519_004357.]: Samplename: 37.5 Root: 37.189 --> Root in between the borders! Added to results. Coefficients: -36.529Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_003630.]: Samplename: 50 + [20250519_004357.]: Samplename: 50 Root: 54.661 --> Root in between the borders! Added to results. Coefficients: -47.73Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_003630.]: Samplename: 62.5 + [20250519_004357.]: Samplename: 62.5 Root: 66.642 --> Root in between the borders! Added to results. Coefficients: -60.5715Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_003630.]: Samplename: 75 + [20250519_004357.]: Samplename: 75 Root: 79.079 --> Root in between the borders! Added to results. Coefficients: -63.414Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_003630.]: Samplename: 87.5 + [20250519_004357.]: Samplename: 87.5 Root: 81.681 --> Root in between the borders! Added to results. Coefficients: -84.964Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_003630.]: Samplename: 100 + [20250519_004357.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. - [20250519_003630.]: Solving hyperbolic regression for CpG#9 + [20250519_004357.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 0 - [20250519_003630.]: Samplename: 0 + [20250519_004357.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 12.2068234201026 - [20250519_003630.]: Samplename: 12.5 + [20250519_004357.]: Samplename: 12.5 Root: 12.207 --> Root in between the borders! Added to results. Hyperbolic solved: 28.0688737636578 - [20250519_003630.]: Samplename: 25 + [20250519_004357.]: Samplename: 25 Root: 28.069 --> Root in between the borders! Added to results. Hyperbolic solved: 37.6661823177513 - [20250519_003630.]: Samplename: 37.5 + [20250519_004357.]: Samplename: 37.5 Root: 37.666 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3684233460174 - [20250519_003630.]: Samplename: 50 + [20250519_004357.]: Samplename: 50 Root: 52.368 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8636916649907 - [20250519_003630.]: Samplename: 62.5 + [20250519_004357.]: Samplename: 62.5 Root: 64.864 --> Root in between the borders! Added to results. Hyperbolic solved: 74.2551330907062 - [20250519_003630.]: Samplename: 75 + [20250519_004357.]: Samplename: 75 Root: 74.255 --> Root in between the borders! Added to results. Hyperbolic solved: 83.9343288872042 - [20250519_003630.]: Samplename: 87.5 + [20250519_004357.]: Samplename: 87.5 Root: 83.934 --> Root in between the borders! Added to results. Hyperbolic solved: 100 - [20250519_003630.]: Samplename: 100 + [20250519_004357.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. - [20250519_003630.]: Solving hyperbolic regression for row_means + [20250519_004357.]: Solving hyperbolic regression for row_means Hyperbolic solved: 0.000228160853766634 - [20250519_003630.]: Samplename: 0 + [20250519_004357.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 11.1587839347378 - [20250519_003630.]: Samplename: 12.5 + [20250519_004357.]: Samplename: 12.5 Root: 11.159 --> Root in between the borders! Added to results. Hyperbolic solved: 25.8570776700364 - [20250519_003630.]: Samplename: 25 + [20250519_004357.]: Samplename: 25 Root: 25.857 --> Root in between the borders! Added to results. Hyperbolic solved: 37.065004454281 - [20250519_003630.]: Samplename: 37.5 + [20250519_004357.]: Samplename: 37.5 Root: 37.065 --> Root in between the borders! Added to results. Hyperbolic solved: 51.1881763075298 - [20250519_003630.]: Samplename: 50 + [20250519_004357.]: Samplename: 50 Root: 51.188 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4439754555105 - [20250519_003630.]: Samplename: 62.5 + [20250519_004357.]: Samplename: 62.5 Root: 65.444 --> Root in between the borders! Added to results. Hyperbolic solved: 75.3005883283082 - [20250519_003630.]: Samplename: 75 + [20250519_004357.]: Samplename: 75 Root: 75.301 --> Root in between the borders! Added to results. Hyperbolic solved: 82.6591025877361 - [20250519_003630.]: Samplename: 87.5 + [20250519_004357.]: Samplename: 87.5 Root: 82.659 --> Root in between the borders! Added to results. Hyperbolic solved: 100.000153506599 - [20250519_003630.]: Samplename: 100 + [20250519_004357.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 - [20250519_003635.]: on_start: using future::plan("sequential") - [20250519_003635.]: Entered 'clean_dt'-Function - [20250519_003635.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_003635.]: got experimental data - [20250519_003635.]: Entered 'clean_dt'-Function - [20250519_003635.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_003635.]: got calibration data - [20250519_003635.]: + [20250519_004359.]: on_start: using future::plan("sequential") + [20250519_004359.]: Entered 'clean_dt'-Function + [20250519_004359.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_004359.]: got experimental data + [20250519_004359.]: Entered 'clean_dt'-Function + [20250519_004359.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_004359.]: got calibration data + [20250519_004359.]: ### Starting with regression calculations ### - [20250519_003635.]: Entered 'regression_type1'-Function + [20250519_004359.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : @@ -12371,141 +12368,141 @@ singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient - [20250519_003635.]: # CpG-site: CpG#1 + [20250519_004359.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_003635.]: Logging df_agg: CpG#1 - [20250519_003635.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003635.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_003635.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_003635.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_003635.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_003635.]: Entered 'hyperbolic_regression'-Function - [20250519_003635.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003635.]: Entered 'cubic_regression'-Function - [20250519_003635.]: 'cubic_regression': minmax = FALSE - [20250519_003635.]: # CpG-site: CpG#2 + [20250519_004359.]: Logging df_agg: CpG#1 + [20250519_004359.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004359.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_004359.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_004359.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_004359.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) + [20250519_004359.]: Entered 'hyperbolic_regression'-Function + [20250519_004359.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004359.]: Entered 'cubic_regression'-Function + [20250519_004359.]: 'cubic_regression': minmax = FALSE + [20250519_004359.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_003635.]: Logging df_agg: CpG#2 - [20250519_003635.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003635.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_003635.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_003635.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_003635.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_003635.]: Entered 'hyperbolic_regression'-Function - [20250519_003635.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003636.]: Entered 'cubic_regression'-Function - [20250519_003636.]: 'cubic_regression': minmax = FALSE - [20250519_003636.]: # CpG-site: CpG#3 + [20250519_004359.]: Logging df_agg: CpG#2 + [20250519_004359.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004359.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_004359.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_004359.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_004359.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) + [20250519_004359.]: Entered 'hyperbolic_regression'-Function + [20250519_004359.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004400.]: Entered 'cubic_regression'-Function + [20250519_004400.]: 'cubic_regression': minmax = FALSE + [20250519_004400.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_003636.]: Logging df_agg: CpG#3 - [20250519_003636.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003636.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_003636.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_003636.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_003636.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_003636.]: Entered 'hyperbolic_regression'-Function - [20250519_003636.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003636.]: Entered 'cubic_regression'-Function - [20250519_003636.]: 'cubic_regression': minmax = FALSE - [20250519_003636.]: # CpG-site: CpG#4 + [20250519_004400.]: Logging df_agg: CpG#3 + [20250519_004400.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004400.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_004400.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_004400.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_004400.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) + [20250519_004400.]: Entered 'hyperbolic_regression'-Function + [20250519_004400.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004400.]: Entered 'cubic_regression'-Function + [20250519_004400.]: 'cubic_regression': minmax = FALSE + [20250519_004400.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_003636.]: Logging df_agg: CpG#4 - [20250519_003636.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003636.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_003636.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_003636.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_003636.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_003636.]: Entered 'hyperbolic_regression'-Function - [20250519_003636.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003636.]: Entered 'cubic_regression'-Function - [20250519_003636.]: 'cubic_regression': minmax = FALSE - [20250519_003636.]: # CpG-site: CpG#5 + [20250519_004400.]: Logging df_agg: CpG#4 + [20250519_004400.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004400.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_004400.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_004400.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_004400.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) + [20250519_004400.]: Entered 'hyperbolic_regression'-Function + [20250519_004400.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004400.]: Entered 'cubic_regression'-Function + [20250519_004400.]: 'cubic_regression': minmax = FALSE + [20250519_004400.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_003636.]: Logging df_agg: CpG#5 - [20250519_003636.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003636.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_003636.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_003636.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_003636.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_003636.]: Entered 'hyperbolic_regression'-Function - [20250519_003636.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003636.]: Entered 'cubic_regression'-Function - [20250519_003636.]: 'cubic_regression': minmax = FALSE - [20250519_003636.]: # CpG-site: CpG#6 + [20250519_004400.]: Logging df_agg: CpG#5 + [20250519_004400.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004400.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_004400.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_004400.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_004400.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) + [20250519_004400.]: Entered 'hyperbolic_regression'-Function + [20250519_004400.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004400.]: Entered 'cubic_regression'-Function + [20250519_004400.]: 'cubic_regression': minmax = FALSE + [20250519_004400.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_003636.]: Logging df_agg: CpG#6 - [20250519_003636.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003636.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_003636.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_003636.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_003636.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_003636.]: Entered 'hyperbolic_regression'-Function - [20250519_003636.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003637.]: Entered 'cubic_regression'-Function - [20250519_003637.]: 'cubic_regression': minmax = FALSE - [20250519_003637.]: # CpG-site: CpG#7 + [20250519_004400.]: Logging df_agg: CpG#6 + [20250519_004400.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004400.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_004400.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_004400.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_004400.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) + [20250519_004400.]: Entered 'hyperbolic_regression'-Function + [20250519_004400.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004401.]: Entered 'cubic_regression'-Function + [20250519_004401.]: 'cubic_regression': minmax = FALSE + [20250519_004401.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_003637.]: Logging df_agg: CpG#7 - [20250519_003637.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003637.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_003637.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_003637.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_003637.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_003637.]: Entered 'hyperbolic_regression'-Function - [20250519_003637.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003637.]: Entered 'cubic_regression'-Function - [20250519_003637.]: 'cubic_regression': minmax = FALSE - [20250519_003637.]: # CpG-site: CpG#8 + [20250519_004401.]: Logging df_agg: CpG#7 + [20250519_004401.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004401.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_004401.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_004401.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_004401.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) + [20250519_004401.]: Entered 'hyperbolic_regression'-Function + [20250519_004401.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004401.]: Entered 'cubic_regression'-Function + [20250519_004401.]: 'cubic_regression': minmax = FALSE + [20250519_004401.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_003637.]: Logging df_agg: CpG#8 - [20250519_003637.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003637.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_003637.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_003637.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_003637.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_003637.]: Entered 'hyperbolic_regression'-Function - [20250519_003637.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003637.]: Entered 'cubic_regression'-Function - [20250519_003637.]: 'cubic_regression': minmax = FALSE - [20250519_003637.]: # CpG-site: CpG#9 + [20250519_004401.]: Logging df_agg: CpG#8 + [20250519_004401.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004401.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_004401.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_004401.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_004401.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) + [20250519_004401.]: Entered 'hyperbolic_regression'-Function + [20250519_004401.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004401.]: Entered 'cubic_regression'-Function + [20250519_004401.]: 'cubic_regression': minmax = FALSE + [20250519_004401.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_003637.]: Logging df_agg: CpG#9 - [20250519_003637.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003637.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_003637.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_003637.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_003637.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_003637.]: Entered 'hyperbolic_regression'-Function - [20250519_003637.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003637.]: Entered 'cubic_regression'-Function - [20250519_003637.]: 'cubic_regression': minmax = FALSE - [20250519_003637.]: # CpG-site: row_means + [20250519_004401.]: Logging df_agg: CpG#9 + [20250519_004401.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004401.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_004401.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_004401.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_004401.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) + [20250519_004401.]: Entered 'hyperbolic_regression'-Function + [20250519_004401.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004401.]: Entered 'cubic_regression'-Function + [20250519_004401.]: 'cubic_regression': minmax = FALSE + [20250519_004401.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723122, 2.47760998435345)c(5.94911111111111, 0.84962962962963, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703704, 19.6995555555555, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_003637.]: Logging df_agg: row_means - [20250519_003637.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003637.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_003637.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723122, 2.47760998435345)[20250519_003637.]: c(5.94911111111111, 0.84962962962963, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_003637.]: c(NA, 6.79703703703704, 19.6995555555555, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_003637.]: Entered 'hyperbolic_regression'-Function - [20250519_003637.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003638.]: Entered 'cubic_regression'-Function - [20250519_003638.]: 'cubic_regression': minmax = FALSE - [20250519_003638.]: ### Starting with plotting ### - [20250519_003638.]: Creating plot No. 1 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG1.png - [20250519_003638.]: # CpG-site: CpG#1 + [20250519_004401.]: Logging df_agg: row_means + [20250519_004401.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004401.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_004401.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723122, 2.47760998435345)[20250519_004401.]: c(5.94911111111111, 0.84962962962963, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_004401.]: c(NA, 6.79703703703704, 19.6995555555555, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) + [20250519_004401.]: Entered 'hyperbolic_regression'-Function + [20250519_004401.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004402.]: Entered 'cubic_regression'-Function + [20250519_004402.]: 'cubic_regression': minmax = FALSE + [20250519_004402.]: ### Starting with plotting ### + [20250519_004402.]: Creating plot No. 1 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG1.png + [20250519_004402.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -937.719419567728 , a = -108.56796364622 , b = -937.719419567728 , d = -232.057111323599 - [20250519_003638.]: # CpG-site: CpG#1 + [20250519_004403.]: # CpG-site: CpG#1 Cubic: Using a = 6.53413423120091e-05 , b = -0.0055806968734969 , c = 0.784061853455188 , d = 1.93182659932656 - [20250519_003641.]: Creating plot No. 2 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG2.png - [20250519_003641.]: # CpG-site: CpG#2 + [20250519_004404.]: Creating plot No. 2 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG2.png + [20250519_004404.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = -2393.1426335766 , a = -141.862264327279 , b = -2393.1426335766 , d = -268.331583599043 - [20250519_003641.]: # CpG-site: CpG#2 + [20250519_004404.]: # CpG-site: CpG#2 Cubic: Using a = 8.04237934904601e-06 , b = 0.00293158941798942 , c = 0.514821742825076 , d = 9.27667003367003 - [20250519_003642.]: Creating plot No. 3 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG3.png - [20250519_003642.]: # CpG-site: CpG#3 + [20250519_004406.]: Creating plot No. 3 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG3.png + [20250519_004406.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = -1477.46855437885 , a = -170.424507883158 , b = -1477.46855437885 , d = -327.190157171162 - [20250519_003642.]: # CpG-site: CpG#3 + [20250519_004406.]: # CpG-site: CpG#3 Cubic: Using a = 2.30555869809204e-05 , b = -0.000797009331409346 , c = 0.62783129228796 , d = 3.88705218855218 - [20250519_003644.]: Creating plot No. 4 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG4.png - [20250519_003644.]: # CpG-site: CpG#4 + [20250519_004408.]: Creating plot No. 4 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG4.png + [20250519_004408.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = -2115.87792930847 , a = -162.169768036114 , b = -2115.87792930847 , d = -299.662268971294 - [20250519_003644.]: # CpG-site: CpG#4 + [20250519_004408.]: # CpG-site: CpG#4 Cubic: Using a = 3.2166356902357e-05 , b = -0.0015913142857143 , c = 0.697398364598366 , d = 6.17055050505048 - [20250519_003645.]: Creating plot No. 5 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG5.png - [20250519_003645.]: # CpG-site: CpG#5 + [20250519_004409.]: Creating plot No. 5 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG5.png + [20250519_004409.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = -1383.32738741788 , a = -151.440067063617 , b = -1383.32738741788 , d = -304.200253800186 - [20250519_003645.]: # CpG-site: CpG#5 + [20250519_004409.]: # CpG-site: CpG#5 Cubic: Using a = -4.48459708193036e-06 , b = 0.00375371236171235 , c = 0.422446384479718 , d = 5.17203872053872 - [20250519_003647.]: Creating plot No. 6 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG6.png - [20250519_003647.]: # CpG-site: CpG#6 + [20250519_004411.]: Creating plot No. 6 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG6.png + [20250519_004411.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = -1550.59307603208 , a = -125.016146072952 , b = -1550.59307603208 , d = -247.279679418643 - [20250519_003647.]: # CpG-site: CpG#6 + [20250519_004411.]: # CpG-site: CpG#6 Cubic: Using a = 2.38852884399552e-05 , b = 0.000918637037037021 , c = 0.561022132435467 , d = 6.1479276094276 - [20250519_003648.]: Creating plot No. 7 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG7.png - [20250519_003648.]: # CpG-site: CpG#7 + [20250519_004412.]: Creating plot No. 7 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG7.png + [20250519_004412.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -772.844192345493 , a = -75.2724875322984 , b = -772.844192345493 , d = -226.673147125363 - [20250519_003648.]: # CpG-site: CpG#7 + [20250519_004412.]: # CpG-site: CpG#7 Cubic: Using a = 4.98010505050505e-05 , b = -0.00436152150072151 , c = 0.579588917748918 , d = 1.72104545454544 - [20250519_003650.]: Creating plot No. 8 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG8.png - [20250519_003650.]: # CpG-site: CpG#8 + [20250519_004414.]: Creating plot No. 8 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG8.png + [20250519_004414.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = -3872.73616547768 , a = -278.017275815885 , b = -3872.73616547768 , d = -438.894268920994 - [20250519_003650.]: # CpG-site: CpG#8 + [20250519_004414.]: # CpG-site: CpG#8 Cubic: Using a = -1.50060965207632e-05 , b = 0.00447978143338143 , c = 0.534897737694404 , d = 9.56981481481482 - [20250519_003651.]: Creating plot No. 9 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG9.png - [20250519_003651.]: # CpG-site: CpG#9 + [20250519_004415.]: Creating plot No. 9 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG9.png + [20250519_004415.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -843.925451494751 , a = -70.6796095478879 , b = -843.925451494751 , d = -184.482153986057 - [20250519_003651.]: # CpG-site: CpG#9 + [20250519_004415.]: # CpG-site: CpG#9 Cubic: Using a = 7.72300426487093e-05 , b = -0.00542281173641174 , c = 0.67899229597563 , d = 2.72413468013467 - [20250519_003653.]: Creating plot No. 10 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_rowmeans.png - [20250519_003653.]: # CpG-site: row_means + [20250519_004417.]: Creating plot No. 10 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_rowmeans.png + [20250519_004417.]: # CpG-site: row_means Hyperbolic: Using bias_weight = -1525.75758254501 , a = -127.67501720702 , b = -1525.75758254501 , d = -263.003030149428 - [20250519_003653.]: # CpG-site: row_means + [20250519_004417.]: # CpG-site: row_means Cubic: Using a = 2.88923726150392e-05 , b = -0.0006299592752926 , c = 0.600117857944524 , d = 5.17789562289563 - [20250519_003654.]: Entered 'solving_equations'-Function - [20250519_003654.]: Solving hyperbolic regression for CpG#1 + [20250519_004418.]: Entered 'solving_equations'-Function + [20250519_004418.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: -2.23222990163966 - [20250519_003654.]: Samplename: 0 + [20250519_004418.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. @@ -12513,126 +12510,126 @@ Root: -2.232 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.1698489850618 - [20250519_003654.]: Samplename: 12.5 + [20250519_004418.]: Samplename: 12.5 Root: 12.17 --> Root in between the borders! Added to results. Hyperbolic solved: 24.4781920312644 - [20250519_003654.]: Samplename: 25 + [20250519_004418.]: Samplename: 25 Root: 24.478 --> Root in between the borders! Added to results. Hyperbolic solved: 38.173044740918 - [20250519_003654.]: Samplename: 37.5 + [20250519_004418.]: Samplename: 37.5 Root: 38.173 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3349371964438 - [20250519_003654.]: Samplename: 50 + [20250519_004418.]: Samplename: 50 Root: 52.335 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4582773627666 - [20250519_003654.]: Samplename: 62.5 + [20250519_004418.]: Samplename: 62.5 Root: 65.458 --> Root in between the borders! Added to results. Hyperbolic solved: 75.0090795260796 - [20250519_003654.]: Samplename: 75 + [20250519_004418.]: Samplename: 75 Root: 75.009 --> Root in between the borders! Added to results. Hyperbolic solved: 81.5271920968417 - [20250519_003654.]: Samplename: 87.5 + [20250519_004418.]: Samplename: 87.5 Root: 81.527 --> Root in between the borders! Added to results. Hyperbolic solved: 102.400893095062 - [20250519_003654.]: Samplename: 100 + [20250519_004418.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.401 --> '100 < root < 110' --> substitute 100 - [20250519_003654.]: Solving hyperbolic regression for CpG#2 + [20250519_004418.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 1.13660501904968 - [20250519_003654.]: Samplename: 0 + [20250519_004418.]: Samplename: 0 Root: 1.137 --> Root in between the borders! Added to results. Hyperbolic solved: 11.4129696733689 - [20250519_003654.]: Samplename: 12.5 + [20250519_004418.]: Samplename: 12.5 Root: 11.413 --> Root in between the borders! Added to results. Hyperbolic solved: 26.174000526428 - [20250519_003654.]: Samplename: 25 + [20250519_004418.]: Samplename: 25 Root: 26.174 --> Root in between the borders! Added to results. Hyperbolic solved: 35.1050449117028 - [20250519_003655.]: Samplename: 37.5 + [20250519_004418.]: Samplename: 37.5 Root: 35.105 --> Root in between the borders! Added to results. Hyperbolic solved: 47.685500330611 - [20250519_003655.]: Samplename: 50 + [20250519_004418.]: Samplename: 50 Root: 47.686 --> Root in between the borders! Added to results. Hyperbolic solved: 67.1440494417104 - [20250519_003655.]: Samplename: 62.5 + [20250519_004418.]: Samplename: 62.5 Root: 67.144 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7644668894086 - [20250519_003655.]: Samplename: 75 + [20250519_004418.]: Samplename: 75 Root: 75.764 --> Root in between the borders! Added to results. Hyperbolic solved: 84.4054158616395 - [20250519_003655.]: Samplename: 87.5 + [20250519_004418.]: Samplename: 87.5 Root: 84.405 --> Root in between the borders! Added to results. Hyperbolic solved: 100.94827248399 - [20250519_003655.]: Samplename: 100 + [20250519_004418.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.948 --> '100 < root < 110' --> substitute 100 - [20250519_003655.]: Solving hyperbolic regression for CpG#3 + [20250519_004418.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 0.51235653688495 - [20250519_003655.]: Samplename: 0 + [20250519_004418.]: Samplename: 0 Root: 0.512 --> Root in between the borders! Added to results. Hyperbolic solved: 10.7523884294604 - [20250519_003655.]: Samplename: 12.5 + [20250519_004418.]: Samplename: 12.5 Root: 10.752 --> Root in between the borders! Added to results. Hyperbolic solved: 25.5218907947761 - [20250519_003655.]: Samplename: 25 + [20250519_004418.]: Samplename: 25 Root: 25.522 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5270462675211 - [20250519_003655.]: Samplename: 37.5 + [20250519_004418.]: Samplename: 37.5 Root: 36.527 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7909245028224 - [20250519_003655.]: Samplename: 50 + [20250519_004418.]: Samplename: 50 Root: 50.791 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8686317550184 - [20250519_003655.]: Samplename: 62.5 + [20250519_004418.]: Samplename: 62.5 Root: 64.869 --> Root in between the borders! Added to results. Hyperbolic solved: 77.5524188495235 - [20250519_003655.]: Samplename: 75 + [20250519_004418.]: Samplename: 75 Root: 77.552 --> Root in between the borders! Added to results. Hyperbolic solved: 80.4374617358174 - [20250519_003655.]: Samplename: 87.5 + [20250519_004418.]: Samplename: 87.5 Root: 80.437 --> Root in between the borders! Added to results. Hyperbolic solved: 102.704024900825 - [20250519_003655.]: Samplename: 100 + [20250519_004418.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.704 --> '100 < root < 110' --> substitute 100 - [20250519_003655.]: Solving hyperbolic regression for CpG#4 + [20250519_004418.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: -0.519503092357606 - [20250519_003655.]: Samplename: 0 + [20250519_004418.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. @@ -12640,126 +12637,126 @@ Root: -0.52 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.4934147844872 - [20250519_003655.]: Samplename: 12.5 + [20250519_004418.]: Samplename: 12.5 Root: 12.493 --> Root in between the borders! Added to results. Hyperbolic solved: 24.2685420024115 - [20250519_003655.]: Samplename: 25 + [20250519_004418.]: Samplename: 25 Root: 24.269 --> Root in between the borders! Added to results. Hyperbolic solved: 38.0817128465023 - [20250519_003655.]: Samplename: 37.5 + [20250519_004418.]: Samplename: 37.5 Root: 38.082 --> Root in between the borders! Added to results. Hyperbolic solved: 48.5843181174811 - [20250519_003655.]: Samplename: 50 + [20250519_004418.]: Samplename: 50 Root: 48.584 --> Root in between the borders! Added to results. Hyperbolic solved: 67.6722399183037 - [20250519_003655.]: Samplename: 62.5 + [20250519_004418.]: Samplename: 62.5 Root: 67.672 --> Root in between the borders! Added to results. Hyperbolic solved: 74.1549277799119 - [20250519_003655.]: Samplename: 75 + [20250519_004418.]: Samplename: 75 Root: 74.155 --> Root in between the borders! Added to results. Hyperbolic solved: 82.8821797890026 - [20250519_003655.]: Samplename: 87.5 + [20250519_004418.]: Samplename: 87.5 Root: 82.882 --> Root in between the borders! Added to results. Hyperbolic solved: 102.0791269023 - [20250519_003655.]: Samplename: 100 + [20250519_004418.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.079 --> '100 < root < 110' --> substitute 100 - [20250519_003655.]: Solving hyperbolic regression for CpG#5 + [20250519_004418.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 2.41558626275183 - [20250519_003655.]: Samplename: 0 + [20250519_004418.]: Samplename: 0 Root: 2.416 --> Root in between the borders! Added to results. Hyperbolic solved: 10.1649674907454 - [20250519_003655.]: Samplename: 12.5 + [20250519_004418.]: Samplename: 12.5 Root: 10.165 --> Root in between the borders! Added to results. Hyperbolic solved: 23.9830820412762 - [20250519_003655.]: Samplename: 25 + [20250519_004418.]: Samplename: 25 Root: 23.983 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2773619900429 - [20250519_003655.]: Samplename: 37.5 + [20250519_004418.]: Samplename: 37.5 Root: 37.277 --> Root in between the borders! Added to results. Hyperbolic solved: 50.8659386543864 - [20250519_003655.]: Samplename: 50 + [20250519_004418.]: Samplename: 50 Root: 50.866 --> Root in between the borders! Added to results. Hyperbolic solved: 62.4342273571069 - [20250519_003655.]: Samplename: 62.5 + [20250519_004418.]: Samplename: 62.5 Root: 62.434 --> Root in between the borders! Added to results. Hyperbolic solved: 76.3915260534323 - [20250519_003655.]: Samplename: 75 + [20250519_004418.]: Samplename: 75 Root: 76.392 --> Root in between the borders! Added to results. Hyperbolic solved: 86.159788778566 - [20250519_003655.]: Samplename: 87.5 + [20250519_004418.]: Samplename: 87.5 Root: 86.16 --> Root in between the borders! Added to results. Hyperbolic solved: 100.267759893323 - [20250519_003655.]: Samplename: 100 + [20250519_004418.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.268 --> '100 < root < 110' --> substitute 100 - [20250519_003655.]: Solving hyperbolic regression for CpG#6 + [20250519_004418.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 0.138163748613034 - [20250519_003655.]: Samplename: 0 + [20250519_004419.]: Samplename: 0 Root: 0.138 --> Root in between the borders! Added to results. Hyperbolic solved: 11.8635558881981 - [20250519_003655.]: Samplename: 12.5 + [20250519_004419.]: Samplename: 12.5 Root: 11.864 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5107449550797 - [20250519_003655.]: Samplename: 25 + [20250519_004419.]: Samplename: 25 Root: 26.511 --> Root in between the borders! Added to results. Hyperbolic solved: 35.3205073050661 - [20250519_003655.]: Samplename: 37.5 + [20250519_004419.]: Samplename: 37.5 Root: 35.321 --> Root in between the borders! Added to results. Hyperbolic solved: 50.0570767570666 - [20250519_003655.]: Samplename: 50 + [20250519_004419.]: Samplename: 50 Root: 50.057 --> Root in between the borders! Added to results. Hyperbolic solved: 64.9602944381018 - [20250519_003655.]: Samplename: 62.5 + [20250519_004419.]: Samplename: 62.5 Root: 64.96 --> Root in between the borders! Added to results. Hyperbolic solved: 73.66890571617 - [20250519_003655.]: Samplename: 75 + [20250519_004419.]: Samplename: 75 Root: 73.669 --> Root in between the borders! Added to results. Hyperbolic solved: 87.1266086585036 - [20250519_003655.]: Samplename: 87.5 + [20250519_004419.]: Samplename: 87.5 Root: 87.127 --> Root in between the borders! Added to results. Hyperbolic solved: 100.261637014212 - [20250519_003655.]: Samplename: 100 + [20250519_004419.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.262 --> '100 < root < 110' --> substitute 100 - [20250519_003655.]: Solving hyperbolic regression for CpG#7 + [20250519_004419.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: -1.37238087287012 - [20250519_003655.]: Samplename: 0 + [20250519_004419.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. @@ -12767,85 +12764,85 @@ Root: -1.372 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.1993162352498 - [20250519_003655.]: Samplename: 12.5 + [20250519_004419.]: Samplename: 12.5 Root: 10.199 --> Root in between the borders! Added to results. Hyperbolic solved: 24.595178967123 - [20250519_003655.]: Samplename: 25 + [20250519_004419.]: Samplename: 25 Root: 24.595 --> Root in between the borders! Added to results. Hyperbolic solved: 37.8310421041787 - [20250519_003655.]: Samplename: 37.5 + [20250519_004419.]: Samplename: 37.5 Root: 37.831 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5588739724067 - [20250519_003655.]: Samplename: 50 + [20250519_004419.]: Samplename: 50 Root: 53.559 --> Root in between the borders! Added to results. Hyperbolic solved: 65.9364947980258 - [20250519_003655.]: Samplename: 62.5 + [20250519_004419.]: Samplename: 62.5 Root: 65.936 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7361094434913 - [20250519_003655.]: Samplename: 75 + [20250519_004419.]: Samplename: 75 Root: 75.736 --> Root in between the borders! Added to results. Hyperbolic solved: 79.432823759854 - [20250519_003655.]: Samplename: 87.5 + [20250519_004419.]: Samplename: 87.5 Root: 79.433 --> Root in between the borders! Added to results. Hyperbolic solved: 103.004237013737 - [20250519_003655.]: Samplename: 100 + [20250519_004419.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 103.004 --> '100 < root < 110' --> substitute 100 - [20250519_003655.]: Solving hyperbolic regression for CpG#8 + [20250519_004419.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 2.80068218205093 - [20250519_003655.]: Samplename: 0 + [20250519_004419.]: Samplename: 0 Root: 2.801 --> Root in between the borders! Added to results. Hyperbolic solved: 9.27535134596596 - [20250519_003655.]: Samplename: 12.5 + [20250519_004419.]: Samplename: 12.5 Root: 9.275 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4762621928197 - [20250519_003655.]: Samplename: 25 + [20250519_004419.]: Samplename: 25 Root: 25.476 --> Root in between the borders! Added to results. Hyperbolic solved: 34.0122075735416 - [20250519_003655.]: Samplename: 37.5 + [20250519_004419.]: Samplename: 37.5 Root: 34.012 --> Root in between the borders! Added to results. Hyperbolic solved: 51.7842655662325 - [20250519_003655.]: Samplename: 50 + [20250519_004419.]: Samplename: 50 Root: 51.784 --> Root in between the borders! Added to results. Hyperbolic solved: 64.6732311906145 - [20250519_003655.]: Samplename: 62.5 + [20250519_004419.]: Samplename: 62.5 Root: 64.673 --> Root in between the borders! Added to results. Hyperbolic solved: 78.4326978859189 - [20250519_003655.]: Samplename: 75 + [20250519_004419.]: Samplename: 75 Root: 78.433 --> Root in between the borders! Added to results. Hyperbolic solved: 81.3427232852719 - [20250519_003655.]: Samplename: 87.5 + [20250519_004419.]: Samplename: 87.5 Root: 81.343 --> Root in between the borders! Added to results. Hyperbolic solved: 101.964406640583 - [20250519_003655.]: Samplename: 100 + [20250519_004419.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.964 --> '100 < root < 110' --> substitute 100 - [20250519_003655.]: Solving hyperbolic regression for CpG#9 + [20250519_004419.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: -2.13403721845678 - [20250519_003655.]: Samplename: 0 + [20250519_004419.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. @@ -12853,85 +12850,85 @@ Root: -2.134 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.5082192457956 - [20250519_003655.]: Samplename: 12.5 + [20250519_004419.]: Samplename: 12.5 Root: 10.508 --> Root in between the borders! Added to results. Hyperbolic solved: 26.9164567253388 - [20250519_003655.]: Samplename: 25 + [20250519_004419.]: Samplename: 25 Root: 26.916 --> Root in between the borders! Added to results. Hyperbolic solved: 36.8334779159501 - [20250519_003655.]: Samplename: 37.5 + [20250519_004419.]: Samplename: 37.5 Root: 36.833 --> Root in between the borders! Added to results. Hyperbolic solved: 52.0097895977263 - [20250519_003655.]: Samplename: 50 + [20250519_004419.]: Samplename: 50 Root: 52.01 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8930527921581 - [20250519_003655.]: Samplename: 62.5 + [20250519_004419.]: Samplename: 62.5 Root: 64.893 --> Root in between the borders! Added to results. Hyperbolic solved: 74.5671055499357 - [20250519_003655.]: Samplename: 75 + [20250519_004419.]: Samplename: 75 Root: 74.567 --> Root in between the borders! Added to results. Hyperbolic solved: 84.5294954832669 - [20250519_003655.]: Samplename: 87.5 + [20250519_004419.]: Samplename: 87.5 Root: 84.529 --> Root in between the borders! Added to results. Hyperbolic solved: 101.047146466811 - [20250519_003655.]: Samplename: 100 + [20250519_004419.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.047 --> '100 < root < 110' --> substitute 100 - [20250519_003655.]: Solving hyperbolic regression for row_means + [20250519_004419.]: Solving hyperbolic regression for row_means Hyperbolic solved: 0.290940467580009 - [20250519_003655.]: Samplename: 0 + [20250519_004419.]: Samplename: 0 Root: 0.291 --> Root in between the borders! Added to results. Hyperbolic solved: 11.041240609297 - [20250519_003655.]: Samplename: 12.5 + [20250519_004419.]: Samplename: 12.5 Root: 11.041 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4081503210647 - [20250519_003655.]: Samplename: 25 + [20250519_004419.]: Samplename: 25 Root: 25.408 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5243723188613 - [20250519_003655.]: Samplename: 37.5 + [20250519_004419.]: Samplename: 37.5 Root: 36.524 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7348829528732 - [20250519_003655.]: Samplename: 50 + [20250519_004419.]: Samplename: 50 Root: 50.735 --> Root in between the borders! Added to results. Hyperbolic solved: 65.3135214250794 - [20250519_003655.]: Samplename: 62.5 + [20250519_004419.]: Samplename: 62.5 Root: 65.314 --> Root in between the borders! Added to results. Hyperbolic solved: 75.5342711952113 - [20250519_003655.]: Samplename: 75 + [20250519_004419.]: Samplename: 75 Root: 75.534 --> Root in between the borders! Added to results. Hyperbolic solved: 83.2411229564887 - [20250519_003655.]: Samplename: 87.5 + [20250519_004419.]: Samplename: 87.5 Root: 83.241 --> Root in between the borders! Added to results. Hyperbolic solved: 101.666942268285 - [20250519_003655.]: Samplename: 100 + [20250519_004419.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.667 --> '100 < root < 110' --> substitute 100 - [20250519_003655.]: + [20250519_004419.]: ### Starting with regression calculations ### - [20250519_003655.]: Entered 'regression_type1'-Function + [20250519_004419.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : @@ -13416,561 +13413,561 @@ singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient - [20250519_003655.]: # CpG-site: CpG#1 + [20250519_004419.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.1698489850618, 24.4781920312644, 38.173044740918, 52.3349371964438, 65.4582773627666, 75.0090795260796, 81.5271920968417, 100)c(0, 0.330151014938201, 0.521807968735601, 0.673044740918002, 2.3349371964438, 2.95827736276659, 0.00907952607960283, 5.9728079031583, 0)c(NA, 2.64120811950561, 2.0872318749424, 1.79478597578134, 4.66987439288761, 4.73324378042655, 0.0121060347728038, 6.82606617503806, 0) - [20250519_003655.]: Logging df_agg: CpG#1 - [20250519_003655.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003655.]: c(0, 12.1698489850618, 24.4781920312644, 38.173044740918, 52.3349371964438, 65.4582773627666, 75.0090795260796, 81.5271920968417, 100)[20250519_003655.]: c(0, 0.330151014938201, 0.521807968735601, 0.673044740918002, 2.3349371964438, 2.95827736276659, 0.00907952607960283, 5.9728079031583, 0)[20250519_003655.]: c(NA, 2.64120811950561, 2.0872318749424, 1.79478597578134, 4.66987439288761, 4.73324378042655, 0.0121060347728038, 6.82606617503806, 0) - [20250519_003655.]: Entered 'hyperbolic_regression'-Function - [20250519_003655.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003655.]: Entered 'cubic_regression'-Function - [20250519_003655.]: 'cubic_regression': minmax = FALSE - [20250519_003655.]: # CpG-site: CpG#2 + [20250519_004419.]: Logging df_agg: CpG#1 + [20250519_004419.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004419.]: c(0, 12.1698489850618, 24.4781920312644, 38.173044740918, 52.3349371964438, 65.4582773627666, 75.0090795260796, 81.5271920968417, 100)[20250519_004419.]: c(0, 0.330151014938201, 0.521807968735601, 0.673044740918002, 2.3349371964438, 2.95827736276659, 0.00907952607960283, 5.9728079031583, 0)[20250519_004419.]: c(NA, 2.64120811950561, 2.0872318749424, 1.79478597578134, 4.66987439288761, 4.73324378042655, 0.0121060347728038, 6.82606617503806, 0) + [20250519_004419.]: Entered 'hyperbolic_regression'-Function + [20250519_004419.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004419.]: Entered 'cubic_regression'-Function + [20250519_004419.]: 'cubic_regression': minmax = FALSE + [20250519_004419.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.13660501904968, 11.4129696733689, 26.174000526428, 35.1050449117028, 47.685500330611, 67.1440494417104, 75.7644668894086, 84.4054158616395, 100)c(1.13660501904968, 1.0870303266311, 1.174000526428, 2.3949550882972, 2.314499669389, 4.64404944171039, 0.764466889408595, 3.0945841383605, 0)c(NA, 8.6962426130488, 4.696002105712, 6.38654690212587, 4.628999338778, 7.43047910673663, 1.01928918587813, 3.53666758669772, 0) - [20250519_003655.]: Logging df_agg: CpG#2 - [20250519_003655.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003655.]: c(1.13660501904968, 11.4129696733689, 26.174000526428, 35.1050449117028, 47.685500330611, 67.1440494417104, 75.7644668894086, 84.4054158616395, 100)[20250519_003655.]: c(1.13660501904968, 1.0870303266311, 1.174000526428, 2.3949550882972, 2.314499669389, 4.64404944171039, 0.764466889408595, 3.0945841383605, 0)[20250519_003655.]: c(NA, 8.6962426130488, 4.696002105712, 6.38654690212587, 4.628999338778, 7.43047910673663, 1.01928918587813, 3.53666758669772, 0) - [20250519_003655.]: Entered 'hyperbolic_regression'-Function - [20250519_003655.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003656.]: Entered 'cubic_regression'-Function - [20250519_003656.]: 'cubic_regression': minmax = FALSE - [20250519_003656.]: # CpG-site: CpG#3 + [20250519_004419.]: Logging df_agg: CpG#2 + [20250519_004419.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004419.]: c(1.13660501904968, 11.4129696733689, 26.174000526428, 35.1050449117028, 47.685500330611, 67.1440494417104, 75.7644668894086, 84.4054158616395, 100)[20250519_004419.]: c(1.13660501904968, 1.0870303266311, 1.174000526428, 2.3949550882972, 2.314499669389, 4.64404944171039, 0.764466889408595, 3.0945841383605, 0)[20250519_004419.]: c(NA, 8.6962426130488, 4.696002105712, 6.38654690212587, 4.628999338778, 7.43047910673663, 1.01928918587813, 3.53666758669772, 0) + [20250519_004419.]: Entered 'hyperbolic_regression'-Function + [20250519_004419.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004419.]: Entered 'cubic_regression'-Function + [20250519_004419.]: 'cubic_regression': minmax = FALSE + [20250519_004419.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.51235653688495, 10.7523884294604, 25.5218907947761, 36.5270462675211, 50.7909245028224, 64.8686317550184, 77.5524188495235, 80.4374617358174, 100)c(0.51235653688495, 1.7476115705396, 0.521890794776098, 0.972953732478899, 0.790924502822399, 2.3686317550184, 2.55241884952351, 7.06253826418261, 0)c(NA, 13.9808925643168, 2.08756317910439, 2.5945432866104, 1.5818490056448, 3.78981080802944, 3.40322513269801, 8.07147230192298, 0) - [20250519_003656.]: Logging df_agg: CpG#3 - [20250519_003656.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003656.]: c(0.51235653688495, 10.7523884294604, 25.5218907947761, 36.5270462675211, 50.7909245028224, 64.8686317550184, 77.5524188495235, 80.4374617358174, 100)[20250519_003656.]: c(0.51235653688495, 1.7476115705396, 0.521890794776098, 0.972953732478899, 0.790924502822399, 2.3686317550184, 2.55241884952351, 7.06253826418261, 0)[20250519_003656.]: c(NA, 13.9808925643168, 2.08756317910439, 2.5945432866104, 1.5818490056448, 3.78981080802944, 3.40322513269801, 8.07147230192298, 0) - [20250519_003656.]: Entered 'hyperbolic_regression'-Function - [20250519_003656.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003656.]: Entered 'cubic_regression'-Function - [20250519_003656.]: 'cubic_regression': minmax = FALSE - [20250519_003656.]: # CpG-site: CpG#4 + [20250519_004419.]: Logging df_agg: CpG#3 + [20250519_004419.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004419.]: c(0.51235653688495, 10.7523884294604, 25.5218907947761, 36.5270462675211, 50.7909245028224, 64.8686317550184, 77.5524188495235, 80.4374617358174, 100)[20250519_004419.]: c(0.51235653688495, 1.7476115705396, 0.521890794776098, 0.972953732478899, 0.790924502822399, 2.3686317550184, 2.55241884952351, 7.06253826418261, 0)[20250519_004419.]: c(NA, 13.9808925643168, 2.08756317910439, 2.5945432866104, 1.5818490056448, 3.78981080802944, 3.40322513269801, 8.07147230192298, 0) + [20250519_004419.]: Entered 'hyperbolic_regression'-Function + [20250519_004419.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004420.]: Entered 'cubic_regression'-Function + [20250519_004420.]: 'cubic_regression': minmax = FALSE + [20250519_004420.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.4934147844872, 24.2685420024115, 38.0817128465023, 48.5843181174811, 67.6722399183037, 74.1549277799119, 82.8821797890026, 100)c(0, 0.00658521551279989, 0.7314579975885, 0.581712846502299, 1.4156818825189, 5.1722399183037, 0.845072220088099, 4.61782021099739, 0)c(NA, 0.0526817241023991, 2.925831990354, 1.55123425733946, 2.83136376503781, 8.27558386928592, 1.12676296011747, 5.27750881256845, 0) - [20250519_003656.]: Logging df_agg: CpG#4 - [20250519_003656.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003656.]: c(0, 12.4934147844872, 24.2685420024115, 38.0817128465023, 48.5843181174811, 67.6722399183037, 74.1549277799119, 82.8821797890026, 100)[20250519_003656.]: c(0, 0.00658521551279989, 0.7314579975885, 0.581712846502299, 1.4156818825189, 5.1722399183037, 0.845072220088099, 4.61782021099739, 0)[20250519_003656.]: c(NA, 0.0526817241023991, 2.925831990354, 1.55123425733946, 2.83136376503781, 8.27558386928592, 1.12676296011747, 5.27750881256845, 0) - [20250519_003656.]: Entered 'hyperbolic_regression'-Function - [20250519_003656.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003656.]: Entered 'cubic_regression'-Function - [20250519_003656.]: 'cubic_regression': minmax = FALSE - [20250519_003656.]: # CpG-site: CpG#5 + [20250519_004420.]: Logging df_agg: CpG#4 + [20250519_004420.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004420.]: c(0, 12.4934147844872, 24.2685420024115, 38.0817128465023, 48.5843181174811, 67.6722399183037, 74.1549277799119, 82.8821797890026, 100)[20250519_004420.]: c(0, 0.00658521551279989, 0.7314579975885, 0.581712846502299, 1.4156818825189, 5.1722399183037, 0.845072220088099, 4.61782021099739, 0)[20250519_004420.]: c(NA, 0.0526817241023991, 2.925831990354, 1.55123425733946, 2.83136376503781, 8.27558386928592, 1.12676296011747, 5.27750881256845, 0) + [20250519_004420.]: Entered 'hyperbolic_regression'-Function + [20250519_004420.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004420.]: Entered 'cubic_regression'-Function + [20250519_004420.]: 'cubic_regression': minmax = FALSE + [20250519_004420.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.41558626275183, 10.1649674907454, 23.9830820412762, 37.2773619900429, 50.8659386543864, 62.4342273571069, 76.3915260534323, 86.159788778566, 100)c(2.41558626275183, 2.3350325092546, 1.0169179587238, 0.222638009957102, 0.865938654386397, 0.0657726428931014, 1.3915260534323, 1.340211221434, 0)c(NA, 18.6802600740368, 4.06767183489519, 0.593701359885604, 1.73187730877279, 0.105236228628962, 1.85536807124307, 1.53166996735315, 0) - [20250519_003656.]: Logging df_agg: CpG#5 - [20250519_003656.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003656.]: c(2.41558626275183, 10.1649674907454, 23.9830820412762, 37.2773619900429, 50.8659386543864, 62.4342273571069, 76.3915260534323, 86.159788778566, 100)[20250519_003656.]: c(2.41558626275183, 2.3350325092546, 1.0169179587238, 0.222638009957102, 0.865938654386397, 0.0657726428931014, 1.3915260534323, 1.340211221434, 0)[20250519_003656.]: c(NA, 18.6802600740368, 4.06767183489519, 0.593701359885604, 1.73187730877279, 0.105236228628962, 1.85536807124307, 1.53166996735315, 0) - [20250519_003656.]: Entered 'hyperbolic_regression'-Function - [20250519_003656.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003656.]: Entered 'cubic_regression'-Function - [20250519_003656.]: 'cubic_regression': minmax = FALSE - [20250519_003656.]: # CpG-site: CpG#6 + [20250519_004420.]: Logging df_agg: CpG#5 + [20250519_004420.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004420.]: c(2.41558626275183, 10.1649674907454, 23.9830820412762, 37.2773619900429, 50.8659386543864, 62.4342273571069, 76.3915260534323, 86.159788778566, 100)[20250519_004420.]: c(2.41558626275183, 2.3350325092546, 1.0169179587238, 0.222638009957102, 0.865938654386397, 0.0657726428931014, 1.3915260534323, 1.340211221434, 0)[20250519_004420.]: c(NA, 18.6802600740368, 4.06767183489519, 0.593701359885604, 1.73187730877279, 0.105236228628962, 1.85536807124307, 1.53166996735315, 0) + [20250519_004420.]: Entered 'hyperbolic_regression'-Function + [20250519_004420.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004420.]: Entered 'cubic_regression'-Function + [20250519_004420.]: 'cubic_regression': minmax = FALSE + [20250519_004420.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.138163748613034, 11.8635558881981, 26.5107449550797, 35.3205073050661, 50.0570767570666, 64.9602944381018, 73.66890571617, 87.1266086585036, 100)c(0.138163748613034, 0.6364441118019, 1.5107449550797, 2.1794926949339, 0.0570767570666035, 2.4602944381018, 1.33109428383, 0.373391341496401, 0)c(NA, 5.0915528944152, 6.04297982031881, 5.81198051982373, 0.114153514133207, 3.93647110096288, 1.77479237844, 0.426732961710172, 0) - [20250519_003656.]: Logging df_agg: CpG#6 - [20250519_003656.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003656.]: c(0.138163748613034, 11.8635558881981, 26.5107449550797, 35.3205073050661, 50.0570767570666, 64.9602944381018, 73.66890571617, 87.1266086585036, 100)[20250519_003656.]: c(0.138163748613034, 0.6364441118019, 1.5107449550797, 2.1794926949339, 0.0570767570666035, 2.4602944381018, 1.33109428383, 0.373391341496401, 0)[20250519_003656.]: c(NA, 5.0915528944152, 6.04297982031881, 5.81198051982373, 0.114153514133207, 3.93647110096288, 1.77479237844, 0.426732961710172, 0) - [20250519_003656.]: Entered 'hyperbolic_regression'-Function - [20250519_003656.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003657.]: Entered 'cubic_regression'-Function - [20250519_003657.]: 'cubic_regression': minmax = FALSE - [20250519_003657.]: # CpG-site: CpG#7 + [20250519_004420.]: Logging df_agg: CpG#6 + [20250519_004420.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004420.]: c(0.138163748613034, 11.8635558881981, 26.5107449550797, 35.3205073050661, 50.0570767570666, 64.9602944381018, 73.66890571617, 87.1266086585036, 100)[20250519_004420.]: c(0.138163748613034, 0.6364441118019, 1.5107449550797, 2.1794926949339, 0.0570767570666035, 2.4602944381018, 1.33109428383, 0.373391341496401, 0)[20250519_004420.]: c(NA, 5.0915528944152, 6.04297982031881, 5.81198051982373, 0.114153514133207, 3.93647110096288, 1.77479237844, 0.426732961710172, 0) + [20250519_004420.]: Entered 'hyperbolic_regression'-Function + [20250519_004420.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004420.]: Entered 'cubic_regression'-Function + [20250519_004420.]: 'cubic_regression': minmax = FALSE + [20250519_004420.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.1993162352498, 24.595178967123, 37.8310421041787, 53.5588739724067, 65.9364947980258, 75.7361094434913, 79.432823759854, 100)c(0, 2.3006837647502, 0.404821032876999, 0.3310421041787, 3.5588739724067, 3.43649479802581, 0.736109443491301, 8.067176240146, 0)c(NA, 18.4054701180016, 1.619284131508, 0.882778944476532, 7.1177479448134, 5.49839167684129, 0.981479257988402, 9.21962998873828, 0) - [20250519_003657.]: Logging df_agg: CpG#7 - [20250519_003657.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003657.]: c(0, 10.1993162352498, 24.595178967123, 37.8310421041787, 53.5588739724067, 65.9364947980258, 75.7361094434913, 79.432823759854, 100)[20250519_003657.]: c(0, 2.3006837647502, 0.404821032876999, 0.3310421041787, 3.5588739724067, 3.43649479802581, 0.736109443491301, 8.067176240146, 0)[20250519_003657.]: c(NA, 18.4054701180016, 1.619284131508, 0.882778944476532, 7.1177479448134, 5.49839167684129, 0.981479257988402, 9.21962998873828, 0) - [20250519_003657.]: Entered 'hyperbolic_regression'-Function - [20250519_003657.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003657.]: Entered 'cubic_regression'-Function - [20250519_003657.]: 'cubic_regression': minmax = FALSE - [20250519_003657.]: # CpG-site: CpG#8 + [20250519_004420.]: Logging df_agg: CpG#7 + [20250519_004420.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004420.]: c(0, 10.1993162352498, 24.595178967123, 37.8310421041787, 53.5588739724067, 65.9364947980258, 75.7361094434913, 79.432823759854, 100)[20250519_004420.]: c(0, 2.3006837647502, 0.404821032876999, 0.3310421041787, 3.5588739724067, 3.43649479802581, 0.736109443491301, 8.067176240146, 0)[20250519_004420.]: c(NA, 18.4054701180016, 1.619284131508, 0.882778944476532, 7.1177479448134, 5.49839167684129, 0.981479257988402, 9.21962998873828, 0) + [20250519_004420.]: Entered 'hyperbolic_regression'-Function + [20250519_004420.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004421.]: Entered 'cubic_regression'-Function + [20250519_004421.]: 'cubic_regression': minmax = FALSE + [20250519_004421.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.80068218205093, 9.27535134596596, 25.4762621928197, 34.0122075735416, 51.7842655662325, 64.6732311906145, 78.4326978859189, 81.3427232852719, 100)c(2.80068218205093, 3.22464865403404, 0.4762621928197, 3.4877924264584, 1.7842655662325, 2.1732311906145, 3.4326978859189, 6.1572767147281, 0)c(NA, 25.7971892322723, 1.9050487712788, 9.30077980388906, 3.568531132465, 3.4771699049832, 4.57693051455853, 7.03688767397497, 0) - [20250519_003657.]: Logging df_agg: CpG#8 - [20250519_003657.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003657.]: c(2.80068218205093, 9.27535134596596, 25.4762621928197, 34.0122075735416, 51.7842655662325, 64.6732311906145, 78.4326978859189, 81.3427232852719, 100)[20250519_003657.]: c(2.80068218205093, 3.22464865403404, 0.4762621928197, 3.4877924264584, 1.7842655662325, 2.1732311906145, 3.4326978859189, 6.1572767147281, 0)[20250519_003657.]: c(NA, 25.7971892322723, 1.9050487712788, 9.30077980388906, 3.568531132465, 3.4771699049832, 4.57693051455853, 7.03688767397497, 0) - [20250519_003657.]: Entered 'hyperbolic_regression'-Function - [20250519_003657.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003657.]: Entered 'cubic_regression'-Function - [20250519_003657.]: 'cubic_regression': minmax = FALSE - [20250519_003657.]: # CpG-site: CpG#9 + [20250519_004421.]: Logging df_agg: CpG#8 + [20250519_004421.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004421.]: c(2.80068218205093, 9.27535134596596, 25.4762621928197, 34.0122075735416, 51.7842655662325, 64.6732311906145, 78.4326978859189, 81.3427232852719, 100)[20250519_004421.]: c(2.80068218205093, 3.22464865403404, 0.4762621928197, 3.4877924264584, 1.7842655662325, 2.1732311906145, 3.4326978859189, 6.1572767147281, 0)[20250519_004421.]: c(NA, 25.7971892322723, 1.9050487712788, 9.30077980388906, 3.568531132465, 3.4771699049832, 4.57693051455853, 7.03688767397497, 0) + [20250519_004421.]: Entered 'hyperbolic_regression'-Function + [20250519_004421.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004421.]: Entered 'cubic_regression'-Function + [20250519_004421.]: 'cubic_regression': minmax = FALSE + [20250519_004421.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.5082192457956, 26.9164567253388, 36.8334779159501, 52.0097895977263, 64.8930527921581, 74.5671055499357, 84.5294954832669, 100)c(0, 1.9917807542044, 1.9164567253388, 0.666522084049902, 2.0097895977263, 2.3930527921581, 0.432894450064296, 2.9705045167331, 0)c(NA, 15.9342460336352, 7.6658269013552, 1.77739222413307, 4.01957919545259, 3.82888446745296, 0.577192600085728, 3.39486230483783, 0) - [20250519_003657.]: Logging df_agg: CpG#9 - [20250519_003657.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003657.]: c(0, 10.5082192457956, 26.9164567253388, 36.8334779159501, 52.0097895977263, 64.8930527921581, 74.5671055499357, 84.5294954832669, 100)[20250519_003657.]: c(0, 1.9917807542044, 1.9164567253388, 0.666522084049902, 2.0097895977263, 2.3930527921581, 0.432894450064296, 2.9705045167331, 0)[20250519_003657.]: c(NA, 15.9342460336352, 7.6658269013552, 1.77739222413307, 4.01957919545259, 3.82888446745296, 0.577192600085728, 3.39486230483783, 0) - [20250519_003657.]: Entered 'hyperbolic_regression'-Function - [20250519_003657.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003657.]: Entered 'cubic_regression'-Function - [20250519_003657.]: 'cubic_regression': minmax = FALSE - [20250519_003657.]: # CpG-site: row_means + [20250519_004421.]: Logging df_agg: CpG#9 + [20250519_004421.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004421.]: c(0, 10.5082192457956, 26.9164567253388, 36.8334779159501, 52.0097895977263, 64.8930527921581, 74.5671055499357, 84.5294954832669, 100)[20250519_004421.]: c(0, 1.9917807542044, 1.9164567253388, 0.666522084049902, 2.0097895977263, 2.3930527921581, 0.432894450064296, 2.9705045167331, 0)[20250519_004421.]: c(NA, 15.9342460336352, 7.6658269013552, 1.77739222413307, 4.01957919545259, 3.82888446745296, 0.577192600085728, 3.39486230483783, 0) + [20250519_004421.]: Entered 'hyperbolic_regression'-Function + [20250519_004421.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004421.]: Entered 'cubic_regression'-Function + [20250519_004421.]: 'cubic_regression': minmax = FALSE + [20250519_004421.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.290940467580009, 11.041240609297, 25.4081503210647, 36.5243723188613, 50.7348829528732, 65.3135214250794, 75.5342711952113, 83.2411229564887, 100)c(0.290940467580009, 1.458759390703, 0.408150321064699, 0.975627681138697, 0.7348829528732, 2.8135214250794, 0.534271195211304, 4.2588770435113, 0)c(NA, 11.670075125624, 1.6326012842588, 2.60167381636986, 1.4697659057464, 4.50163428012704, 0.712361593615071, 4.8672880497272, 0) - [20250519_003657.]: Logging df_agg: row_means - [20250519_003657.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003657.]: c(0.290940467580009, 11.041240609297, 25.4081503210647, 36.5243723188613, 50.7348829528732, 65.3135214250794, 75.5342711952113, 83.2411229564887, 100)[20250519_003657.]: c(0.290940467580009, 1.458759390703, 0.408150321064699, 0.975627681138697, 0.7348829528732, 2.8135214250794, 0.534271195211304, 4.2588770435113, 0)[20250519_003657.]: c(NA, 11.670075125624, 1.6326012842588, 2.60167381636986, 1.4697659057464, 4.50163428012704, 0.712361593615071, 4.8672880497272, 0) - [20250519_003657.]: Entered 'hyperbolic_regression'-Function - [20250519_003657.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003658.]: Entered 'cubic_regression'-Function - [20250519_003658.]: 'cubic_regression': minmax = FALSE - [20250519_003658.]: ### Starting with plotting ### - [20250519_003658.]: Creating BiasCorrected (hyperbolic) plot No. 1 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG1_corrected_h.png - [20250519_003658.]: # CpG-site: CpG#1 + [20250519_004421.]: Logging df_agg: row_means + [20250519_004421.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004421.]: c(0.290940467580009, 11.041240609297, 25.4081503210647, 36.5243723188613, 50.7348829528732, 65.3135214250794, 75.5342711952113, 83.2411229564887, 100)[20250519_004421.]: c(0.290940467580009, 1.458759390703, 0.408150321064699, 0.975627681138697, 0.7348829528732, 2.8135214250794, 0.534271195211304, 4.2588770435113, 0)[20250519_004421.]: c(NA, 11.670075125624, 1.6326012842588, 2.60167381636986, 1.4697659057464, 4.50163428012704, 0.712361593615071, 4.8672880497272, 0) + [20250519_004421.]: Entered 'hyperbolic_regression'-Function + [20250519_004421.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004421.]: Entered 'cubic_regression'-Function + [20250519_004421.]: 'cubic_regression': minmax = FALSE + [20250519_004422.]: ### Starting with plotting ### + [20250519_004422.]: Creating BiasCorrected (hyperbolic) plot No. 1 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG1_corrected_h.png + [20250519_004422.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -657.853491622191 , a = 985.267531706584 , b = -657.853491622191 , d = 903.45414536045 - [20250519_003658.]: # CpG-site: CpG#1 + [20250519_004422.]: # CpG-site: CpG#1 Cubic: Using a = 5.18463232357963e-06 , b = -0.00180592068110451 , c = 1.11404020909943 , d = -0.858600112691325 - [20250519_003700.]: Creating BiasCorrected (hyperbolic) plot No. 2 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG2_corrected_h.png - [20250519_003700.]: # CpG-site: CpG#2 + [20250519_004424.]: Creating BiasCorrected (hyperbolic) plot No. 2 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG2_corrected_h.png + [20250519_004424.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = 3399.55230393578 , a = 30276.0421015763 , b = 3399.55230393578 , d = 30352.4984502595 - [20250519_003700.]: # CpG-site: CpG#2 + [20250519_004424.]: # CpG-site: CpG#2 Cubic: Using a = -2.58097549676629e-05 , b = 0.00383753278164476 , c = 0.8516077608074 , d = 0.957127237506517 - [20250519_003701.]: Creating BiasCorrected (hyperbolic) plot No. 3 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG3_corrected_h.png - [20250519_003701.]: # CpG-site: CpG#3 + [20250519_004425.]: Creating BiasCorrected (hyperbolic) plot No. 3 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG3_corrected_h.png + [20250519_004425.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = -945.757057025882 , a = 1333.61975246108 , b = -945.757057025882 , d = 1252.95856205348 - [20250519_003701.]: # CpG-site: CpG#3 + [20250519_004425.]: # CpG-site: CpG#3 Cubic: Using a = -1.13948611491254e-05 , b = 0.000937180276062016 , c = 0.998630620080636 , d = -0.383337686690576 - [20250519_003703.]: Creating BiasCorrected (hyperbolic) plot No. 4 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG4_corrected_h.png - [20250519_003703.]: # CpG-site: CpG#4 + [20250519_004427.]: Creating BiasCorrected (hyperbolic) plot No. 4 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG4_corrected_h.png + [20250519_004427.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = -754.050736416394 , a = 1637.26786961468 , b = -754.050736416394 , d = 1558.27361713715 - [20250519_003703.]: # CpG-site: CpG#4 + [20250519_004427.]: # CpG-site: CpG#4 Cubic: Using a = -3.25779988054422e-06 , b = -0.000129596030700936 , c = 1.03077246222439 , d = -0.370343704072989 - [20250519_003704.]: Creating BiasCorrected (hyperbolic) plot No. 5 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG5_corrected_h.png - [20250519_003704.]: # CpG-site: CpG#5 + [20250519_004428.]: Creating BiasCorrected (hyperbolic) plot No. 5 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG5_corrected_h.png + [20250519_004428.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = -1917.44420974597 , a = -5621.4534701009 , b = -1917.44420974597 , d = -5734.47300613195 - [20250519_003704.]: # CpG-site: CpG#5 + [20250519_004428.]: # CpG-site: CpG#5 Cubic: Using a = -3.16782834876228e-05 , b = 0.00493527189107616 , c = 0.800194601834967 , d = 1.38653015028503 - [20250519_003706.]: Creating BiasCorrected (hyperbolic) plot No. 6 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG6_corrected_h.png - [20250519_003706.]: # CpG-site: CpG#6 + [20250519_004430.]: Creating BiasCorrected (hyperbolic) plot No. 6 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG6_corrected_h.png + [20250519_004430.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = -1255.67712347228 , a = 13775.5934089325 , b = -1255.67712347228 , d = 13690.4825730065 - [20250519_003706.]: # CpG-site: CpG#6 + [20250519_004430.]: # CpG-site: CpG#6 Cubic: Using a = -3.70201684653088e-06 , b = 0.000482210694203131 , c = 0.985293133118367 , d = 0.0293568698433451 - [20250519_003707.]: Creating BiasCorrected (hyperbolic) plot No. 7 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG7_corrected_h.png - [20250519_003707.]: # CpG-site: CpG#7 + [20250519_004431.]: Creating BiasCorrected (hyperbolic) plot No. 7 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG7_corrected_h.png + [20250519_004431.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -1065.05415478934 , a = 722.083975601617 , b = -1065.05415478934 , d = 634.402060442185 - [20250519_003707.]: # CpG-site: CpG#7 + [20250519_004431.]: # CpG-site: CpG#7 Cubic: Using a = -1.04111972402046e-06 , b = -0.00130150084904922 , c = 1.12399516228677 , d = -1.5985899870939 - [20250519_003709.]: Creating BiasCorrected (hyperbolic) plot No. 8 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG8_corrected_h.png - [20250519_003709.]: # CpG-site: CpG#8 + [20250519_004433.]: Creating BiasCorrected (hyperbolic) plot No. 8 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG8_corrected_h.png + [20250519_004433.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = 68.5784871630731 , a = 4784.90969361316 , b = 68.5784871630731 , d = 4739.12296431021 - [20250519_003709.]: # CpG-site: CpG#8 + [20250519_004433.]: # CpG-site: CpG#8 Cubic: Using a = -4.65730502627556e-05 , b = 0.00676525570440644 , c = 0.747405744075959 , d = 1.52337158754213 - [20250519_003710.]: Creating BiasCorrected (hyperbolic) plot No. 9 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG9_corrected_h.png - [20250519_003710.]: # CpG-site: CpG#9 + [20250519_004434.]: Creating BiasCorrected (hyperbolic) plot No. 9 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG9_corrected_h.png + [20250519_004434.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -1022.70169872734 , a = 1273.73039431947 , b = -1022.70169872734 , d = 1181.86496720377 - [20250519_003710.]: # CpG-site: CpG#9 + [20250519_004434.]: # CpG-site: CpG#9 Cubic: Using a = 4.24790097543708e-06 , b = -0.00144169637811555 , c = 1.09837791472274 , d = -0.97898795630014 - [20250519_003712.]: Creating BiasCorrected (hyperbolic) plot No. 10 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_rowmeans_corrected_h.png - [20250519_003712.]: # CpG-site: row_means + [20250519_004436.]: Creating BiasCorrected (hyperbolic) plot No. 10 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_rowmeans_corrected_h.png + [20250519_004436.]: # CpG-site: row_means Hyperbolic: Using bias_weight = -1166.73858297355 , a = 1740.63784318208 , b = -1166.73858297355 , d = 1653.86071043116 - [20250519_003712.]: # CpG-site: row_means + [20250519_004436.]: # CpG-site: row_means Cubic: Using a = -8.71186803343537e-06 , b = 0.000715553560638339 , c = 1.00248252890793 , d = -0.420554394203132 - [20250519_003713.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG1_corrected_h.png - [20250519_003715.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG2_corrected_h.png - [20250519_003716.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG3_corrected_h.png - [20250519_003717.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG4_corrected_h.png - [20250519_003719.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG5_corrected_h.png - [20250519_003720.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG6_corrected_h.png - [20250519_003721.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG7_corrected_h.png - [20250519_003723.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG8_corrected_h.png - [20250519_003724.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG9_corrected_h.png - [20250519_003725.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_rowmeans_corrected_h.png - [20250519_003727.]: Entered 'solving_equations'-Function - [20250519_003727.]: Solving cubic regression for CpG#1 + [20250519_004437.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG1_corrected_h.png + [20250519_004439.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG2_corrected_h.png + [20250519_004440.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG3_corrected_h.png + [20250519_004441.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG4_corrected_h.png + [20250519_004443.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG5_corrected_h.png + [20250519_004444.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG6_corrected_h.png + [20250519_004445.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG7_corrected_h.png + [20250519_004446.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG8_corrected_h.png + [20250519_004448.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG9_corrected_h.png + [20250519_004449.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_rowmeans_corrected_h.png + [20250519_004450.]: Entered 'solving_equations'-Function + [20250519_004450.]: Solving cubic regression for CpG#1 Coefficients: -1.03617340067344Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_003727.]: Samplename: 0 + [20250519_004450.]: Samplename: 0 Root: 1.334 --> Root in between the borders! Added to results. Coefficients: -8.34150673400678Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_003727.]: Samplename: 12.5 + [20250519_004450.]: Samplename: 12.5 Root: 11.446 --> Root in between the borders! Added to results. Coefficients: -15.3881734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_003727.]: Samplename: 25 + [20250519_004450.]: Samplename: 25 Root: 22.228 --> Root in between the borders! Added to results. Coefficients: -24.2801734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_003727.]: Samplename: 37.5 + [20250519_004450.]: Samplename: 37.5 Root: 36.374 --> Root in between the borders! Added to results. Coefficients: -34.9006734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_003727.]: Samplename: 50 + [20250519_004450.]: Samplename: 50 Root: 52.044 --> Root in between the borders! Added to results. Coefficients: -46.3541734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_003727.]: Samplename: 62.5 + [20250519_004450.]: Samplename: 62.5 Root: 66.144 --> Root in between the borders! Added to results. Coefficients: -55.8931734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_003727.]: Samplename: 75 + [20250519_004450.]: Samplename: 75 Root: 75.864 --> Root in between the borders! Added to results. Coefficients: -63.0981734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_003727.]: Samplename: 87.5 + [20250519_004450.]: Samplename: 87.5 Root: 82.254 --> Root in between the borders! Added to results. Coefficients: -91.0461734006735Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_003727.]: Samplename: 100 + [20250519_004450.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.877 --> '100 < root < 110' --> substitute 100 - [20250519_003727.]: Solving cubic regression for CpG#2 + [20250519_004450.]: Solving cubic regression for CpG#2 Coefficients: -0.283329966329966Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_003727.]: Samplename: 0 + [20250519_004450.]: Samplename: 0 Root: 0.549 --> Root in between the borders! Added to results. Coefficients: -6.33999663299663Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_003727.]: Samplename: 12.5 + [20250519_004450.]: Samplename: 12.5 Root: 11.533 --> Root in between the borders! Added to results. Coefficients: -15.93932996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_003727.]: Samplename: 25 + [20250519_004450.]: Samplename: 25 Root: 26.628 --> Root in between the borders! Added to results. Coefficients: -22.33732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_003727.]: Samplename: 37.5 + [20250519_004450.]: Samplename: 37.5 Root: 35.509 --> Root in between the borders! Added to results. Coefficients: -32.22832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_003727.]: Samplename: 50 + [20250519_004450.]: Samplename: 50 Root: 47.851 --> Root in between the borders! Added to results. Coefficients: -49.96332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_003727.]: Samplename: 62.5 + [20250519_004450.]: Samplename: 62.5 Root: 66.893 --> Root in between the borders! Added to results. Coefficients: -58.96582996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_003727.]: Samplename: 75 + [20250519_004450.]: Samplename: 75 Root: 75.431 --> Root in between the borders! Added to results. Coefficients: -68.8366632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_003727.]: Samplename: 87.5 + [20250519_004450.]: Samplename: 87.5 Root: 84.118 --> Root in between the borders! Added to results. Coefficients: -90.57732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_003727.]: Samplename: 100 + [20250519_004450.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.287 --> '100 < root < 110' --> substitute 100 - [20250519_003727.]: Solving cubic regression for CpG#3 + [20250519_004451.]: Solving cubic regression for CpG#3 Coefficients: -0.90294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_003727.]: Samplename: 0 + [20250519_004451.]: Samplename: 0 Root: 1.441 --> Root in between the borders! Added to results. Coefficients: -6.57294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_003727.]: Samplename: 12.5 + [20250519_004451.]: Samplename: 12.5 Root: 10.568 --> Root in between the borders! Added to results. Coefficients: -15.4289478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_003727.]: Samplename: 25 + [20250519_004451.]: Samplename: 25 Root: 24.796 --> Root in between the borders! Added to results. Coefficients: -22.6129478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_003727.]: Samplename: 37.5 + [20250519_004451.]: Samplename: 37.5 Root: 35.952 --> Root in between the borders! Added to results. Coefficients: -32.7754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_003727.]: Samplename: 50 + [20250519_004451.]: Samplename: 50 Root: 50.684 --> Root in between the borders! Added to results. Coefficients: -43.8889478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_003727.]: Samplename: 62.5 + [20250519_004451.]: Samplename: 62.5 Root: 65.142 --> Root in between the borders! Added to results. Coefficients: -54.9754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_003727.]: Samplename: 75 + [20250519_004451.]: Samplename: 75 Root: 77.905 --> Root in between the borders! Added to results. Coefficients: -57.6562811447812Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_003727.]: Samplename: 87.5 + [20250519_004451.]: Samplename: 87.5 Root: 80.767 --> Root in between the borders! Added to results. Coefficients: -80.6649478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_003727.]: Samplename: 100 + [20250519_004451.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.38 --> '100 < root < 110' --> substitute 100 - [20250519_003727.]: Solving cubic regression for CpG#4 + [20250519_004451.]: Solving cubic regression for CpG#4 Coefficients: -0.597449494949524Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_003727.]: Samplename: 0 + [20250519_004451.]: Samplename: 0 Root: 0.858 --> Root in between the borders! Added to results. Coefficients: -8.25278282828286Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_003727.]: Samplename: 12.5 + [20250519_004451.]: Samplename: 12.5 Root: 12.086 --> Root in between the borders! Added to results. Coefficients: -15.8034494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_003727.]: Samplename: 25 + [20250519_004451.]: Samplename: 25 Root: 23.316 --> Root in between the borders! Added to results. Coefficients: -25.5274494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_003727.]: Samplename: 37.5 + [20250519_004451.]: Samplename: 37.5 Root: 37.383 --> Root in between the borders! Added to results. Coefficients: -33.6369494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_003727.]: Samplename: 50 + [20250519_004451.]: Samplename: 50 Root: 48.353 --> Root in between the borders! Added to results. Coefficients: -50.2554494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_003727.]: Samplename: 62.5 + [20250519_004451.]: Samplename: 62.5 Root: 68.082 --> Root in between the borders! Added to results. Coefficients: -56.5394494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_003727.]: Samplename: 75 + [20250519_004451.]: Samplename: 75 Root: 74.615 --> Root in between the borders! Added to results. Coefficients: -65.5927828282829Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_003727.]: Samplename: 87.5 + [20250519_004451.]: Samplename: 87.5 Root: 83.254 --> Root in between the borders! Added to results. Coefficients: -88.3214494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_003727.]: Samplename: 100 + [20250519_004451.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.715 --> '100 < root < 110' --> substitute 100 - [20250519_003727.]: Solving cubic regression for CpG#5 + [20250519_004451.]: Solving cubic regression for CpG#5 Coefficients: -0.623961279461278Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_003727.]: Samplename: 0 + [20250519_004451.]: Samplename: 0 Root: 1.458 --> Root in between the borders! Added to results. Coefficients: -4.76796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_003727.]: Samplename: 12.5 + [20250519_004451.]: Samplename: 12.5 Root: 10.347 --> Root in between the borders! Added to results. Coefficients: -12.7259612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_003727.]: Samplename: 25 + [20250519_004451.]: Samplename: 25 Root: 24.815 --> Root in between the borders! Added to results. Coefficients: -21.1599612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_003727.]: Samplename: 37.5 + [20250519_004451.]: Samplename: 37.5 Root: 37.902 --> Root in between the borders! Added to results. Coefficients: -30.6954612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_003727.]: Samplename: 50 + [20250519_004451.]: Samplename: 50 Root: 50.977 --> Root in between the borders! Added to results. Coefficients: -39.6579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_003727.]: Samplename: 62.5 + [20250519_004451.]: Samplename: 62.5 Root: 62.126 --> Root in between the borders! Added to results. Coefficients: -51.6829612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_003727.]: Samplename: 75 + [20250519_004451.]: Samplename: 75 Root: 75.852 --> Root in between the borders! Added to results. Coefficients: -61.0146279461279Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_003727.]: Samplename: 87.5 + [20250519_004451.]: Samplename: 87.5 Root: 85.767 --> Root in between the borders! Added to results. Coefficients: -76.0699612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_003727.]: Samplename: 100 + [20250519_004451.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.743 --> '100 < root < 110' --> substitute 100 - [20250519_003727.]: Solving cubic regression for CpG#6 + [20250519_004451.]: Solving cubic regression for CpG#6 Coefficients: -0.196072390572403Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_003727.]: Samplename: 0 + [20250519_004451.]: Samplename: 0 Root: 0.349 --> Root in between the borders! Added to results. Coefficients: -6.73873905723907Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_003727.]: Samplename: 12.5 + [20250519_004451.]: Samplename: 12.5 Root: 11.718 --> Root in between the borders! Added to results. Coefficients: -15.8880723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_003727.]: Samplename: 25 + [20250519_004451.]: Samplename: 25 Root: 26.396 --> Root in between the borders! Added to results. Coefficients: -22.0000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_003727.]: Samplename: 37.5 + [20250519_004451.]: Samplename: 37.5 Root: 35.301 --> Root in between the borders! Added to results. Coefficients: -33.4445723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_003727.]: Samplename: 50 + [20250519_004451.]: Samplename: 50 Root: 50.134 --> Root in between the borders! Added to results. Coefficients: -46.9000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_003727.]: Samplename: 62.5 + [20250519_004451.]: Samplename: 62.5 Root: 64.993 --> Root in between the borders! Added to results. Coefficients: -55.8320723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_003727.]: Samplename: 75 + [20250519_004451.]: Samplename: 75 Root: 73.639 --> Root in between the borders! Added to results. Coefficients: -71.5454057239057Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_003727.]: Samplename: 87.5 + [20250519_004451.]: Samplename: 87.5 Root: 87.043 --> Root in between the borders! Added to results. Coefficients: -89.6560723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_003727.]: Samplename: 100 + [20250519_004451.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.329 --> '100 < root < 110' --> substitute 100 - [20250519_003727.]: Solving cubic regression for CpG#7 + [20250519_004451.]: Solving cubic regression for CpG#7 Coefficients: -1.21495454545456Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_003727.]: Samplename: 0 + [20250519_004451.]: Samplename: 0 Root: 2.13 --> Root in between the borders! Added to results. Coefficients: -5.39562121212123Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_003727.]: Samplename: 12.5 + [20250519_004451.]: Samplename: 12.5 Root: 9.973 --> Root in between the borders! Added to results. Coefficients: -11.2649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_003727.]: Samplename: 25 + [20250519_004451.]: Samplename: 25 Root: 22.206 --> Root in between the borders! Added to results. Coefficients: -17.4509545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_003727.]: Samplename: 37.5 + [20250519_004451.]: Samplename: 37.5 Root: 35.814 --> Root in between the borders! Added to results. Coefficients: -26.0314545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_003727.]: Samplename: 50 + [20250519_004451.]: Samplename: 50 Root: 53.28 --> Root in between the borders! Added to results. Coefficients: -33.9649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_003727.]: Samplename: 62.5 + [20250519_004451.]: Samplename: 62.5 Root: 66.598 --> Root in between the borders! Added to results. Coefficients: -41.1689545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_003727.]: Samplename: 75 + [20250519_004451.]: Samplename: 75 Root: 76.575 --> Root in between the borders! Added to results. Coefficients: -44.1356212121212Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_003727.]: Samplename: 87.5 + [20250519_004451.]: Samplename: 87.5 Root: 80.219 --> Root in between the borders! Added to results. Coefficients: -67.2229545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_003727.]: Samplename: 100 + [20250519_004451.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.506 --> '100 < root < 110' --> substitute 100 - [20250519_003727.]: Solving cubic regression for CpG#8 + [20250519_004451.]: Solving cubic regression for CpG#8 Coefficients: -1.09618518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_003727.]: Samplename: 0 + [20250519_004451.]: Samplename: 0 Root: 2.016 --> Root in between the borders! Added to results. Coefficients: -5.44685185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_003727.]: Samplename: 12.5 + [20250519_004451.]: Samplename: 12.5 Root: 9.458 --> Root in between the borders! Added to results. Coefficients: -16.9301851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_003727.]: Samplename: 25 + [20250519_004451.]: Samplename: 25 Root: 26.35 --> Root in between the borders! Added to results. Coefficients: -23.3501851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_003727.]: Samplename: 37.5 + [20250519_004451.]: Samplename: 37.5 Root: 34.728 --> Root in between the borders! Added to results. Coefficients: -37.6251851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_003727.]: Samplename: 50 + [20250519_004451.]: Samplename: 50 Root: 51.781 --> Root in between the borders! Added to results. Coefficients: -48.8261851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_003727.]: Samplename: 62.5 + [20250519_004451.]: Samplename: 62.5 Root: 64.192 --> Root in between the borders! Added to results. Coefficients: -61.6676851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_003727.]: Samplename: 75 + [20250519_004451.]: Samplename: 75 Root: 77.804 --> Root in between the borders! Added to results. Coefficients: -64.5101851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_003727.]: Samplename: 87.5 + [20250519_004451.]: Samplename: 87.5 Root: 80.758 --> Root in between the borders! Added to results. Coefficients: -86.0601851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_003727.]: Samplename: 100 + [20250519_004451.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.834 --> '100 < root < 110' --> substitute 100 - [20250519_003727.]: Solving cubic regression for CpG#9 + [20250519_004451.]: Solving cubic regression for CpG#9 Coefficients: -0.989865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_003727.]: Samplename: 0 + [20250519_004451.]: Samplename: 0 Root: 1.475 --> Root in between the borders! Added to results. Coefficients: -6.39586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_003727.]: Samplename: 12.5 + [20250519_004451.]: Samplename: 12.5 Root: 10.12 --> Root in between the borders! Added to results. Coefficients: -14.7058653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_003727.]: Samplename: 25 + [20250519_004451.]: Samplename: 25 Root: 24.844 --> Root in between the borders! Added to results. Coefficients: -20.6238653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_003727.]: Samplename: 37.5 + [20250519_004451.]: Samplename: 37.5 Root: 35.327 --> Root in between the borders! Added to results. Coefficients: -31.3958653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_003727.]: Samplename: 50 + [20250519_004451.]: Samplename: 50 Root: 51.855 --> Root in between the borders! Added to results. Coefficients: -42.6858653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_003727.]: Samplename: 62.5 + [20250519_004451.]: Samplename: 62.5 Root: 65.265 --> Root in between the borders! Added to results. Coefficients: -52.9033653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_003727.]: Samplename: 75 + [20250519_004451.]: Samplename: 75 Root: 74.915 --> Root in between the borders! Added to results. Coefficients: -65.492531986532Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_003727.]: Samplename: 87.5 + [20250519_004451.]: Samplename: 87.5 Root: 84.67 --> Root in between the borders! Added to results. Coefficients: -92.9898653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_003727.]: Samplename: 100 + [20250519_004451.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.082 --> '100 < root < 110' --> substitute 100 - [20250519_003727.]: Solving cubic regression for row_means + [20250519_004451.]: Solving cubic regression for row_means Coefficients: -0.771215488215478Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_003727.]: Samplename: 0 + [20250519_004451.]: Samplename: 0 Root: 1.287 --> Root in between the borders! Added to results. Coefficients: -6.47247474747474Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_003727.]: Samplename: 12.5 + [20250519_004451.]: Samplename: 12.5 Root: 10.847 --> Root in between the borders! Added to results. Coefficients: -14.8972154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_003727.]: Samplename: 25 + [20250519_004451.]: Samplename: 25 Root: 24.737 --> Root in between the borders! Added to results. Coefficients: -22.1492154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_003727.]: Samplename: 37.5 + [20250519_004451.]: Samplename: 37.5 Root: 36.02 --> Root in between the borders! Added to results. Coefficients: -32.5259932659933Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_003727.]: Samplename: 50 + [20250519_004451.]: Samplename: 50 Root: 50.639 --> Root in between the borders! Added to results. Coefficients: -44.7218821548821Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_003727.]: Samplename: 62.5 + [20250519_004451.]: Samplename: 62.5 Root: 65.497 --> Root in between the borders! Added to results. Coefficients: -54.4032154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_003727.]: Samplename: 75 + [20250519_004451.]: Samplename: 75 Root: 75.751 --> Root in between the borders! Added to results. Coefficients: -62.4313636363636Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_003727.]: Samplename: 87.5 + [20250519_004451.]: Samplename: 87.5 Root: 83.403 --> Root in between the borders! Added to results. Coefficients: -84.7343265993266Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_003727.]: Samplename: 100 + [20250519_004451.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.573 --> '100 < root < 110' --> substitute 100 - [20250519_003727.]: + [20250519_004451.]: ### Starting with regression calculations ### - [20250519_003727.]: Entered 'regression_type1'-Function + [20250519_004451.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : @@ -14449,592 +14446,592 @@ singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient - [20250519_003727.]: # CpG-site: CpG#1 + [20250519_004451.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) - [20250519_003727.]: Logging df_agg: CpG#1 - [20250519_003727.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003727.]: c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)[20250519_003727.]: c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)[20250519_003727.]: c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) - [20250519_003727.]: Entered 'hyperbolic_regression'-Function - [20250519_003727.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003728.]: Entered 'cubic_regression'-Function - [20250519_003728.]: 'cubic_regression': minmax = FALSE - [20250519_003728.]: # CpG-site: CpG#2 + [20250519_004451.]: Logging df_agg: CpG#1 + [20250519_004451.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004451.]: c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)[20250519_004451.]: c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)[20250519_004451.]: c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) + [20250519_004451.]: Entered 'hyperbolic_regression'-Function + [20250519_004451.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004451.]: Entered 'cubic_regression'-Function + [20250519_004451.]: 'cubic_regression': minmax = FALSE + [20250519_004451.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) - [20250519_003728.]: Logging df_agg: CpG#2 - [20250519_003728.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003728.]: c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)[20250519_003728.]: c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)[20250519_003728.]: c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) - [20250519_003728.]: Entered 'hyperbolic_regression'-Function - [20250519_003728.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003728.]: Entered 'cubic_regression'-Function - [20250519_003728.]: 'cubic_regression': minmax = FALSE - [20250519_003728.]: # CpG-site: CpG#3 + [20250519_004451.]: Logging df_agg: CpG#2 + [20250519_004451.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004451.]: c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)[20250519_004451.]: c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)[20250519_004451.]: c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) + [20250519_004451.]: Entered 'hyperbolic_regression'-Function + [20250519_004451.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004452.]: Entered 'cubic_regression'-Function + [20250519_004452.]: 'cubic_regression': minmax = FALSE + [20250519_004452.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) - [20250519_003728.]: Logging df_agg: CpG#3 - [20250519_003728.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003728.]: c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)[20250519_003728.]: c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)[20250519_003728.]: c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) - [20250519_003728.]: Entered 'hyperbolic_regression'-Function - [20250519_003728.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003728.]: Entered 'cubic_regression'-Function - [20250519_003728.]: 'cubic_regression': minmax = FALSE - [20250519_003728.]: # CpG-site: CpG#4 + [20250519_004452.]: Logging df_agg: CpG#3 + [20250519_004452.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004452.]: c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)[20250519_004452.]: c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)[20250519_004452.]: c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) + [20250519_004452.]: Entered 'hyperbolic_regression'-Function + [20250519_004452.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004452.]: Entered 'cubic_regression'-Function + [20250519_004452.]: 'cubic_regression': minmax = FALSE + [20250519_004452.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) - [20250519_003728.]: Logging df_agg: CpG#4 - [20250519_003728.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003728.]: c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)[20250519_003728.]: c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)[20250519_003728.]: c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) - [20250519_003728.]: Entered 'hyperbolic_regression'-Function - [20250519_003728.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003728.]: Entered 'cubic_regression'-Function - [20250519_003728.]: 'cubic_regression': minmax = FALSE - [20250519_003728.]: # CpG-site: CpG#5 + [20250519_004452.]: Logging df_agg: CpG#4 + [20250519_004452.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004452.]: c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)[20250519_004452.]: c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)[20250519_004452.]: c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) + [20250519_004452.]: Entered 'hyperbolic_regression'-Function + [20250519_004452.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004452.]: Entered 'cubic_regression'-Function + [20250519_004452.]: 'cubic_regression': minmax = FALSE + [20250519_004452.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) - [20250519_003728.]: Logging df_agg: CpG#5 - [20250519_003728.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003728.]: c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)[20250519_003728.]: c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)[20250519_003728.]: c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) - [20250519_003728.]: Entered 'hyperbolic_regression'-Function - [20250519_003728.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003729.]: Entered 'cubic_regression'-Function - [20250519_003729.]: 'cubic_regression': minmax = FALSE - [20250519_003729.]: # CpG-site: CpG#6 + [20250519_004452.]: Logging df_agg: CpG#5 + [20250519_004452.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004452.]: c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)[20250519_004452.]: c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)[20250519_004452.]: c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) + [20250519_004452.]: Entered 'hyperbolic_regression'-Function + [20250519_004452.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004452.]: Entered 'cubic_regression'-Function + [20250519_004452.]: 'cubic_regression': minmax = FALSE + [20250519_004452.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) - [20250519_003729.]: Logging df_agg: CpG#6 - [20250519_003729.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003729.]: c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)[20250519_003729.]: c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)[20250519_003729.]: c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) - [20250519_003729.]: Entered 'hyperbolic_regression'-Function - [20250519_003729.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003729.]: Entered 'cubic_regression'-Function - [20250519_003729.]: 'cubic_regression': minmax = FALSE - [20250519_003729.]: # CpG-site: CpG#7 + [20250519_004452.]: Logging df_agg: CpG#6 + [20250519_004452.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004452.]: c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)[20250519_004452.]: c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)[20250519_004452.]: c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) + [20250519_004452.]: Entered 'hyperbolic_regression'-Function + [20250519_004452.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004453.]: Entered 'cubic_regression'-Function + [20250519_004453.]: 'cubic_regression': minmax = FALSE + [20250519_004453.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) - [20250519_003729.]: Logging df_agg: CpG#7 - [20250519_003729.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003729.]: c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)[20250519_003729.]: c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)[20250519_003729.]: c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) - [20250519_003729.]: Entered 'hyperbolic_regression'-Function - [20250519_003729.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003729.]: Entered 'cubic_regression'-Function - [20250519_003729.]: 'cubic_regression': minmax = FALSE - [20250519_003729.]: # CpG-site: CpG#8 + [20250519_004453.]: Logging df_agg: CpG#7 + [20250519_004453.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004453.]: c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)[20250519_004453.]: c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)[20250519_004453.]: c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) + [20250519_004453.]: Entered 'hyperbolic_regression'-Function + [20250519_004453.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004453.]: Entered 'cubic_regression'-Function + [20250519_004453.]: 'cubic_regression': minmax = FALSE + [20250519_004453.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) - [20250519_003729.]: Logging df_agg: CpG#8 - [20250519_003729.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003729.]: c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)[20250519_003729.]: c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)[20250519_003729.]: c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) - [20250519_003729.]: Entered 'hyperbolic_regression'-Function - [20250519_003729.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003729.]: Entered 'cubic_regression'-Function - [20250519_003729.]: 'cubic_regression': minmax = FALSE - [20250519_003729.]: # CpG-site: CpG#9 + [20250519_004453.]: Logging df_agg: CpG#8 + [20250519_004453.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004453.]: c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)[20250519_004453.]: c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)[20250519_004453.]: c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) + [20250519_004453.]: Entered 'hyperbolic_regression'-Function + [20250519_004453.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004453.]: Entered 'cubic_regression'-Function + [20250519_004453.]: 'cubic_regression': minmax = FALSE + [20250519_004453.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) - [20250519_003729.]: Logging df_agg: CpG#9 - [20250519_003729.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003729.]: c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)[20250519_003729.]: c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)[20250519_003729.]: c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) - [20250519_003729.]: Entered 'hyperbolic_regression'-Function - [20250519_003729.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003730.]: Entered 'cubic_regression'-Function - [20250519_003730.]: 'cubic_regression': minmax = FALSE - [20250519_003730.]: # CpG-site: row_means + [20250519_004453.]: Logging df_agg: CpG#9 + [20250519_004453.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004453.]: c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)[20250519_004453.]: c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)[20250519_004453.]: c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) + [20250519_004453.]: Entered 'hyperbolic_regression'-Function + [20250519_004453.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004453.]: Entered 'cubic_regression'-Function + [20250519_004453.]: 'cubic_regression': minmax = FALSE + [20250519_004453.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) - [20250519_003730.]: Logging df_agg: row_means - [20250519_003730.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003730.]: c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)[20250519_003730.]: c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)[20250519_003730.]: c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) - [20250519_003730.]: Entered 'hyperbolic_regression'-Function - [20250519_003730.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003730.]: Entered 'cubic_regression'-Function - [20250519_003730.]: 'cubic_regression': minmax = FALSE - [20250519_003730.]: ### Starting with plotting ### - [20250519_003731.]: Creating BiasCorrected (cubic) plot No. 1 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG1_corrected_c.png - [20250519_003731.]: # CpG-site: CpG#1 + [20250519_004453.]: Logging df_agg: row_means + [20250519_004453.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004453.]: c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)[20250519_004453.]: c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)[20250519_004453.]: c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) + [20250519_004453.]: Entered 'hyperbolic_regression'-Function + [20250519_004453.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004454.]: Entered 'cubic_regression'-Function + [20250519_004454.]: 'cubic_regression': minmax = FALSE + [20250519_004454.]: ### Starting with plotting ### + [20250519_004454.]: Creating BiasCorrected (cubic) plot No. 1 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG1_corrected_c.png + [20250519_004454.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -1164.77580684101 , a = 2039.49844669959 , b = -1164.77580684101 , d = 1955.83553683067 - [20250519_003731.]: # CpG-site: CpG#1 + [20250519_004454.]: # CpG-site: CpG#1 Cubic: Using a = -3.42821196240366e-05 , b = 0.00462173155013798 , c = 0.850326258865826 , d = 0.500073874211125 - [20250519_003732.]: Creating BiasCorrected (cubic) plot No. 2 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG2_corrected_c.png - [20250519_003732.]: # CpG-site: CpG#2 + [20250519_004456.]: Creating BiasCorrected (cubic) plot No. 2 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG2_corrected_c.png + [20250519_004456.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = -464.128853555559 , a = 4988.85258428155 , b = -464.128853555559 , d = 4925.32559808582 - [20250519_003732.]: # CpG-site: CpG#2 + [20250519_004456.]: # CpG-site: CpG#2 Cubic: Using a = -1.40811796906314e-05 , b = 0.00190849431754485 , c = 0.933455870941473 , d = 0.363096210099674 - [20250519_003733.]: Creating BiasCorrected (cubic) plot No. 3 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG3_corrected_c.png - [20250519_003733.]: # CpG-site: CpG#3 + [20250519_004457.]: Creating BiasCorrected (cubic) plot No. 3 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG3_corrected_c.png + [20250519_004457.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = -745.611435752166 , a = 2026.53033232932 , b = -745.611435752166 , d = 1953.28002286922 - [20250519_003733.]: # CpG-site: CpG#3 + [20250519_004457.]: # CpG-site: CpG#3 Cubic: Using a = -2.78835051857186e-05 , b = 0.00366863707889089 , c = 0.880665171220893 , d = 0.510779308833653 - [20250519_003735.]: Creating BiasCorrected (cubic) plot No. 4 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG4_corrected_c.png - [20250519_003735.]: # CpG-site: CpG#4 + [20250519_004458.]: Creating BiasCorrected (cubic) plot No. 4 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG4_corrected_c.png + [20250519_004459.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = -833.073059054674 , a = 2871.1407505286 , b = -833.073059054674 , d = 2796.8155099796 - [20250519_003735.]: # CpG-site: CpG#4 + [20250519_004459.]: # CpG-site: CpG#4 Cubic: Using a = -2.30028865643e-05 , b = 0.00309017851940692 , c = 0.897073107777645 , d = 0.443455761841866 - [20250519_003736.]: Creating BiasCorrected (cubic) plot No. 5 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG5_corrected_c.png - [20250519_003736.]: # CpG-site: CpG#5 + [20250519_004500.]: Creating BiasCorrected (cubic) plot No. 5 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG5_corrected_c.png + [20250519_004500.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = 222.127659665624 , a = 12477.5957294815 , b = 222.127659665624 , d = 12432.2636419037 - [20250519_003736.]: # CpG-site: CpG#5 + [20250519_004500.]: # CpG-site: CpG#5 Cubic: Using a = -1.28632696989134e-05 , b = 0.00184822285299014 , c = 0.931001046631176 , d = 0.437963095185599 - [20250519_003738.]: Creating BiasCorrected (cubic) plot No. 6 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG6_corrected_c.png - [20250519_003738.]: # CpG-site: CpG#6 + [20250519_004501.]: Creating BiasCorrected (cubic) plot No. 6 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG6_corrected_c.png + [20250519_004501.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = -700.510534379779 , a = 21299.1825568909 , b = -700.510534379779 , d = 21234.5878606004 - [20250519_003738.]: # CpG-site: CpG#6 + [20250519_004501.]: # CpG-site: CpG#6 Cubic: Using a = -5.838335151375e-06 , b = 0.000828447163218526 , c = 0.970047764475166 , d = 0.158074701412251 - [20250519_003739.]: Creating BiasCorrected (cubic) plot No. 7 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG7_corrected_c.png - [20250519_003739.]: # CpG-site: CpG#7 + [20250519_004503.]: Creating BiasCorrected (cubic) plot No. 7 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG7_corrected_c.png + [20250519_004503.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -1144.90822837929 , a = 1477.91227480711 , b = -1144.90822837929 , d = 1394.70075909546 - [20250519_003739.]: # CpG-site: CpG#7 + [20250519_004503.]: # CpG-site: CpG#7 Cubic: Using a = -4.54220535614047e-05 , b = 0.00607993130565131 , c = 0.805411493452389 , d = 0.615125382790254 - [20250519_003741.]: Creating BiasCorrected (cubic) plot No. 8 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG8_corrected_c.png - [20250519_003741.]: # CpG-site: CpG#8 + [20250519_004504.]: Creating BiasCorrected (cubic) plot No. 8 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG8_corrected_c.png + [20250519_004504.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = -349.378022452652 , a = 2377.37834502072 , b = -349.378022452652 , d = 2315.2055766781 - [20250519_003741.]: # CpG-site: CpG#8 + [20250519_004504.]: # CpG-site: CpG#8 Cubic: Using a = -2.64622306669639e-05 , b = 0.00353621593528015 , c = 0.877916844709074 , d = 0.698684606493051 - [20250519_003742.]: Creating BiasCorrected (cubic) plot No. 9 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG9_corrected_c.png - [20250519_003742.]: # CpG-site: CpG#9 + [20250519_004506.]: Creating BiasCorrected (cubic) plot No. 9 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG9_corrected_c.png + [20250519_004506.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -1536.91601393605 , a = 3745.17687624221 , b = -1536.91601393605 , d = 3656.2687244524 - [20250519_003742.]: # CpG-site: CpG#9 + [20250519_004506.]: # CpG-site: CpG#9 Cubic: Using a = -2.70976130611725e-05 , b = 0.00378478247182342 , c = 0.871825263051793 , d = 0.455475740974113 - [20250519_003743.]: Creating BiasCorrected (cubic) plot No. 10 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_rowmeans_corrected_c.png - [20250519_003743.]: # CpG-site: row_means + [20250519_004507.]: Creating BiasCorrected (cubic) plot No. 10 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_rowmeans_corrected_c.png + [20250519_004507.]: # CpG-site: row_means Hyperbolic: Using bias_weight = -820.650348722241 , a = 3198.46441212347 , b = -820.650348722241 , d = 3124.20895615508 - [20250519_003743.]: # CpG-site: row_means + [20250519_004507.]: # CpG-site: row_means Cubic: Using a = -2.34328362100009e-05 , b = 0.00319139073176266 , c = 0.891842168032129 , d = 0.493517689787135 - [20250519_003745.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG1_corrected_c.png - [20250519_003746.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG2_corrected_c.png - [20250519_003748.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG3_corrected_c.png - [20250519_003749.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG4_corrected_c.png - [20250519_003750.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG5_corrected_c.png - [20250519_003752.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG6_corrected_c.png - [20250519_003753.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG7_corrected_c.png - [20250519_003754.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG8_corrected_c.png - [20250519_003755.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG9_corrected_c.png - [20250519_003757.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_rowmeans_corrected_c.png - [20250519_003758.]: Entered 'solving_equations'-Function - [20250519_003758.]: Solving hyperbolic regression for CpG#1 + [20250519_004509.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG1_corrected_c.png + [20250519_004510.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG2_corrected_c.png + [20250519_004511.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG3_corrected_c.png + [20250519_004512.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG4_corrected_c.png + [20250519_004514.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG5_corrected_c.png + [20250519_004515.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG6_corrected_c.png + [20250519_004516.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG7_corrected_c.png + [20250519_004517.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG8_corrected_c.png + [20250519_004519.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG9_corrected_c.png + [20250519_004520.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_rowmeans_corrected_c.png + [20250519_004521.]: Entered 'solving_equations'-Function + [20250519_004521.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 79.8673456895745 - [20250519_003758.]: Samplename: Sample#1 + [20250519_004521.]: Samplename: Sample#1 Root: 79.867 --> Root in between the borders! Added to results. Hyperbolic solved: 29.7900184340805 - [20250519_003758.]: Samplename: Sample#10 + [20250519_004521.]: Samplename: Sample#10 Root: 29.79 --> Root in between the borders! Added to results. Hyperbolic solved: 41.6525415639691 - [20250519_003758.]: Samplename: Sample#2 + [20250519_004521.]: Samplename: Sample#2 Root: 41.653 --> Root in between the borders! Added to results. Hyperbolic solved: 57.4652090254513 - [20250519_003758.]: Samplename: Sample#3 + [20250519_004521.]: Samplename: Sample#3 Root: 57.465 --> Root in between the borders! Added to results. Hyperbolic solved: 9.2007130627765 - [20250519_003758.]: Samplename: Sample#4 + [20250519_004521.]: Samplename: Sample#4 Root: 9.201 --> Root in between the borders! Added to results. Hyperbolic solved: 21.8059600538131 - [20250519_003758.]: Samplename: Sample#5 + [20250519_004521.]: Samplename: Sample#5 Root: 21.806 --> Root in between the borders! Added to results. Hyperbolic solved: 23.083796735881 - [20250519_003758.]: Samplename: Sample#6 + [20250519_004521.]: Samplename: Sample#6 Root: 23.084 --> Root in between the borders! Added to results. Hyperbolic solved: 45.5034245569385 - [20250519_003758.]: Samplename: Sample#7 + [20250519_004521.]: Samplename: Sample#7 Root: 45.503 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6987904075704 - [20250519_003758.]: Samplename: Sample#8 + [20250519_004521.]: Samplename: Sample#8 Root: 85.699 --> Root in between the borders! Added to results. Hyperbolic solved: -3.66512807265101 - [20250519_003758.]: Samplename: Sample#9 + [20250519_004521.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -3.665 --> '-10 < root < 0' --> substitute 0 - [20250519_003758.]: Solving hyperbolic regression for CpG#2 + [20250519_004521.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 76.7227931065881 - [20250519_003758.]: Samplename: Sample#1 + [20250519_004521.]: Samplename: Sample#1 Root: 76.723 --> Root in between the borders! Added to results. Hyperbolic solved: 30.992427511473 - [20250519_003758.]: Samplename: Sample#10 + [20250519_004521.]: Samplename: Sample#10 Root: 30.992 --> Root in between the borders! Added to results. Hyperbolic solved: 42.6810882780827 - [20250519_003758.]: Samplename: Sample#2 + [20250519_004521.]: Samplename: Sample#2 Root: 42.681 --> Root in between the borders! Added to results. Hyperbolic solved: 58.9337908379889 - [20250519_003758.]: Samplename: Sample#3 + [20250519_004521.]: Samplename: Sample#3 Root: 58.934 --> Root in between the borders! Added to results. Hyperbolic solved: 4.98020457313721 - [20250519_003758.]: Samplename: Sample#4 + [20250519_004521.]: Samplename: Sample#4 Root: 4.98 --> Root in between the borders! Added to results. Hyperbolic solved: 20.2480263675669 - [20250519_003758.]: Samplename: Sample#5 + [20250519_004521.]: Samplename: Sample#5 Root: 20.248 --> Root in between the borders! Added to results. Hyperbolic solved: 17.8133085643845 - [20250519_003758.]: Samplename: Sample#6 + [20250519_004521.]: Samplename: Sample#6 Root: 17.813 --> Root in between the borders! Added to results. Hyperbolic solved: 40.9176745340012 - [20250519_003758.]: Samplename: Sample#7 + [20250519_004521.]: Samplename: Sample#7 Root: 40.918 --> Root in between the borders! Added to results. Hyperbolic solved: 86.0450715732916 - [20250519_003758.]: Samplename: Sample#8 + [20250519_004521.]: Samplename: Sample#8 Root: 86.045 --> Root in between the borders! Added to results. Hyperbolic solved: 3.11579770508969 - [20250519_003758.]: Samplename: Sample#9 + [20250519_004521.]: Samplename: Sample#9 Root: 3.116 --> Root in between the borders! Added to results. - [20250519_003758.]: Solving hyperbolic regression for CpG#3 + [20250519_004521.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 74.9349254100163 - [20250519_003758.]: Samplename: Sample#1 + [20250519_004521.]: Samplename: Sample#1 Root: 74.935 --> Root in between the borders! Added to results. Hyperbolic solved: 27.6844381581493 - [20250519_003758.]: Samplename: Sample#10 + [20250519_004521.]: Samplename: Sample#10 Root: 27.684 --> Root in between the borders! Added to results. Hyperbolic solved: 41.852019114379 - [20250519_003758.]: Samplename: Sample#2 + [20250519_004521.]: Samplename: Sample#2 Root: 41.852 --> Root in between the borders! Added to results. Hyperbolic solved: 55.8325180209418 - [20250519_003758.]: Samplename: Sample#3 + [20250519_004521.]: Samplename: Sample#3 Root: 55.833 --> Root in between the borders! Added to results. Hyperbolic solved: 8.03519251633153 - [20250519_003758.]: Samplename: Sample#4 + [20250519_004521.]: Samplename: Sample#4 Root: 8.035 --> Root in between the borders! Added to results. Hyperbolic solved: 24.1066315721853 - [20250519_003758.]: Samplename: Sample#5 + [20250519_004521.]: Samplename: Sample#5 Root: 24.107 --> Root in between the borders! Added to results. Hyperbolic solved: 26.2419820027673 - [20250519_003758.]: Samplename: Sample#6 + [20250519_004521.]: Samplename: Sample#6 Root: 26.242 --> Root in between the borders! Added to results. Hyperbolic solved: 44.0944922703422 - [20250519_003758.]: Samplename: Sample#7 + [20250519_004521.]: Samplename: Sample#7 Root: 44.094 --> Root in between the borders! Added to results. Hyperbolic solved: 85.8279382585787 - [20250519_003758.]: Samplename: Sample#8 + [20250519_004521.]: Samplename: Sample#8 Root: 85.828 --> Root in between the borders! Added to results. Hyperbolic solved: -0.666482392725758 - [20250519_003758.]: Samplename: Sample#9 + [20250519_004521.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.666 --> '-10 < root < 0' --> substitute 0 - [20250519_003758.]: Solving hyperbolic regression for CpG#4 + [20250519_004521.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 76.3495278640236 - [20250519_003758.]: Samplename: Sample#1 + [20250519_004521.]: Samplename: Sample#1 Root: 76.35 --> Root in between the borders! Added to results. Hyperbolic solved: 28.2568553570941 - [20250519_003758.]: Samplename: Sample#10 + [20250519_004521.]: Samplename: Sample#10 Root: 28.257 --> Root in between the borders! Added to results. Hyperbolic solved: 43.4089839390807 - [20250519_003758.]: Samplename: Sample#2 + [20250519_004521.]: Samplename: Sample#2 Root: 43.409 --> Root in between the borders! Added to results. Hyperbolic solved: 58.5435236860146 - [20250519_003758.]: Samplename: Sample#3 + [20250519_004521.]: Samplename: Sample#3 Root: 58.544 --> Root in between the borders! Added to results. Hyperbolic solved: 10.3087045690571 - [20250519_003758.]: Samplename: Sample#4 + [20250519_004521.]: Samplename: Sample#4 Root: 10.309 --> Root in between the borders! Added to results. Hyperbolic solved: 22.183045165659 - [20250519_003758.]: Samplename: Sample#5 + [20250519_004521.]: Samplename: Sample#5 Root: 22.183 --> Root in between the borders! Added to results. Hyperbolic solved: 27.1337769553499 - [20250519_003758.]: Samplename: Sample#6 + [20250519_004521.]: Samplename: Sample#6 Root: 27.134 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8321096080155 - [20250519_003758.]: Samplename: Sample#7 + [20250519_004521.]: Samplename: Sample#7 Root: 41.832 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6890189074743 - [20250519_003758.]: Samplename: Sample#8 + [20250519_004521.]: Samplename: Sample#8 Root: 85.689 --> Root in between the borders! Added to results. Hyperbolic solved: 2.42232098177269 - [20250519_003758.]: Samplename: Sample#9 + [20250519_004521.]: Samplename: Sample#9 Root: 2.422 --> Root in between the borders! Added to results. - [20250519_003758.]: Solving hyperbolic regression for CpG#5 + [20250519_004521.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 72.8126914355011 - [20250519_003758.]: Samplename: Sample#1 + [20250519_004521.]: Samplename: Sample#1 Root: 72.813 --> Root in between the borders! Added to results. Hyperbolic solved: 26.4206935815673 - [20250519_003758.]: Samplename: Sample#10 + [20250519_004521.]: Samplename: Sample#10 Root: 26.421 --> Root in between the borders! Added to results. Hyperbolic solved: 44.2738765057261 - [20250519_003758.]: Samplename: Sample#2 + [20250519_004521.]: Samplename: Sample#2 Root: 44.274 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5324386232341 - [20250519_003758.]: Samplename: Sample#3 + [20250519_004521.]: Samplename: Sample#3 Root: 53.532 --> Root in between the borders! Added to results. Hyperbolic solved: 10.0373717962154 - [20250519_003758.]: Samplename: Sample#4 + [20250519_004521.]: Samplename: Sample#4 Root: 10.037 --> Root in between the borders! Added to results. Hyperbolic solved: 22.8733115052607 - [20250519_003758.]: Samplename: Sample#5 + [20250519_004521.]: Samplename: Sample#5 Root: 22.873 --> Root in between the borders! Added to results. Hyperbolic solved: 25.9417915784748 - [20250519_003758.]: Samplename: Sample#6 + [20250519_004521.]: Samplename: Sample#6 Root: 25.942 --> Root in between the borders! Added to results. Hyperbolic solved: 42.7945074918604 - [20250519_003758.]: Samplename: Sample#7 + [20250519_004521.]: Samplename: Sample#7 Root: 42.795 --> Root in between the borders! Added to results. Hyperbolic solved: 88.8741214630346 - [20250519_003758.]: Samplename: Sample#8 + [20250519_004521.]: Samplename: Sample#8 Root: 88.874 --> Root in between the borders! Added to results. Hyperbolic solved: 2.34647529966966 - [20250519_003758.]: Samplename: Sample#9 + [20250519_004521.]: Samplename: Sample#9 Root: 2.346 --> Root in between the borders! Added to results. - [20250519_003758.]: Solving hyperbolic regression for CpG#6 + [20250519_004521.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 79.2780593622711 - [20250519_003758.]: Samplename: Sample#1 + [20250519_004521.]: Samplename: Sample#1 Root: 79.278 --> Root in between the borders! Added to results. Hyperbolic solved: 30.2012458984074 - [20250519_003758.]: Samplename: Sample#10 + [20250519_004521.]: Samplename: Sample#10 Root: 30.201 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8474393624107 - [20250519_003758.]: Samplename: Sample#2 + [20250519_004521.]: Samplename: Sample#2 Root: 41.847 --> Root in between the borders! Added to results. Hyperbolic solved: 56.8423517321508 - [20250519_003758.]: Samplename: Sample#3 + [20250519_004521.]: Samplename: Sample#3 Root: 56.842 --> Root in between the borders! Added to results. Hyperbolic solved: 8.87856046118588 - [20250519_003758.]: Samplename: Sample#4 + [20250519_004521.]: Samplename: Sample#4 Root: 8.879 --> Root in between the borders! Added to results. Hyperbolic solved: 18.69015950004 - [20250519_003758.]: Samplename: Sample#5 + [20250519_004521.]: Samplename: Sample#5 Root: 18.69 --> Root in between the borders! Added to results. Hyperbolic solved: 29.9309263534749 - [20250519_003758.]: Samplename: Sample#6 + [20250519_004521.]: Samplename: Sample#6 Root: 29.931 --> Root in between the borders! Added to results. Hyperbolic solved: 42.8148560027697 - [20250519_003758.]: Samplename: Sample#7 + [20250519_004521.]: Samplename: Sample#7 Root: 42.815 --> Root in between the borders! Added to results. Hyperbolic solved: 86.7501831416152 - [20250519_003758.]: Samplename: Sample#8 + [20250519_004521.]: Samplename: Sample#8 Root: 86.75 --> Root in between the borders! Added to results. Hyperbolic solved: 1.51516194985267 - [20250519_003758.]: Samplename: Sample#9 + [20250519_004521.]: Samplename: Sample#9 Root: 1.515 --> Root in between the borders! Added to results. - [20250519_003758.]: Solving hyperbolic regression for CpG#7 + [20250519_004521.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 78.2565592569279 - [20250519_003758.]: Samplename: Sample#1 + [20250519_004521.]: Samplename: Sample#1 Root: 78.257 --> Root in between the borders! Added to results. Hyperbolic solved: 25.488739349283 - [20250519_003758.]: Samplename: Sample#10 + [20250519_004521.]: Samplename: Sample#10 Root: 25.489 --> Root in between the borders! Added to results. Hyperbolic solved: 47.3712258915285 - [20250519_003758.]: Samplename: Sample#2 + [20250519_004521.]: Samplename: Sample#2 Root: 47.371 --> Root in between the borders! Added to results. Hyperbolic solved: 58.3142673189298 - [20250519_003758.]: Samplename: Sample#3 + [20250519_004521.]: Samplename: Sample#3 Root: 58.314 --> Root in between the borders! Added to results. Hyperbolic solved: 11.7212231360573 - [20250519_003758.]: Samplename: Sample#4 + [20250519_004521.]: Samplename: Sample#4 Root: 11.721 --> Root in between the borders! Added to results. Hyperbolic solved: 25.3797485992238 - [20250519_003758.]: Samplename: Sample#5 + [20250519_004521.]: Samplename: Sample#5 Root: 25.38 --> Root in between the borders! Added to results. Hyperbolic solved: 29.4095133062523 - [20250519_003758.]: Samplename: Sample#6 + [20250519_004521.]: Samplename: Sample#6 Root: 29.41 --> Root in between the borders! Added to results. Hyperbolic solved: 44.5755071469546 - [20250519_003758.]: Samplename: Sample#7 + [20250519_004521.]: Samplename: Sample#7 Root: 44.576 --> Root in between the borders! Added to results. Hyperbolic solved: 85.9628731021447 - [20250519_003758.]: Samplename: Sample#8 + [20250519_004521.]: Samplename: Sample#8 Root: 85.963 --> Root in between the borders! Added to results. Hyperbolic solved: -4.1645647175353 - [20250519_003758.]: Samplename: Sample#9 + [20250519_004521.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -4.165 --> '-10 < root < 0' --> substitute 0 - [20250519_003758.]: Solving hyperbolic regression for CpG#8 + [20250519_004521.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 72.9696605050898 - [20250519_003758.]: Samplename: Sample#1 + [20250519_004521.]: Samplename: Sample#1 Root: 72.97 --> Root in between the borders! Added to results. Hyperbolic solved: 27.8250956361743 - [20250519_003758.]: Samplename: Sample#10 + [20250519_004521.]: Samplename: Sample#10 Root: 27.825 --> Root in between the borders! Added to results. Hyperbolic solved: 34.9068388175484 - [20250519_003758.]: Samplename: Sample#2 + [20250519_004521.]: Samplename: Sample#2 Root: 34.907 --> Root in between the borders! Added to results. Hyperbolic solved: 59.163743824213 - [20250519_003758.]: Samplename: Sample#3 + [20250519_004521.]: Samplename: Sample#3 Root: 59.164 --> Root in between the borders! Added to results. Hyperbolic solved: 9.64644413255063 - [20250519_003758.]: Samplename: Sample#4 + [20250519_004521.]: Samplename: Sample#4 Root: 9.646 --> Root in between the borders! Added to results. Hyperbolic solved: 19.3522607837916 - [20250519_003758.]: Samplename: Sample#5 + [20250519_004522.]: Samplename: Sample#5 Root: 19.352 --> Root in between the borders! Added to results. Hyperbolic solved: 38.5571436812761 - [20250519_003758.]: Samplename: Sample#6 + [20250519_004522.]: Samplename: Sample#6 Root: 38.557 --> Root in between the borders! Added to results. Hyperbolic solved: 44.7759625743335 - [20250519_003758.]: Samplename: Sample#7 + [20250519_004522.]: Samplename: Sample#7 Root: 44.776 --> Root in between the borders! Added to results. Hyperbolic solved: 84.8599671624974 - [20250519_003758.]: Samplename: Sample#8 + [20250519_004522.]: Samplename: Sample#8 Root: 84.86 --> Root in between the borders! Added to results. Hyperbolic solved: -2.05044894070214 - [20250519_003758.]: Samplename: Sample#9 + [20250519_004522.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.05 --> '-10 < root < 0' --> substitute 0 - [20250519_003758.]: Solving hyperbolic regression for CpG#9 + [20250519_004522.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 81.041667802328 - [20250519_003758.]: Samplename: Sample#1 + [20250519_004522.]: Samplename: Sample#1 Root: 81.042 --> Root in between the borders! Added to results. Hyperbolic solved: 26.6441666289306 - [20250519_003758.]: Samplename: Sample#10 + [20250519_004522.]: Samplename: Sample#10 Root: 26.644 --> Root in between the borders! Added to results. Hyperbolic solved: 45.7046199658262 - [20250519_003758.]: Samplename: Sample#2 + [20250519_004522.]: Samplename: Sample#2 Root: 45.705 --> Root in between the borders! Added to results. Hyperbolic solved: 56.9767986854544 - [20250519_003758.]: Samplename: Sample#3 + [20250519_004522.]: Samplename: Sample#3 Root: 56.977 --> Root in between the borders! Added to results. Hyperbolic solved: 6.81231222322667 - [20250519_003758.]: Samplename: Sample#4 + [20250519_004522.]: Samplename: Sample#4 Root: 6.812 --> Root in between the borders! Added to results. Hyperbolic solved: 22.9284780361346 - [20250519_003758.]: Samplename: Sample#5 + [20250519_004522.]: Samplename: Sample#5 Root: 22.928 --> Root in between the borders! Added to results. Hyperbolic solved: 38.8653203361426 - [20250519_003758.]: Samplename: Sample#6 + [20250519_004522.]: Samplename: Sample#6 Root: 38.865 --> Root in between the borders! Added to results. Hyperbolic solved: 43.6855631975613 - [20250519_003758.]: Samplename: Sample#7 + [20250519_004522.]: Samplename: Sample#7 Root: 43.686 --> Root in between the borders! Added to results. Hyperbolic solved: 88.0155364495973 - [20250519_003758.]: Samplename: Sample#8 + [20250519_004522.]: Samplename: Sample#8 Root: 88.016 --> Root in between the borders! Added to results. Hyperbolic solved: -3.35610072407525 - [20250519_003758.]: Samplename: Sample#9 + [20250519_004522.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -3.356 --> '-10 < root < 0' --> substitute 0 - [20250519_003758.]: Solving hyperbolic regression for row_means + [20250519_004522.]: Solving hyperbolic regression for row_means Hyperbolic solved: 77.0692799954538 - [20250519_003758.]: Samplename: Sample#1 + [20250519_004522.]: Samplename: Sample#1 Root: 77.069 --> Root in between the borders! Added to results. Hyperbolic solved: 28.3620043244584 - [20250519_003758.]: Samplename: Sample#10 + [20250519_004522.]: Samplename: Sample#10 Root: 28.362 --> Root in between the borders! Added to results. Hyperbolic solved: 42.5026175468089 - [20250519_003758.]: Samplename: Sample#2 + [20250519_004522.]: Samplename: Sample#2 Root: 42.503 --> Root in between the borders! Added to results. Hyperbolic solved: 57.2972050444248 - [20250519_003758.]: Samplename: Sample#3 + [20250519_004522.]: Samplename: Sample#3 Root: 57.297 --> Root in between the borders! Added to results. Hyperbolic solved: 8.82704012618393 - [20250519_003758.]: Samplename: Sample#4 + [20250519_004522.]: Samplename: Sample#4 Root: 8.827 --> Root in between the borders! Added to results. Hyperbolic solved: 21.8102592524881 - [20250519_003758.]: Samplename: Sample#5 + [20250519_004522.]: Samplename: Sample#5 Root: 21.81 --> Root in between the borders! Added to results. Hyperbolic solved: 28.7228660045975 - [20250519_003758.]: Samplename: Sample#6 + [20250519_004522.]: Samplename: Sample#6 Root: 28.723 --> Root in between the borders! Added to results. Hyperbolic solved: 43.410510290696 - [20250519_003758.]: Samplename: Sample#7 + [20250519_004522.]: Samplename: Sample#7 Root: 43.411 --> Root in between the borders! Added to results. Hyperbolic solved: 86.4143551963301 - [20250519_003758.]: Samplename: Sample#8 + [20250519_004522.]: Samplename: Sample#8 Root: 86.414 --> Root in between the borders! Added to results. Hyperbolic solved: -0.237020571201611 - [20250519_003758.]: Samplename: Sample#9 + [20250519_004522.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.237 --> '-10 < root < 0' --> substitute 0 - [20250519_003758.]: on_start: using future::plan("sequential") - [20250519_003758.]: Entered 'clean_dt'-Function - [20250519_003758.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_003758.]: got experimental data - [20250519_003758.]: Entered 'clean_dt'-Function - [20250519_003758.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_003758.]: got calibration data - [20250519_003759.]: + [20250519_004522.]: on_start: using future::plan("sequential") + [20250519_004522.]: Entered 'clean_dt'-Function + [20250519_004522.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_004522.]: got experimental data + [20250519_004522.]: Entered 'clean_dt'-Function + [20250519_004522.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_004522.]: got calibration data + [20250519_004522.]: ### Starting with regression calculations ### - [20250519_003759.]: Entered 'regression_type1'-Function + [20250519_004522.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : @@ -15807,141 +15804,141 @@ step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient - [20250519_003759.]: # CpG-site: CpG#1 + [20250519_004522.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_003759.]: Logging df_agg: CpG#1 - [20250519_003759.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003759.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_003759.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_003759.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_003759.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_003759.]: Entered 'hyperbolic_regression'-Function - [20250519_003759.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003759.]: Entered 'cubic_regression'-Function - [20250519_003759.]: 'cubic_regression': minmax = FALSE - [20250519_003759.]: # CpG-site: CpG#2 + [20250519_004522.]: Logging df_agg: CpG#1 + [20250519_004522.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004522.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_004522.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_004522.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_004522.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) + [20250519_004522.]: Entered 'hyperbolic_regression'-Function + [20250519_004522.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004522.]: Entered 'cubic_regression'-Function + [20250519_004522.]: 'cubic_regression': minmax = FALSE + [20250519_004522.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_003759.]: Logging df_agg: CpG#2 - [20250519_003759.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003759.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_003759.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_003759.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_003759.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_003759.]: Entered 'hyperbolic_regression'-Function - [20250519_003759.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003759.]: Entered 'cubic_regression'-Function - [20250519_003759.]: 'cubic_regression': minmax = FALSE - [20250519_003759.]: # CpG-site: CpG#3 + [20250519_004522.]: Logging df_agg: CpG#2 + [20250519_004522.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004522.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_004522.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_004522.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_004522.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) + [20250519_004522.]: Entered 'hyperbolic_regression'-Function + [20250519_004522.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004522.]: Entered 'cubic_regression'-Function + [20250519_004522.]: 'cubic_regression': minmax = FALSE + [20250519_004522.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_003759.]: Logging df_agg: CpG#3 - [20250519_003759.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003759.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_003759.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_003759.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_003759.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_003759.]: Entered 'hyperbolic_regression'-Function - [20250519_003759.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003759.]: Entered 'cubic_regression'-Function - [20250519_003759.]: 'cubic_regression': minmax = FALSE - [20250519_003759.]: # CpG-site: CpG#4 + [20250519_004522.]: Logging df_agg: CpG#3 + [20250519_004522.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004522.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_004522.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_004522.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_004522.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) + [20250519_004522.]: Entered 'hyperbolic_regression'-Function + [20250519_004522.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004523.]: Entered 'cubic_regression'-Function + [20250519_004523.]: 'cubic_regression': minmax = FALSE + [20250519_004523.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_003759.]: Logging df_agg: CpG#4 - [20250519_003759.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003759.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_003759.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_003759.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_003759.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_003759.]: Entered 'hyperbolic_regression'-Function - [20250519_003759.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003759.]: Entered 'cubic_regression'-Function - [20250519_003759.]: 'cubic_regression': minmax = FALSE - [20250519_003800.]: # CpG-site: CpG#5 + [20250519_004523.]: Logging df_agg: CpG#4 + [20250519_004523.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004523.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_004523.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_004523.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_004523.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) + [20250519_004523.]: Entered 'hyperbolic_regression'-Function + [20250519_004523.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004523.]: Entered 'cubic_regression'-Function + [20250519_004523.]: 'cubic_regression': minmax = FALSE + [20250519_004523.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_003800.]: Logging df_agg: CpG#5 - [20250519_003800.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003800.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_003800.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_003800.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_003800.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_003800.]: Entered 'hyperbolic_regression'-Function - [20250519_003800.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003800.]: Entered 'cubic_regression'-Function - [20250519_003800.]: 'cubic_regression': minmax = FALSE - [20250519_003800.]: # CpG-site: CpG#6 + [20250519_004523.]: Logging df_agg: CpG#5 + [20250519_004523.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004523.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_004523.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_004523.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_004523.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) + [20250519_004523.]: Entered 'hyperbolic_regression'-Function + [20250519_004523.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004523.]: Entered 'cubic_regression'-Function + [20250519_004523.]: 'cubic_regression': minmax = FALSE + [20250519_004523.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_003800.]: Logging df_agg: CpG#6 - [20250519_003800.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003800.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_003800.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_003800.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_003800.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_003800.]: Entered 'hyperbolic_regression'-Function - [20250519_003800.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003800.]: Entered 'cubic_regression'-Function - [20250519_003800.]: 'cubic_regression': minmax = FALSE - [20250519_003800.]: # CpG-site: CpG#7 + [20250519_004523.]: Logging df_agg: CpG#6 + [20250519_004523.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004523.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_004523.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_004523.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_004523.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) + [20250519_004523.]: Entered 'hyperbolic_regression'-Function + [20250519_004523.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004523.]: Entered 'cubic_regression'-Function + [20250519_004523.]: 'cubic_regression': minmax = FALSE + [20250519_004523.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_003800.]: Logging df_agg: CpG#7 - [20250519_003800.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003800.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_003800.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_003800.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_003800.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_003800.]: Entered 'hyperbolic_regression'-Function - [20250519_003800.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003800.]: Entered 'cubic_regression'-Function - [20250519_003800.]: 'cubic_regression': minmax = FALSE - [20250519_003800.]: # CpG-site: CpG#8 + [20250519_004523.]: Logging df_agg: CpG#7 + [20250519_004523.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004523.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_004523.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_004523.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_004523.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) + [20250519_004523.]: Entered 'hyperbolic_regression'-Function + [20250519_004523.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004523.]: Entered 'cubic_regression'-Function + [20250519_004523.]: 'cubic_regression': minmax = FALSE + [20250519_004523.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_003800.]: Logging df_agg: CpG#8 - [20250519_003800.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003800.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_003800.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_003800.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_003800.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_003800.]: Entered 'hyperbolic_regression'-Function - [20250519_003800.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003800.]: Entered 'cubic_regression'-Function - [20250519_003800.]: 'cubic_regression': minmax = FALSE - [20250519_003800.]: # CpG-site: CpG#9 + [20250519_004523.]: Logging df_agg: CpG#8 + [20250519_004523.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004523.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_004523.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_004523.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_004523.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) + [20250519_004523.]: Entered 'hyperbolic_regression'-Function + [20250519_004523.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004524.]: Entered 'cubic_regression'-Function + [20250519_004524.]: 'cubic_regression': minmax = FALSE + [20250519_004524.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_003800.]: Logging df_agg: CpG#9 - [20250519_003800.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003800.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_003800.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_003800.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_003800.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_003800.]: Entered 'hyperbolic_regression'-Function - [20250519_003800.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003801.]: Entered 'cubic_regression'-Function - [20250519_003801.]: 'cubic_regression': minmax = FALSE - [20250519_003801.]: # CpG-site: row_means + [20250519_004524.]: Logging df_agg: CpG#9 + [20250519_004524.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004524.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_004524.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_004524.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_004524.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) + [20250519_004524.]: Entered 'hyperbolic_regression'-Function + [20250519_004524.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004524.]: Entered 'cubic_regression'-Function + [20250519_004524.]: 'cubic_regression': minmax = FALSE + [20250519_004524.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723122, 2.47760998435345)c(5.94911111111111, 0.84962962962963, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703704, 19.6995555555555, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_003801.]: Logging df_agg: row_means - [20250519_003801.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003801.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_003801.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723122, 2.47760998435345)[20250519_003801.]: c(5.94911111111111, 0.84962962962963, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_003801.]: c(NA, 6.79703703703704, 19.6995555555555, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_003801.]: Entered 'hyperbolic_regression'-Function - [20250519_003801.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003801.]: Entered 'cubic_regression'-Function - [20250519_003801.]: 'cubic_regression': minmax = FALSE - [20250519_003801.]: ### Starting with plotting ### - [20250519_003802.]: Creating plot No. 1 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG1.png - [20250519_003802.]: # CpG-site: CpG#1 + [20250519_004524.]: Logging df_agg: row_means + [20250519_004524.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004524.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_004524.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723122, 2.47760998435345)[20250519_004524.]: c(5.94911111111111, 0.84962962962963, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_004524.]: c(NA, 6.79703703703704, 19.6995555555555, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) + [20250519_004524.]: Entered 'hyperbolic_regression'-Function + [20250519_004524.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004524.]: Entered 'cubic_regression'-Function + [20250519_004524.]: 'cubic_regression': minmax = FALSE + [20250519_004525.]: ### Starting with plotting ### + [20250519_004525.]: Creating plot No. 1 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG1.png + [20250519_004525.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -937.71833725671 , a = -108.568188547925 , b = -937.71833725671 , d = -232.05735859911 - [20250519_003802.]: # CpG-site: CpG#1 + [20250519_004525.]: # CpG-site: CpG#1 Cubic: Using a = 6.53413423120091e-05 , b = -0.0055806968734969 , c = 0.784061853455188 , d = 1.93182659932656 - [20250519_003803.]: Creating plot No. 2 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG2.png - [20250519_003803.]: # CpG-site: CpG#2 + [20250519_004526.]: Creating plot No. 2 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG2.png + [20250519_004526.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = -2393.14264996499 , a = -141.862267639951 , b = -2393.14264996499 , d = -268.331587255735 - [20250519_003803.]: # CpG-site: CpG#2 + [20250519_004526.]: # CpG-site: CpG#2 Cubic: Using a = 8.04237934904601e-06 , b = 0.00293158941798942 , c = 0.514821742825076 , d = 9.27667003367003 - [20250519_003805.]: Creating plot No. 3 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG3.png - [20250519_003805.]: # CpG-site: CpG#3 + [20250519_004528.]: Creating plot No. 3 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG3.png + [20250519_004528.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = -1477.46867711742 , a = -170.424279984248 , b = -1477.46867711742 , d = -327.189866698889 - [20250519_003805.]: # CpG-site: CpG#3 + [20250519_004528.]: # CpG-site: CpG#3 Cubic: Using a = 2.30555869809204e-05 , b = -0.000797009331409346 , c = 0.62783129228796 , d = 3.88705218855218 - [20250519_003806.]: Creating plot No. 4 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG4.png - [20250519_003806.]: # CpG-site: CpG#4 + [20250519_004529.]: Creating plot No. 4 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG4.png + [20250519_004529.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = -2115.87814484424 , a = -162.169929539519 , b = -2115.87814484424 , d = -299.662454768746 - [20250519_003806.]: # CpG-site: CpG#4 + [20250519_004529.]: # CpG-site: CpG#4 Cubic: Using a = 3.2166356902357e-05 , b = -0.0015913142857143 , c = 0.697398364598366 , d = 6.17055050505048 - [20250519_003808.]: Creating plot No. 5 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG5.png - [20250519_003808.]: # CpG-site: CpG#5 + [20250519_004531.]: Creating plot No. 5 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG5.png + [20250519_004531.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = -1383.32734884945 , a = -151.440088502132 , b = -1383.32734884945 , d = -304.200280997675 - [20250519_003808.]: # CpG-site: CpG#5 + [20250519_004531.]: # CpG-site: CpG#5 Cubic: Using a = -4.48459708193036e-06 , b = 0.00375371236171235 , c = 0.422446384479718 , d = 5.17203872053872 - [20250519_003809.]: Creating plot No. 6 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG6.png - [20250519_003809.]: # CpG-site: CpG#6 + [20250519_004532.]: Creating plot No. 6 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG6.png + [20250519_004532.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = -1550.59304894212 , a = -125.016150436792 , b = -1550.59304894212 , d = -247.279683783527 - [20250519_003809.]: # CpG-site: CpG#6 + [20250519_004532.]: # CpG-site: CpG#6 Cubic: Using a = 2.38852884399552e-05 , b = 0.000918637037037021 , c = 0.561022132435467 , d = 6.1479276094276 - [20250519_003811.]: Creating plot No. 7 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG7.png - [20250519_003811.]: # CpG-site: CpG#7 + [20250519_004534.]: Creating plot No. 7 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG7.png + [20250519_004534.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -772.843265948977 , a = -75.2726697123378 , b = -772.843265948977 , d = -226.67343046903 - [20250519_003811.]: # CpG-site: CpG#7 + [20250519_004534.]: # CpG-site: CpG#7 Cubic: Using a = 4.98010505050505e-05 , b = -0.00436152150072151 , c = 0.579588917748918 , d = 1.72104545454544 - [20250519_003812.]: Creating plot No. 8 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG8.png - [20250519_003812.]: # CpG-site: CpG#8 + [20250519_004535.]: Creating plot No. 8 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG8.png + [20250519_004535.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = -3872.73534838154 , a = -278.017155483789 , b = -3872.73534838154 , d = -438.894126934244 - [20250519_003812.]: # CpG-site: CpG#8 + [20250519_004535.]: # CpG-site: CpG#8 Cubic: Using a = -1.50060965207632e-05 , b = 0.00447978143338143 , c = 0.534897737694404 , d = 9.56981481481482 - [20250519_003814.]: Creating plot No. 9 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG9.png - [20250519_003814.]: # CpG-site: CpG#9 + [20250519_004537.]: Creating plot No. 9 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG9.png + [20250519_004537.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -843.925271611492 , a = -70.6796326247736 , b = -843.925271611492 , d = -184.482178799669 - [20250519_003814.]: # CpG-site: CpG#9 + [20250519_004537.]: # CpG-site: CpG#9 Cubic: Using a = 7.72300426487093e-05 , b = -0.00542281173641174 , c = 0.67899229597563 , d = 2.72413468013467 - [20250519_003815.]: Creating plot No. 10 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_rowmeans.png - [20250519_003815.]: # CpG-site: row_means + [20250519_004538.]: Creating plot No. 10 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_rowmeans.png + [20250519_004538.]: # CpG-site: row_means Hyperbolic: Using bias_weight = -1525.75751056682 , a = -127.675093193972 , b = -1525.75751056682 , d = -263.003121358972 - [20250519_003815.]: # CpG-site: row_means + [20250519_004538.]: # CpG-site: row_means Cubic: Using a = 2.88923726150392e-05 , b = -0.0006299592752926 , c = 0.600117857944524 , d = 5.17789562289563 - [20250519_003817.]: Entered 'solving_equations'-Function - [20250519_003817.]: Solving hyperbolic regression for CpG#1 + [20250519_004540.]: Entered 'solving_equations'-Function + [20250519_004540.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: -2.23220911684257 - [20250519_003817.]: Samplename: 0 + [20250519_004540.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. @@ -15949,126 +15946,126 @@ Root: -2.232 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.1698564372971 - [20250519_003817.]: Samplename: 12.5 + [20250519_004540.]: Samplename: 12.5 Root: 12.17 --> Root in between the borders! Added to results. Hyperbolic solved: 24.478190918656 - [20250519_003817.]: Samplename: 25 + [20250519_004540.]: Samplename: 25 Root: 24.478 --> Root in between the borders! Added to results. Hyperbolic solved: 38.1730371634976 - [20250519_003817.]: Samplename: 37.5 + [20250519_004540.]: Samplename: 37.5 Root: 38.173 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3349263290239 - [20250519_003817.]: Samplename: 50 + [20250519_004540.]: Samplename: 50 Root: 52.335 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4582665283612 - [20250519_003817.]: Samplename: 62.5 + [20250519_004540.]: Samplename: 62.5 Root: 65.458 --> Root in between the borders! Added to results. Hyperbolic solved: 75.0090705794847 - [20250519_003817.]: Samplename: 75 + [20250519_004540.]: Samplename: 75 Root: 75.009 --> Root in between the borders! Added to results. Hyperbolic solved: 81.5271853400487 - [20250519_003817.]: Samplename: 87.5 + [20250519_004540.]: Samplename: 87.5 Root: 81.527 --> Root in between the borders! Added to results. Hyperbolic solved: 102.400898271831 - [20250519_003817.]: Samplename: 100 + [20250519_004540.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.401 --> '100 < root < 110' --> substitute 100 - [20250519_003817.]: Solving hyperbolic regression for CpG#2 + [20250519_004540.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 1.13660511681856 - [20250519_003817.]: Samplename: 0 + [20250519_004540.]: Samplename: 0 Root: 1.137 --> Root in between the borders! Added to results. Hyperbolic solved: 11.4129696918445 - [20250519_003817.]: Samplename: 12.5 + [20250519_004540.]: Samplename: 12.5 Root: 11.413 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1740004612661 - [20250519_003817.]: Samplename: 25 + [20250519_004540.]: Samplename: 25 Root: 26.174 --> Root in between the borders! Added to results. Hyperbolic solved: 35.1050448132614 - [20250519_003817.]: Samplename: 37.5 + [20250519_004540.]: Samplename: 37.5 Root: 35.105 --> Root in between the borders! Added to results. Hyperbolic solved: 47.6855002074492 - [20250519_003817.]: Samplename: 50 + [20250519_004540.]: Samplename: 50 Root: 47.686 --> Root in between the borders! Added to results. Hyperbolic solved: 67.1440493313574 - [20250519_003817.]: Samplename: 62.5 + [20250519_004540.]: Samplename: 62.5 Root: 67.144 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7644668045486 - [20250519_003817.]: Samplename: 75 + [20250519_004540.]: Samplename: 75 Root: 75.764 --> Root in between the borders! Added to results. Hyperbolic solved: 84.4054158145455 - [20250519_003817.]: Samplename: 87.5 + [20250519_004540.]: Samplename: 87.5 Root: 84.405 --> Root in between the borders! Added to results. Hyperbolic solved: 100.948272543309 - [20250519_003817.]: Samplename: 100 + [20250519_004540.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.948 --> '100 < root < 110' --> substitute 100 - [20250519_003817.]: Solving hyperbolic regression for CpG#3 + [20250519_004540.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 0.512348561877098 - [20250519_003817.]: Samplename: 0 + [20250519_004540.]: Samplename: 0 Root: 0.512 --> Root in between the borders! Added to results. Hyperbolic solved: 10.7523845008661 - [20250519_003817.]: Samplename: 12.5 + [20250519_004540.]: Samplename: 12.5 Root: 10.752 --> Root in between the borders! Added to results. Hyperbolic solved: 25.5218912317424 - [20250519_003817.]: Samplename: 25 + [20250519_004540.]: Samplename: 25 Root: 25.522 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5270488279988 - [20250519_003817.]: Samplename: 37.5 + [20250519_004540.]: Samplename: 37.5 Root: 36.527 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7909283805111 - [20250519_003817.]: Samplename: 50 + [20250519_004540.]: Samplename: 50 Root: 50.791 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8686353441458 - [20250519_003817.]: Samplename: 62.5 + [20250519_004540.]: Samplename: 62.5 Root: 64.869 --> Root in between the borders! Added to results. Hyperbolic solved: 77.5524208271734 - [20250519_003817.]: Samplename: 75 + [20250519_004540.]: Samplename: 75 Root: 77.552 --> Root in between the borders! Added to results. Hyperbolic solved: 80.4374631680835 - [20250519_003817.]: Samplename: 87.5 + [20250519_004540.]: Samplename: 87.5 Root: 80.437 --> Root in between the borders! Added to results. Hyperbolic solved: 102.704019894026 - [20250519_003817.]: Samplename: 100 + [20250519_004540.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.704 --> '100 < root < 110' --> substitute 100 - [20250519_003817.]: Solving hyperbolic regression for CpG#4 + [20250519_004540.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: -0.519496428117613 - [20250519_003817.]: Samplename: 0 + [20250519_004540.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. @@ -16076,126 +16073,126 @@ Root: -0.519 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.4934173132007 - [20250519_003817.]: Samplename: 12.5 + [20250519_004540.]: Samplename: 12.5 Root: 12.493 --> Root in between the borders! Added to results. Hyperbolic solved: 24.2685417185317 - [20250519_003817.]: Samplename: 25 + [20250519_004540.]: Samplename: 25 Root: 24.269 --> Root in between the borders! Added to results. Hyperbolic solved: 38.0817103888785 - [20250519_003817.]: Samplename: 37.5 + [20250519_004540.]: Samplename: 37.5 Root: 38.082 --> Root in between the borders! Added to results. Hyperbolic solved: 48.5843148203973 - [20250519_003817.]: Samplename: 50 + [20250519_004540.]: Samplename: 50 Root: 48.584 --> Root in between the borders! Added to results. Hyperbolic solved: 67.672236894341 - [20250519_003817.]: Samplename: 62.5 + [20250519_004540.]: Samplename: 62.5 Root: 67.672 --> Root in between the borders! Added to results. Hyperbolic solved: 74.1549253766253 - [20250519_003817.]: Samplename: 75 + [20250519_004540.]: Samplename: 75 Root: 74.155 --> Root in between the borders! Added to results. Hyperbolic solved: 82.8821786440368 - [20250519_003817.]: Samplename: 87.5 + [20250519_004540.]: Samplename: 87.5 Root: 82.882 --> Root in between the borders! Added to results. Hyperbolic solved: 102.079130232402 - [20250519_003817.]: Samplename: 100 + [20250519_004540.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.079 --> '100 < root < 110' --> substitute 100 - [20250519_003817.]: Solving hyperbolic regression for CpG#5 + [20250519_004540.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 2.41558718123367 - [20250519_003817.]: Samplename: 0 + [20250519_004540.]: Samplename: 0 Root: 2.416 --> Root in between the borders! Added to results. Hyperbolic solved: 10.1649680545674 - [20250519_003817.]: Samplename: 12.5 + [20250519_004540.]: Samplename: 12.5 Root: 10.165 --> Root in between the borders! Added to results. Hyperbolic solved: 23.9830821073418 - [20250519_003817.]: Samplename: 25 + [20250519_004540.]: Samplename: 25 Root: 23.983 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2773617400679 - [20250519_003817.]: Samplename: 37.5 + [20250519_004540.]: Samplename: 37.5 Root: 37.277 --> Root in between the borders! Added to results. Hyperbolic solved: 50.8659382464167 - [20250519_003817.]: Samplename: 50 + [20250519_004540.]: Samplename: 50 Root: 50.866 --> Root in between the borders! Added to results. Hyperbolic solved: 62.4342269461158 - [20250519_003817.]: Samplename: 62.5 + [20250519_004540.]: Samplename: 62.5 Root: 62.434 --> Root in between the borders! Added to results. Hyperbolic solved: 76.3915257997575 - [20250519_003817.]: Samplename: 75 + [20250519_004540.]: Samplename: 75 Root: 76.392 --> Root in between the borders! Added to results. Hyperbolic solved: 86.1597887396996 - [20250519_003817.]: Samplename: 87.5 + [20250519_004540.]: Samplename: 87.5 Root: 86.16 --> Root in between the borders! Added to results. Hyperbolic solved: 100.267760316884 - [20250519_003817.]: Samplename: 100 + [20250519_004540.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.268 --> '100 < root < 110' --> substitute 100 - [20250519_003817.]: Solving hyperbolic regression for CpG#6 + [20250519_004540.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 0.138164161050506 - [20250519_003817.]: Samplename: 0 + [20250519_004540.]: Samplename: 0 Root: 0.138 --> Root in between the borders! Added to results. Hyperbolic solved: 11.8635561171135 - [20250519_003817.]: Samplename: 12.5 + [20250519_004540.]: Samplename: 12.5 Root: 11.864 --> Root in between the borders! Added to results. Hyperbolic solved: 26.510745006666 - [20250519_003817.]: Samplename: 25 + [20250519_004540.]: Samplename: 25 Root: 26.511 --> Root in between the borders! Added to results. Hyperbolic solved: 35.3205072777727 - [20250519_003817.]: Samplename: 37.5 + [20250519_004540.]: Samplename: 37.5 Root: 35.321 --> Root in between the borders! Added to results. Hyperbolic solved: 50.0570766444694 - [20250519_003817.]: Samplename: 50 + [20250519_004540.]: Samplename: 50 Root: 50.057 --> Root in between the borders! Added to results. Hyperbolic solved: 64.9602942986125 - [20250519_003817.]: Samplename: 62.5 + [20250519_004540.]: Samplename: 62.5 Root: 64.96 --> Root in between the borders! Added to results. Hyperbolic solved: 73.6689055886064 - [20250519_003817.]: Samplename: 75 + [20250519_004540.]: Samplename: 75 Root: 73.669 --> Root in between the borders! Added to results. Hyperbolic solved: 87.1266085894673 - [20250519_003817.]: Samplename: 87.5 + [20250519_004540.]: Samplename: 87.5 Root: 87.127 --> Root in between the borders! Added to results. Hyperbolic solved: 100.261637049252 - [20250519_003817.]: Samplename: 100 + [20250519_004540.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.262 --> '100 < root < 110' --> substitute 100 - [20250519_003817.]: Solving hyperbolic regression for CpG#7 + [20250519_004540.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: -1.37235519395329 - [20250519_003817.]: Samplename: 0 + [20250519_004540.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. @@ -16203,85 +16200,85 @@ Root: -1.372 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.1993294013604 - [20250519_003817.]: Samplename: 12.5 + [20250519_004540.]: Samplename: 12.5 Root: 10.199 --> Root in between the borders! Added to results. Hyperbolic solved: 24.5951803850766 - [20250519_003817.]: Samplename: 25 + [20250519_004540.]: Samplename: 25 Root: 24.595 --> Root in between the borders! Added to results. Hyperbolic solved: 37.8310364564335 - [20250519_003817.]: Samplename: 37.5 + [20250519_004540.]: Samplename: 37.5 Root: 37.831 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5588645818264 - [20250519_003817.]: Samplename: 50 + [20250519_004540.]: Samplename: 50 Root: 53.559 --> Root in between the borders! Added to results. Hyperbolic solved: 65.9364860153449 - [20250519_003817.]: Samplename: 62.5 + [20250519_004540.]: Samplename: 62.5 Root: 65.936 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7361033620359 - [20250519_003817.]: Samplename: 75 + [20250519_004540.]: Samplename: 75 Root: 75.736 --> Root in between the borders! Added to results. Hyperbolic solved: 79.4328192070151 - [20250519_003817.]: Samplename: 87.5 + [20250519_004540.]: Samplename: 87.5 Root: 79.433 --> Root in between the borders! Added to results. Hyperbolic solved: 103.004248773309 - [20250519_003817.]: Samplename: 100 + [20250519_004540.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 103.004 --> '100 < root < 110' --> substitute 100 - [20250519_003817.]: Solving hyperbolic regression for CpG#8 + [20250519_004540.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 2.80068093389159 - [20250519_003817.]: Samplename: 0 + [20250519_004540.]: Samplename: 0 Root: 2.801 --> Root in between the borders! Added to results. Hyperbolic solved: 9.27535066703723 - [20250519_003817.]: Samplename: 12.5 + [20250519_004540.]: Samplename: 12.5 Root: 9.275 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4762625870809 - [20250519_003817.]: Samplename: 25 + [20250519_004540.]: Samplename: 25 Root: 25.476 --> Root in between the borders! Added to results. Hyperbolic solved: 34.0122083314201 - [20250519_003817.]: Samplename: 37.5 + [20250519_004540.]: Samplename: 37.5 Root: 34.012 --> Root in between the borders! Added to results. Hyperbolic solved: 51.7842666342759 - [20250519_003817.]: Samplename: 50 + [20250519_004540.]: Samplename: 50 Root: 51.784 --> Root in between the borders! Added to results. Hyperbolic solved: 64.6732321058682 - [20250519_003817.]: Samplename: 62.5 + [20250519_004540.]: Samplename: 62.5 Root: 64.673 --> Root in between the borders! Added to results. Hyperbolic solved: 78.4326982875914 - [20250519_003817.]: Samplename: 75 + [20250519_004540.]: Samplename: 75 Root: 78.433 --> Root in between the borders! Added to results. Hyperbolic solved: 81.3427235319597 - [20250519_003817.]: Samplename: 87.5 + [20250519_004540.]: Samplename: 87.5 Root: 81.343 --> Root in between the borders! Added to results. Hyperbolic solved: 101.964405325155 - [20250519_003817.]: Samplename: 100 + [20250519_004540.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.964 --> '100 < root < 110' --> substitute 100 - [20250519_003817.]: Solving hyperbolic regression for CpG#9 + [20250519_004540.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: -2.1340328997008 - [20250519_003817.]: Samplename: 0 + [20250519_004540.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. @@ -16289,85 +16286,85 @@ Root: -2.134 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.5082212970134 - [20250519_003817.]: Samplename: 12.5 + [20250519_004540.]: Samplename: 12.5 Root: 10.508 --> Root in between the borders! Added to results. Hyperbolic solved: 26.9164566258777 - [20250519_003817.]: Samplename: 25 + [20250519_004540.]: Samplename: 25 Root: 26.916 --> Root in between the borders! Added to results. Hyperbolic solved: 36.8334769505904 - [20250519_003817.]: Samplename: 37.5 + [20250519_004540.]: Samplename: 37.5 Root: 36.833 --> Root in between the borders! Added to results. Hyperbolic solved: 52.0097879402751 - [20250519_003817.]: Samplename: 50 + [20250519_004540.]: Samplename: 50 Root: 52.01 --> Root in between the borders! Added to results. Hyperbolic solved: 64.893051148082 - [20250519_003817.]: Samplename: 62.5 + [20250519_004540.]: Samplename: 62.5 Root: 64.893 --> Root in between the borders! Added to results. Hyperbolic solved: 74.5671042786367 - [20250519_003817.]: Samplename: 75 + [20250519_004540.]: Samplename: 75 Root: 74.567 --> Root in between the borders! Added to results. Hyperbolic solved: 84.5294949210319 - [20250519_003817.]: Samplename: 87.5 + [20250519_004540.]: Samplename: 87.5 Root: 84.529 --> Root in between the borders! Added to results. Hyperbolic solved: 101.047147807246 - [20250519_003817.]: Samplename: 100 + [20250519_004540.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.047 --> '100 < root < 110' --> substitute 100 - [20250519_003817.]: Solving hyperbolic regression for row_means + [20250519_004540.]: Solving hyperbolic regression for row_means Hyperbolic solved: 0.290944901553815 - [20250519_003817.]: Samplename: 0 + [20250519_004540.]: Samplename: 0 Root: 0.291 --> Root in between the borders! Added to results. Hyperbolic solved: 11.0412427310408 - [20250519_003817.]: Samplename: 12.5 + [20250519_004540.]: Samplename: 12.5 Root: 11.041 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4081501338029 - [20250519_003817.]: Samplename: 25 + [20250519_004540.]: Samplename: 25 Root: 25.408 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5243709582085 - [20250519_003817.]: Samplename: 37.5 + [20250519_004540.]: Samplename: 37.5 Root: 36.524 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7348808712519 - [20250519_003817.]: Samplename: 50 + [20250519_004540.]: Samplename: 50 Root: 50.735 --> Root in between the borders! Added to results. Hyperbolic solved: 65.3135195123017 - [20250519_003817.]: Samplename: 62.5 + [20250519_004540.]: Samplename: 62.5 Root: 65.314 --> Root in between the borders! Added to results. Hyperbolic solved: 75.5342699494589 - [20250519_003817.]: Samplename: 75 + [20250519_004540.]: Samplename: 75 Root: 75.534 --> Root in between the borders! Added to results. Hyperbolic solved: 83.2411225127701 - [20250519_003817.]: Samplename: 87.5 + [20250519_004540.]: Samplename: 87.5 Root: 83.241 --> Root in between the borders! Added to results. Hyperbolic solved: 101.666944784378 - [20250519_003817.]: Samplename: 100 + [20250519_004540.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.667 --> '100 < root < 110' --> substitute 100 - [20250519_003817.]: + [20250519_004540.]: ### Starting with regression calculations ### - [20250519_003817.]: Entered 'regression_type1'-Function + [20250519_004540.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : @@ -17096,561 +17093,561 @@ step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient - [20250519_003817.]: # CpG-site: CpG#1 + [20250519_004540.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.1698564372971, 24.478190918656, 38.1730371634976, 52.3349263290239, 65.4582665283612, 75.0090705794847, 81.5271853400487, 100)c(0, 0.330143562702901, 0.521809081343999, 0.673037163497597, 2.3349263290239, 2.95826652836119, 0.00907057948469969, 5.9728146599513, 0)c(NA, 2.64114850162321, 2.087236325376, 1.79476576932692, 4.6698526580478, 4.73322644537791, 0.0120941059795996, 6.8260738970872, 0) - [20250519_003817.]: Logging df_agg: CpG#1 - [20250519_003817.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003817.]: c(0, 12.1698564372971, 24.478190918656, 38.1730371634976, 52.3349263290239, 65.4582665283612, 75.0090705794847, 81.5271853400487, 100)[20250519_003817.]: c(0, 0.330143562702901, 0.521809081343999, 0.673037163497597, 2.3349263290239, 2.95826652836119, 0.00907057948469969, 5.9728146599513, 0)[20250519_003817.]: c(NA, 2.64114850162321, 2.087236325376, 1.79476576932692, 4.6698526580478, 4.73322644537791, 0.0120941059795996, 6.8260738970872, 0) - [20250519_003817.]: Entered 'hyperbolic_regression'-Function - [20250519_003817.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003818.]: Entered 'cubic_regression'-Function - [20250519_003818.]: 'cubic_regression': minmax = FALSE - [20250519_003818.]: # CpG-site: CpG#2 + [20250519_004540.]: Logging df_agg: CpG#1 + [20250519_004540.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004540.]: c(0, 12.1698564372971, 24.478190918656, 38.1730371634976, 52.3349263290239, 65.4582665283612, 75.0090705794847, 81.5271853400487, 100)[20250519_004540.]: c(0, 0.330143562702901, 0.521809081343999, 0.673037163497597, 2.3349263290239, 2.95826652836119, 0.00907057948469969, 5.9728146599513, 0)[20250519_004540.]: c(NA, 2.64114850162321, 2.087236325376, 1.79476576932692, 4.6698526580478, 4.73322644537791, 0.0120941059795996, 6.8260738970872, 0) + [20250519_004540.]: Entered 'hyperbolic_regression'-Function + [20250519_004540.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004541.]: Entered 'cubic_regression'-Function + [20250519_004541.]: 'cubic_regression': minmax = FALSE + [20250519_004541.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.13660511681856, 11.4129696918445, 26.1740004612661, 35.1050448132614, 47.6855002074492, 67.1440493313574, 75.7644668045486, 84.4054158145455, 100)c(1.13660511681856, 1.0870303081555, 1.1740004612661, 2.3949551867386, 2.3144997925508, 4.64404933135739, 0.764466804548604, 3.09458418545449, 0)c(NA, 8.696242465244, 4.6960018450644, 6.38654716463626, 4.6289995851016, 7.43047893017183, 1.01928907273147, 3.53666764051942, 0) - [20250519_003818.]: Logging df_agg: CpG#2 - [20250519_003818.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003818.]: c(1.13660511681856, 11.4129696918445, 26.1740004612661, 35.1050448132614, 47.6855002074492, 67.1440493313574, 75.7644668045486, 84.4054158145455, 100)[20250519_003818.]: c(1.13660511681856, 1.0870303081555, 1.1740004612661, 2.3949551867386, 2.3144997925508, 4.64404933135739, 0.764466804548604, 3.09458418545449, 0)[20250519_003818.]: c(NA, 8.696242465244, 4.6960018450644, 6.38654716463626, 4.6289995851016, 7.43047893017183, 1.01928907273147, 3.53666764051942, 0) - [20250519_003818.]: Entered 'hyperbolic_regression'-Function - [20250519_003818.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003818.]: Entered 'cubic_regression'-Function - [20250519_003818.]: 'cubic_regression': minmax = FALSE - [20250519_003818.]: # CpG-site: CpG#3 + [20250519_004541.]: Logging df_agg: CpG#2 + [20250519_004541.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004541.]: c(1.13660511681856, 11.4129696918445, 26.1740004612661, 35.1050448132614, 47.6855002074492, 67.1440493313574, 75.7644668045486, 84.4054158145455, 100)[20250519_004541.]: c(1.13660511681856, 1.0870303081555, 1.1740004612661, 2.3949551867386, 2.3144997925508, 4.64404933135739, 0.764466804548604, 3.09458418545449, 0)[20250519_004541.]: c(NA, 8.696242465244, 4.6960018450644, 6.38654716463626, 4.6289995851016, 7.43047893017183, 1.01928907273147, 3.53666764051942, 0) + [20250519_004541.]: Entered 'hyperbolic_regression'-Function + [20250519_004541.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004541.]: Entered 'cubic_regression'-Function + [20250519_004541.]: 'cubic_regression': minmax = FALSE + [20250519_004541.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.512348561877098, 10.7523845008661, 25.5218912317424, 36.5270488279988, 50.7909283805111, 64.8686353441458, 77.5524208271734, 80.4374631680835, 100)c(0.512348561877098, 1.7476154991339, 0.521891231742401, 0.972951172001203, 0.790928380511097, 2.3686353441458, 2.55242082717341, 7.0625368319165, 0)c(NA, 13.9809239930712, 2.08756492696961, 2.59453645866987, 1.58185676102219, 3.78981655063328, 3.40322776956454, 8.07147066504743, 0) - [20250519_003818.]: Logging df_agg: CpG#3 - [20250519_003818.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003818.]: c(0.512348561877098, 10.7523845008661, 25.5218912317424, 36.5270488279988, 50.7909283805111, 64.8686353441458, 77.5524208271734, 80.4374631680835, 100)[20250519_003818.]: c(0.512348561877098, 1.7476154991339, 0.521891231742401, 0.972951172001203, 0.790928380511097, 2.3686353441458, 2.55242082717341, 7.0625368319165, 0)[20250519_003818.]: c(NA, 13.9809239930712, 2.08756492696961, 2.59453645866987, 1.58185676102219, 3.78981655063328, 3.40322776956454, 8.07147066504743, 0) - [20250519_003818.]: Entered 'hyperbolic_regression'-Function - [20250519_003818.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003818.]: Entered 'cubic_regression'-Function - [20250519_003818.]: 'cubic_regression': minmax = FALSE - [20250519_003818.]: # CpG-site: CpG#4 + [20250519_004541.]: Logging df_agg: CpG#3 + [20250519_004541.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004541.]: c(0.512348561877098, 10.7523845008661, 25.5218912317424, 36.5270488279988, 50.7909283805111, 64.8686353441458, 77.5524208271734, 80.4374631680835, 100)[20250519_004541.]: c(0.512348561877098, 1.7476154991339, 0.521891231742401, 0.972951172001203, 0.790928380511097, 2.3686353441458, 2.55242082717341, 7.0625368319165, 0)[20250519_004541.]: c(NA, 13.9809239930712, 2.08756492696961, 2.59453645866987, 1.58185676102219, 3.78981655063328, 3.40322776956454, 8.07147066504743, 0) + [20250519_004541.]: Entered 'hyperbolic_regression'-Function + [20250519_004541.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004541.]: Entered 'cubic_regression'-Function + [20250519_004541.]: 'cubic_regression': minmax = FALSE + [20250519_004541.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.4934173132007, 24.2685417185317, 38.0817103888785, 48.5843148203973, 67.672236894341, 74.1549253766253, 82.8821786440368, 100)c(0, 0.00658268679929996, 0.731458281468299, 0.581710388878498, 1.4156851796027, 5.172236894341, 0.8450746233747, 4.6178213559632, 0)c(NA, 0.0526614943943997, 2.9258331258732, 1.551227703676, 2.83137035920539, 8.27557903094559, 1.1267661644996, 5.2775101211008, 0) - [20250519_003818.]: Logging df_agg: CpG#4 - [20250519_003818.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003818.]: c(0, 12.4934173132007, 24.2685417185317, 38.0817103888785, 48.5843148203973, 67.672236894341, 74.1549253766253, 82.8821786440368, 100)[20250519_003818.]: c(0, 0.00658268679929996, 0.731458281468299, 0.581710388878498, 1.4156851796027, 5.172236894341, 0.8450746233747, 4.6178213559632, 0)[20250519_003818.]: c(NA, 0.0526614943943997, 2.9258331258732, 1.551227703676, 2.83137035920539, 8.27557903094559, 1.1267661644996, 5.2775101211008, 0) - [20250519_003818.]: Entered 'hyperbolic_regression'-Function - [20250519_003818.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003818.]: Entered 'cubic_regression'-Function - [20250519_003818.]: 'cubic_regression': minmax = FALSE - [20250519_003818.]: # CpG-site: CpG#5 + [20250519_004541.]: Logging df_agg: CpG#4 + [20250519_004541.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004541.]: c(0, 12.4934173132007, 24.2685417185317, 38.0817103888785, 48.5843148203973, 67.672236894341, 74.1549253766253, 82.8821786440368, 100)[20250519_004541.]: c(0, 0.00658268679929996, 0.731458281468299, 0.581710388878498, 1.4156851796027, 5.172236894341, 0.8450746233747, 4.6178213559632, 0)[20250519_004541.]: c(NA, 0.0526614943943997, 2.9258331258732, 1.551227703676, 2.83137035920539, 8.27557903094559, 1.1267661644996, 5.2775101211008, 0) + [20250519_004541.]: Entered 'hyperbolic_regression'-Function + [20250519_004541.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004541.]: Entered 'cubic_regression'-Function + [20250519_004541.]: 'cubic_regression': minmax = FALSE + [20250519_004541.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.41558718123367, 10.1649680545674, 23.9830821073418, 37.2773617400679, 50.8659382464167, 62.4342269461158, 76.3915257997575, 86.1597887396996, 100)c(2.41558718123367, 2.3350319454326, 1.0169178926582, 0.222638259932097, 0.865938246416697, 0.0657730538842003, 1.3915257997575, 1.34021126030041, 0)c(NA, 18.6802555634608, 4.0676715706328, 0.593702026485592, 1.73187649283339, 0.10523688621472, 1.85536773301, 1.53167001177189, 0) - [20250519_003818.]: Logging df_agg: CpG#5 - [20250519_003818.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003818.]: c(2.41558718123367, 10.1649680545674, 23.9830821073418, 37.2773617400679, 50.8659382464167, 62.4342269461158, 76.3915257997575, 86.1597887396996, 100)[20250519_003818.]: c(2.41558718123367, 2.3350319454326, 1.0169178926582, 0.222638259932097, 0.865938246416697, 0.0657730538842003, 1.3915257997575, 1.34021126030041, 0)[20250519_003818.]: c(NA, 18.6802555634608, 4.0676715706328, 0.593702026485592, 1.73187649283339, 0.10523688621472, 1.85536773301, 1.53167001177189, 0) - [20250519_003818.]: Entered 'hyperbolic_regression'-Function - [20250519_003818.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003819.]: Entered 'cubic_regression'-Function - [20250519_003819.]: 'cubic_regression': minmax = FALSE - [20250519_003819.]: # CpG-site: CpG#6 + [20250519_004541.]: Logging df_agg: CpG#5 + [20250519_004541.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004541.]: c(2.41558718123367, 10.1649680545674, 23.9830821073418, 37.2773617400679, 50.8659382464167, 62.4342269461158, 76.3915257997575, 86.1597887396996, 100)[20250519_004541.]: c(2.41558718123367, 2.3350319454326, 1.0169178926582, 0.222638259932097, 0.865938246416697, 0.0657730538842003, 1.3915257997575, 1.34021126030041, 0)[20250519_004541.]: c(NA, 18.6802555634608, 4.0676715706328, 0.593702026485592, 1.73187649283339, 0.10523688621472, 1.85536773301, 1.53167001177189, 0) + [20250519_004541.]: Entered 'hyperbolic_regression'-Function + [20250519_004541.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004542.]: Entered 'cubic_regression'-Function + [20250519_004542.]: 'cubic_regression': minmax = FALSE + [20250519_004542.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.138164161050506, 11.8635561171135, 26.510745006666, 35.3205072777727, 50.0570766444694, 64.9602942986125, 73.6689055886064, 87.1266085894673, 100)c(0.138164161050506, 0.6364438828865, 1.510745006666, 2.1794927222273, 0.0570766444693973, 2.4602942986125, 1.33109441139359, 0.373391410532705, 0)c(NA, 5.091551063092, 6.042980026664, 5.81198059260614, 0.114153288938795, 3.93647087777999, 1.77479254852479, 0.426733040608805, 0) - [20250519_003819.]: Logging df_agg: CpG#6 - [20250519_003819.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003819.]: c(0.138164161050506, 11.8635561171135, 26.510745006666, 35.3205072777727, 50.0570766444694, 64.9602942986125, 73.6689055886064, 87.1266085894673, 100)[20250519_003819.]: c(0.138164161050506, 0.6364438828865, 1.510745006666, 2.1794927222273, 0.0570766444693973, 2.4602942986125, 1.33109441139359, 0.373391410532705, 0)[20250519_003819.]: c(NA, 5.091551063092, 6.042980026664, 5.81198059260614, 0.114153288938795, 3.93647087777999, 1.77479254852479, 0.426733040608805, 0) - [20250519_003819.]: Entered 'hyperbolic_regression'-Function - [20250519_003819.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003819.]: Entered 'cubic_regression'-Function - [20250519_003819.]: 'cubic_regression': minmax = FALSE - [20250519_003819.]: # CpG-site: CpG#7 + [20250519_004542.]: Logging df_agg: CpG#6 + [20250519_004542.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004542.]: c(0.138164161050506, 11.8635561171135, 26.510745006666, 35.3205072777727, 50.0570766444694, 64.9602942986125, 73.6689055886064, 87.1266085894673, 100)[20250519_004542.]: c(0.138164161050506, 0.6364438828865, 1.510745006666, 2.1794927222273, 0.0570766444693973, 2.4602942986125, 1.33109441139359, 0.373391410532705, 0)[20250519_004542.]: c(NA, 5.091551063092, 6.042980026664, 5.81198059260614, 0.114153288938795, 3.93647087777999, 1.77479254852479, 0.426733040608805, 0) + [20250519_004542.]: Entered 'hyperbolic_regression'-Function + [20250519_004542.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004542.]: Entered 'cubic_regression'-Function + [20250519_004542.]: 'cubic_regression': minmax = FALSE + [20250519_004542.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.1993294013604, 24.5951803850766, 37.8310364564335, 53.5588645818264, 65.9364860153449, 75.7361033620359, 79.4328192070151, 100)c(0, 2.3006705986396, 0.404819614923401, 0.331036456433502, 3.5588645818264, 3.4364860153449, 0.736103362035905, 8.0671807929849, 0)c(NA, 18.4053647891168, 1.6192784596936, 0.882763883822671, 7.1177291636528, 5.49837762455184, 0.981471149381207, 9.21963519198274, 0) - [20250519_003819.]: Logging df_agg: CpG#7 - [20250519_003819.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003819.]: c(0, 10.1993294013604, 24.5951803850766, 37.8310364564335, 53.5588645818264, 65.9364860153449, 75.7361033620359, 79.4328192070151, 100)[20250519_003819.]: c(0, 2.3006705986396, 0.404819614923401, 0.331036456433502, 3.5588645818264, 3.4364860153449, 0.736103362035905, 8.0671807929849, 0)[20250519_003819.]: c(NA, 18.4053647891168, 1.6192784596936, 0.882763883822671, 7.1177291636528, 5.49837762455184, 0.981471149381207, 9.21963519198274, 0) - [20250519_003819.]: Entered 'hyperbolic_regression'-Function - [20250519_003819.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003819.]: Entered 'cubic_regression'-Function - [20250519_003819.]: 'cubic_regression': minmax = FALSE - [20250519_003819.]: # CpG-site: CpG#8 + [20250519_004542.]: Logging df_agg: CpG#7 + [20250519_004542.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004542.]: c(0, 10.1993294013604, 24.5951803850766, 37.8310364564335, 53.5588645818264, 65.9364860153449, 75.7361033620359, 79.4328192070151, 100)[20250519_004542.]: c(0, 2.3006705986396, 0.404819614923401, 0.331036456433502, 3.5588645818264, 3.4364860153449, 0.736103362035905, 8.0671807929849, 0)[20250519_004542.]: c(NA, 18.4053647891168, 1.6192784596936, 0.882763883822671, 7.1177291636528, 5.49837762455184, 0.981471149381207, 9.21963519198274, 0) + [20250519_004542.]: Entered 'hyperbolic_regression'-Function + [20250519_004542.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004542.]: Entered 'cubic_regression'-Function + [20250519_004542.]: 'cubic_regression': minmax = FALSE + [20250519_004542.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.80068093389159, 9.27535066703723, 25.4762625870809, 34.0122083314201, 51.7842666342759, 64.6732321058682, 78.4326982875914, 81.3427235319597, 100)c(2.80068093389159, 3.22464933296277, 0.4762625870809, 3.4877916685799, 1.7842666342759, 2.17323210586819, 3.43269828759141, 6.15727646804029, 0)c(NA, 25.7971946637022, 1.9050503483236, 9.30077778287974, 3.5685332685518, 3.47717136938911, 4.57693105012188, 7.03688739204605, 0) - [20250519_003819.]: Logging df_agg: CpG#8 - [20250519_003819.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003819.]: c(2.80068093389159, 9.27535066703723, 25.4762625870809, 34.0122083314201, 51.7842666342759, 64.6732321058682, 78.4326982875914, 81.3427235319597, 100)[20250519_003819.]: c(2.80068093389159, 3.22464933296277, 0.4762625870809, 3.4877916685799, 1.7842666342759, 2.17323210586819, 3.43269828759141, 6.15727646804029, 0)[20250519_003819.]: c(NA, 25.7971946637022, 1.9050503483236, 9.30077778287974, 3.5685332685518, 3.47717136938911, 4.57693105012188, 7.03688739204605, 0) - [20250519_003819.]: Entered 'hyperbolic_regression'-Function - [20250519_003819.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003819.]: Entered 'cubic_regression'-Function - [20250519_003819.]: 'cubic_regression': minmax = FALSE - [20250519_003819.]: # CpG-site: CpG#9 + [20250519_004542.]: Logging df_agg: CpG#8 + [20250519_004542.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004542.]: c(2.80068093389159, 9.27535066703723, 25.4762625870809, 34.0122083314201, 51.7842666342759, 64.6732321058682, 78.4326982875914, 81.3427235319597, 100)[20250519_004542.]: c(2.80068093389159, 3.22464933296277, 0.4762625870809, 3.4877916685799, 1.7842666342759, 2.17323210586819, 3.43269828759141, 6.15727646804029, 0)[20250519_004542.]: c(NA, 25.7971946637022, 1.9050503483236, 9.30077778287974, 3.5685332685518, 3.47717136938911, 4.57693105012188, 7.03688739204605, 0) + [20250519_004542.]: Entered 'hyperbolic_regression'-Function + [20250519_004542.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004542.]: Entered 'cubic_regression'-Function + [20250519_004542.]: 'cubic_regression': minmax = FALSE + [20250519_004542.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.5082212970134, 26.9164566258777, 36.8334769505904, 52.0097879402751, 64.893051148082, 74.5671042786367, 84.5294949210319, 100)c(0, 1.9917787029866, 1.9164566258777, 0.666523049409598, 2.0097879402751, 2.39305114808199, 0.432895721363295, 2.9705050789681, 0)c(NA, 15.9342296238928, 7.6658265035108, 1.77739479842559, 4.01957588055021, 3.82888183693119, 0.57719429515106, 3.39486294739212, 0) - [20250519_003819.]: Logging df_agg: CpG#9 - [20250519_003819.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003819.]: c(0, 10.5082212970134, 26.9164566258777, 36.8334769505904, 52.0097879402751, 64.893051148082, 74.5671042786367, 84.5294949210319, 100)[20250519_003819.]: c(0, 1.9917787029866, 1.9164566258777, 0.666523049409598, 2.0097879402751, 2.39305114808199, 0.432895721363295, 2.9705050789681, 0)[20250519_003819.]: c(NA, 15.9342296238928, 7.6658265035108, 1.77739479842559, 4.01957588055021, 3.82888183693119, 0.57719429515106, 3.39486294739212, 0) - [20250519_003819.]: Entered 'hyperbolic_regression'-Function - [20250519_003819.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003820.]: Entered 'cubic_regression'-Function - [20250519_003820.]: 'cubic_regression': minmax = FALSE - [20250519_003820.]: # CpG-site: row_means + [20250519_004542.]: Logging df_agg: CpG#9 + [20250519_004542.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004542.]: c(0, 10.5082212970134, 26.9164566258777, 36.8334769505904, 52.0097879402751, 64.893051148082, 74.5671042786367, 84.5294949210319, 100)[20250519_004542.]: c(0, 1.9917787029866, 1.9164566258777, 0.666523049409598, 2.0097879402751, 2.39305114808199, 0.432895721363295, 2.9705050789681, 0)[20250519_004542.]: c(NA, 15.9342296238928, 7.6658265035108, 1.77739479842559, 4.01957588055021, 3.82888183693119, 0.57719429515106, 3.39486294739212, 0) + [20250519_004542.]: Entered 'hyperbolic_regression'-Function + [20250519_004542.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004543.]: Entered 'cubic_regression'-Function + [20250519_004543.]: 'cubic_regression': minmax = FALSE + [20250519_004543.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.290944901553815, 11.0412427310408, 25.4081501338029, 36.5243709582085, 50.7348808712519, 65.3135195123017, 75.5342699494589, 83.2411225127701, 100)c(0.290944901553815, 1.4587572689592, 0.408150133802899, 0.9756290417915, 0.734880871251903, 2.8135195123017, 0.534269949458903, 4.2588774872299, 0)c(NA, 11.6700581516736, 1.63260053521159, 2.60167744477733, 1.46976174250381, 4.50163121968271, 0.71235993261187, 4.86728855683417, 0) - [20250519_003820.]: Logging df_agg: row_means - [20250519_003820.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003820.]: c(0.290944901553815, 11.0412427310408, 25.4081501338029, 36.5243709582085, 50.7348808712519, 65.3135195123017, 75.5342699494589, 83.2411225127701, 100)[20250519_003820.]: c(0.290944901553815, 1.4587572689592, 0.408150133802899, 0.9756290417915, 0.734880871251903, 2.8135195123017, 0.534269949458903, 4.2588774872299, 0)[20250519_003820.]: c(NA, 11.6700581516736, 1.63260053521159, 2.60167744477733, 1.46976174250381, 4.50163121968271, 0.71235993261187, 4.86728855683417, 0) - [20250519_003820.]: Entered 'hyperbolic_regression'-Function - [20250519_003820.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003820.]: Entered 'cubic_regression'-Function - [20250519_003820.]: 'cubic_regression': minmax = FALSE - [20250519_003820.]: ### Starting with plotting ### - [20250519_003821.]: Creating BiasCorrected (hyperbolic) plot No. 1 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG1_corrected_h.png - [20250519_003821.]: # CpG-site: CpG#1 + [20250519_004543.]: Logging df_agg: row_means + [20250519_004543.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004543.]: c(0.290944901553815, 11.0412427310408, 25.4081501338029, 36.5243709582085, 50.7348808712519, 65.3135195123017, 75.5342699494589, 83.2411225127701, 100)[20250519_004543.]: c(0.290944901553815, 1.4587572689592, 0.408150133802899, 0.9756290417915, 0.734880871251903, 2.8135195123017, 0.534269949458903, 4.2588774872299, 0)[20250519_004543.]: c(NA, 11.6700581516736, 1.63260053521159, 2.60167744477733, 1.46976174250381, 4.50163121968271, 0.71235993261187, 4.86728855683417, 0) + [20250519_004543.]: Entered 'hyperbolic_regression'-Function + [20250519_004543.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004543.]: Entered 'cubic_regression'-Function + [20250519_004543.]: 'cubic_regression': minmax = FALSE + [20250519_004543.]: ### Starting with plotting ### + [20250519_004544.]: Creating BiasCorrected (hyperbolic) plot No. 1 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG1_corrected_h.png + [20250519_004544.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -657.85070009309 , a = 985.271206705952 , b = -657.85070009309 , d = 903.457967400565 - [20250519_003821.]: # CpG-site: CpG#1 + [20250519_004544.]: # CpG-site: CpG#1 Cubic: Using a = 5.1847317144537e-06 , b = -0.00180593132520811 , c = 1.11404026275692 , d = -0.858597345049636 - [20250519_003822.]: Creating BiasCorrected (hyperbolic) plot No. 2 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG2_corrected_h.png - [20250519_003822.]: # CpG-site: CpG#2 + [20250519_004545.]: Creating BiasCorrected (hyperbolic) plot No. 2 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG2_corrected_h.png + [20250519_004545.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = 3399.63077807263 , a = 30276.8974003894 , b = 3399.63077807263 , d = 30353.3579426379 - [20250519_003822.]: # CpG-site: CpG#2 + [20250519_004545.]: # CpG-site: CpG#2 Cubic: Using a = -2.58097552031906e-05 , b = 0.00383753288450718 , c = 0.851607751869747 , d = 0.95712734045721 - [20250519_003823.]: Creating BiasCorrected (hyperbolic) plot No. 3 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG3_corrected_h.png - [20250519_003823.]: # CpG-site: CpG#3 + [20250519_004546.]: Creating BiasCorrected (hyperbolic) plot No. 3 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG3_corrected_h.png + [20250519_004546.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = -945.763900328712 , a = 1333.61328542459 , b = -945.763900328712 , d = 1252.95190710086 - [20250519_003823.]: # CpG-site: CpG#3 + [20250519_004546.]: # CpG-site: CpG#3 Cubic: Using a = -1.13948418252831e-05 , b = 0.000937174314708123 , c = 0.998631105115713 , d = -0.383346041194064 - [20250519_003825.]: Creating BiasCorrected (hyperbolic) plot No. 4 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG4_corrected_h.png - [20250519_003825.]: # CpG-site: CpG#4 + [20250519_004548.]: Creating BiasCorrected (hyperbolic) plot No. 4 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG4_corrected_h.png + [20250519_004548.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = -754.050808709341 , a = 1637.26986382636 , b = -754.050808709341 , d = 1558.27566152171 - [20250519_003825.]: # CpG-site: CpG#4 + [20250519_004548.]: # CpG-site: CpG#4 Cubic: Using a = -3.25777472656452e-06 , b = -0.000129598485618069 , c = 1.03077245158202 , d = -0.370342672245111 - [20250519_003826.]: Creating BiasCorrected (hyperbolic) plot No. 5 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG5_corrected_h.png - [20250519_003826.]: # CpG-site: CpG#5 + [20250519_004549.]: Creating BiasCorrected (hyperbolic) plot No. 5 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG5_corrected_h.png + [20250519_004549.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = -1917.44470782279 , a = -5621.43051220384 , b = -1917.44470782279 , d = -5734.45004174505 - [20250519_003826.]: # CpG-site: CpG#5 + [20250519_004549.]: # CpG-site: CpG#5 Cubic: Using a = -3.16782847952728e-05 , b = 0.00493527243554485 , c = 0.800194551229801 , d = 1.38653114075915 - [20250519_003828.]: Creating BiasCorrected (hyperbolic) plot No. 6 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG6_corrected_h.png - [20250519_003828.]: # CpG-site: CpG#6 + [20250519_004551.]: Creating BiasCorrected (hyperbolic) plot No. 6 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG6_corrected_h.png + [20250519_004551.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = -1255.66539296107 , a = 13775.5132875201 , b = -1255.66539296107 , d = 13690.4025642689 - [20250519_003828.]: # CpG-site: CpG#6 + [20250519_004551.]: # CpG-site: CpG#6 Cubic: Using a = -3.70201699726522e-06 , b = 0.000482210843999232 , c = 0.98529311552838 , d = 0.0293572853154747 - [20250519_003829.]: Creating BiasCorrected (hyperbolic) plot No. 7 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG7_corrected_h.png - [20250519_003829.]: # CpG-site: CpG#7 + [20250519_004552.]: Creating BiasCorrected (hyperbolic) plot No. 7 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG7_corrected_h.png + [20250519_004552.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -1065.05219601462 , a = 722.085443879134 , b = -1065.05219601462 , d = 634.403611801521 - [20250519_003829.]: # CpG-site: CpG#7 + [20250519_004552.]: # CpG-site: CpG#7 Cubic: Using a = -1.04101209223167e-06 , b = -0.00130151331776721 , c = 1.1239953084199 , d = -1.59858561306358 - [20250519_003831.]: Creating BiasCorrected (hyperbolic) plot No. 8 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG8_corrected_h.png - [20250519_003831.]: # CpG-site: CpG#8 + [20250519_004554.]: Creating BiasCorrected (hyperbolic) plot No. 8 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG8_corrected_h.png + [20250519_004554.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = 68.5259802694693 , a = 4784.81913306904 , b = 68.5259802694693 , d = 4739.03101269703 - [20250519_003831.]: # CpG-site: CpG#8 + [20250519_004554.]: # CpG-site: CpG#8 Cubic: Using a = -4.65730461761547e-05 , b = 0.00676525443932464 , c = 0.747405842988874 , d = 1.5233701793389 - [20250519_003832.]: Creating BiasCorrected (hyperbolic) plot No. 9 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG9_corrected_h.png - [20250519_003832.]: # CpG-site: CpG#9 + [20250519_004555.]: Creating BiasCorrected (hyperbolic) plot No. 9 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG9_corrected_h.png + [20250519_004555.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -1022.70071430348 , a = 1273.73160369723 , b = -1022.70071430348 , d = 1181.86620562781 - [20250519_003832.]: # CpG-site: CpG#9 + [20250519_004555.]: # CpG-site: CpG#9 Cubic: Using a = 4.24791805783875e-06 , b = -0.00144169825546529 , c = 1.09837792836035 , d = -0.978987254622136 - [20250519_003834.]: Creating BiasCorrected (hyperbolic) plot No. 10 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_rowmeans_corrected_h.png - [20250519_003834.]: # CpG-site: row_means + [20250519_004557.]: Creating BiasCorrected (hyperbolic) plot No. 10 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_rowmeans_corrected_h.png + [20250519_004557.]: # CpG-site: row_means Hyperbolic: Using bias_weight = -1166.73385465972 , a = 1740.64188131784 , b = -1166.73385465972 , d = 1653.86486365297 - [20250519_003834.]: # CpG-site: row_means + [20250519_004557.]: # CpG-site: row_means Cubic: Using a = -8.71187897501959e-06 , b = 0.000715556896368103 , c = 1.00248226010187 , d = -0.420549765740683 - [20250519_003835.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG1_corrected_h.png - [20250519_003837.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG2_corrected_h.png - [20250519_003838.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG3_corrected_h.png - [20250519_003839.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG4_corrected_h.png - [20250519_003841.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG5_corrected_h.png - [20250519_003842.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG6_corrected_h.png - [20250519_003843.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG7_corrected_h.png - [20250519_003845.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG8_corrected_h.png - [20250519_003846.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG9_corrected_h.png - [20250519_003847.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_rowmeans_corrected_h.png - [20250519_003848.]: Entered 'solving_equations'-Function - [20250519_003848.]: Solving cubic regression for CpG#1 + [20250519_004558.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG1_corrected_h.png + [20250519_004600.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG2_corrected_h.png + [20250519_004601.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG3_corrected_h.png + [20250519_004602.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG4_corrected_h.png + [20250519_004603.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG5_corrected_h.png + [20250519_004605.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG6_corrected_h.png + [20250519_004606.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG7_corrected_h.png + [20250519_004607.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG8_corrected_h.png + [20250519_004609.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG9_corrected_h.png + [20250519_004610.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_rowmeans_corrected_h.png + [20250519_004611.]: Entered 'solving_equations'-Function + [20250519_004611.]: Solving cubic regression for CpG#1 Coefficients: -1.03617340067344Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_003848.]: Samplename: 0 + [20250519_004611.]: Samplename: 0 Root: 1.334 --> Root in between the borders! Added to results. Coefficients: -8.34150673400678Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_003848.]: Samplename: 12.5 + [20250519_004611.]: Samplename: 12.5 Root: 11.446 --> Root in between the borders! Added to results. Coefficients: -15.3881734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_003848.]: Samplename: 25 + [20250519_004611.]: Samplename: 25 Root: 22.228 --> Root in between the borders! Added to results. Coefficients: -24.2801734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_003848.]: Samplename: 37.5 + [20250519_004611.]: Samplename: 37.5 Root: 36.374 --> Root in between the borders! Added to results. Coefficients: -34.9006734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_003848.]: Samplename: 50 + [20250519_004611.]: Samplename: 50 Root: 52.044 --> Root in between the borders! Added to results. Coefficients: -46.3541734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_003848.]: Samplename: 62.5 + [20250519_004611.]: Samplename: 62.5 Root: 66.144 --> Root in between the borders! Added to results. Coefficients: -55.8931734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_003848.]: Samplename: 75 + [20250519_004611.]: Samplename: 75 Root: 75.864 --> Root in between the borders! Added to results. Coefficients: -63.0981734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_003848.]: Samplename: 87.5 + [20250519_004611.]: Samplename: 87.5 Root: 82.254 --> Root in between the borders! Added to results. Coefficients: -91.0461734006735Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_003848.]: Samplename: 100 + [20250519_004611.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.877 --> '100 < root < 110' --> substitute 100 - [20250519_003848.]: Solving cubic regression for CpG#2 + [20250519_004611.]: Solving cubic regression for CpG#2 Coefficients: -0.283329966329966Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_003848.]: Samplename: 0 + [20250519_004611.]: Samplename: 0 Root: 0.549 --> Root in between the borders! Added to results. Coefficients: -6.33999663299663Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_003848.]: Samplename: 12.5 + [20250519_004611.]: Samplename: 12.5 Root: 11.533 --> Root in between the borders! Added to results. Coefficients: -15.93932996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_003848.]: Samplename: 25 + [20250519_004611.]: Samplename: 25 Root: 26.628 --> Root in between the borders! Added to results. Coefficients: -22.33732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_003848.]: Samplename: 37.5 + [20250519_004611.]: Samplename: 37.5 Root: 35.509 --> Root in between the borders! Added to results. Coefficients: -32.22832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_003848.]: Samplename: 50 + [20250519_004611.]: Samplename: 50 Root: 47.851 --> Root in between the borders! Added to results. Coefficients: -49.96332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_003848.]: Samplename: 62.5 + [20250519_004611.]: Samplename: 62.5 Root: 66.893 --> Root in between the borders! Added to results. Coefficients: -58.96582996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_003848.]: Samplename: 75 + [20250519_004611.]: Samplename: 75 Root: 75.431 --> Root in between the borders! Added to results. Coefficients: -68.8366632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_003848.]: Samplename: 87.5 + [20250519_004611.]: Samplename: 87.5 Root: 84.118 --> Root in between the borders! Added to results. Coefficients: -90.57732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_003849.]: Samplename: 100 + [20250519_004611.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.287 --> '100 < root < 110' --> substitute 100 - [20250519_003849.]: Solving cubic regression for CpG#3 + [20250519_004611.]: Solving cubic regression for CpG#3 Coefficients: -0.90294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_003849.]: Samplename: 0 + [20250519_004611.]: Samplename: 0 Root: 1.441 --> Root in between the borders! Added to results. Coefficients: -6.57294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_003849.]: Samplename: 12.5 + [20250519_004611.]: Samplename: 12.5 Root: 10.568 --> Root in between the borders! Added to results. Coefficients: -15.4289478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_003849.]: Samplename: 25 + [20250519_004611.]: Samplename: 25 Root: 24.796 --> Root in between the borders! Added to results. Coefficients: -22.6129478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_003849.]: Samplename: 37.5 + [20250519_004611.]: Samplename: 37.5 Root: 35.952 --> Root in between the borders! Added to results. Coefficients: -32.7754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_003849.]: Samplename: 50 + [20250519_004611.]: Samplename: 50 Root: 50.684 --> Root in between the borders! Added to results. Coefficients: -43.8889478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_003849.]: Samplename: 62.5 + [20250519_004611.]: Samplename: 62.5 Root: 65.142 --> Root in between the borders! Added to results. Coefficients: -54.9754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_003849.]: Samplename: 75 + [20250519_004611.]: Samplename: 75 Root: 77.905 --> Root in between the borders! Added to results. Coefficients: -57.6562811447812Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_003849.]: Samplename: 87.5 + [20250519_004611.]: Samplename: 87.5 Root: 80.767 --> Root in between the borders! Added to results. Coefficients: -80.6649478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_003849.]: Samplename: 100 + [20250519_004611.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.38 --> '100 < root < 110' --> substitute 100 - [20250519_003849.]: Solving cubic regression for CpG#4 + [20250519_004611.]: Solving cubic regression for CpG#4 Coefficients: -0.597449494949524Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_003849.]: Samplename: 0 + [20250519_004611.]: Samplename: 0 Root: 0.858 --> Root in between the borders! Added to results. Coefficients: -8.25278282828286Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_003849.]: Samplename: 12.5 + [20250519_004611.]: Samplename: 12.5 Root: 12.086 --> Root in between the borders! Added to results. Coefficients: -15.8034494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_003849.]: Samplename: 25 + [20250519_004611.]: Samplename: 25 Root: 23.316 --> Root in between the borders! Added to results. Coefficients: -25.5274494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_003849.]: Samplename: 37.5 + [20250519_004611.]: Samplename: 37.5 Root: 37.383 --> Root in between the borders! Added to results. Coefficients: -33.6369494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_003849.]: Samplename: 50 + [20250519_004611.]: Samplename: 50 Root: 48.353 --> Root in between the borders! Added to results. Coefficients: -50.2554494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_003849.]: Samplename: 62.5 + [20250519_004611.]: Samplename: 62.5 Root: 68.082 --> Root in between the borders! Added to results. Coefficients: -56.5394494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_003849.]: Samplename: 75 + [20250519_004611.]: Samplename: 75 Root: 74.615 --> Root in between the borders! Added to results. Coefficients: -65.5927828282829Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_003849.]: Samplename: 87.5 + [20250519_004611.]: Samplename: 87.5 Root: 83.254 --> Root in between the borders! Added to results. Coefficients: -88.3214494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_003849.]: Samplename: 100 + [20250519_004611.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.715 --> '100 < root < 110' --> substitute 100 - [20250519_003849.]: Solving cubic regression for CpG#5 + [20250519_004611.]: Solving cubic regression for CpG#5 Coefficients: -0.623961279461278Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_003849.]: Samplename: 0 + [20250519_004611.]: Samplename: 0 Root: 1.458 --> Root in between the borders! Added to results. Coefficients: -4.76796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_003849.]: Samplename: 12.5 + [20250519_004611.]: Samplename: 12.5 Root: 10.347 --> Root in between the borders! Added to results. Coefficients: -12.7259612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_003849.]: Samplename: 25 + [20250519_004611.]: Samplename: 25 Root: 24.815 --> Root in between the borders! Added to results. Coefficients: -21.1599612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_003849.]: Samplename: 37.5 + [20250519_004611.]: Samplename: 37.5 Root: 37.902 --> Root in between the borders! Added to results. Coefficients: -30.6954612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_003849.]: Samplename: 50 + [20250519_004611.]: Samplename: 50 Root: 50.977 --> Root in between the borders! Added to results. Coefficients: -39.6579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_003849.]: Samplename: 62.5 + [20250519_004611.]: Samplename: 62.5 Root: 62.126 --> Root in between the borders! Added to results. Coefficients: -51.6829612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_003849.]: Samplename: 75 + [20250519_004611.]: Samplename: 75 Root: 75.852 --> Root in between the borders! Added to results. Coefficients: -61.0146279461279Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_003849.]: Samplename: 87.5 + [20250519_004611.]: Samplename: 87.5 Root: 85.767 --> Root in between the borders! Added to results. Coefficients: -76.0699612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_003849.]: Samplename: 100 + [20250519_004611.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.743 --> '100 < root < 110' --> substitute 100 - [20250519_003849.]: Solving cubic regression for CpG#6 + [20250519_004611.]: Solving cubic regression for CpG#6 Coefficients: -0.196072390572403Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_003849.]: Samplename: 0 + [20250519_004611.]: Samplename: 0 Root: 0.349 --> Root in between the borders! Added to results. Coefficients: -6.73873905723907Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_003849.]: Samplename: 12.5 + [20250519_004611.]: Samplename: 12.5 Root: 11.718 --> Root in between the borders! Added to results. Coefficients: -15.8880723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_003849.]: Samplename: 25 + [20250519_004611.]: Samplename: 25 Root: 26.396 --> Root in between the borders! Added to results. Coefficients: -22.0000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_003849.]: Samplename: 37.5 + [20250519_004611.]: Samplename: 37.5 Root: 35.301 --> Root in between the borders! Added to results. Coefficients: -33.4445723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_003849.]: Samplename: 50 + [20250519_004611.]: Samplename: 50 Root: 50.134 --> Root in between the borders! Added to results. Coefficients: -46.9000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_003849.]: Samplename: 62.5 + [20250519_004611.]: Samplename: 62.5 Root: 64.993 --> Root in between the borders! Added to results. Coefficients: -55.8320723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_003849.]: Samplename: 75 + [20250519_004611.]: Samplename: 75 Root: 73.639 --> Root in between the borders! Added to results. Coefficients: -71.5454057239057Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_003849.]: Samplename: 87.5 + [20250519_004611.]: Samplename: 87.5 Root: 87.043 --> Root in between the borders! Added to results. Coefficients: -89.6560723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_003849.]: Samplename: 100 + [20250519_004611.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.329 --> '100 < root < 110' --> substitute 100 - [20250519_003849.]: Solving cubic regression for CpG#7 + [20250519_004611.]: Solving cubic regression for CpG#7 Coefficients: -1.21495454545456Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_003849.]: Samplename: 0 + [20250519_004611.]: Samplename: 0 Root: 2.13 --> Root in between the borders! Added to results. Coefficients: -5.39562121212123Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_003849.]: Samplename: 12.5 + [20250519_004611.]: Samplename: 12.5 Root: 9.973 --> Root in between the borders! Added to results. Coefficients: -11.2649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_003849.]: Samplename: 25 + [20250519_004611.]: Samplename: 25 Root: 22.206 --> Root in between the borders! Added to results. Coefficients: -17.4509545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_003849.]: Samplename: 37.5 + [20250519_004611.]: Samplename: 37.5 Root: 35.814 --> Root in between the borders! Added to results. Coefficients: -26.0314545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_003849.]: Samplename: 50 + [20250519_004611.]: Samplename: 50 Root: 53.28 --> Root in between the borders! Added to results. Coefficients: -33.9649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_003849.]: Samplename: 62.5 + [20250519_004611.]: Samplename: 62.5 Root: 66.598 --> Root in between the borders! Added to results. Coefficients: -41.1689545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_003849.]: Samplename: 75 + [20250519_004611.]: Samplename: 75 Root: 76.575 --> Root in between the borders! Added to results. Coefficients: -44.1356212121212Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_003849.]: Samplename: 87.5 + [20250519_004611.]: Samplename: 87.5 Root: 80.219 --> Root in between the borders! Added to results. Coefficients: -67.2229545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_003849.]: Samplename: 100 + [20250519_004611.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.506 --> '100 < root < 110' --> substitute 100 - [20250519_003849.]: Solving cubic regression for CpG#8 + [20250519_004611.]: Solving cubic regression for CpG#8 Coefficients: -1.09618518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_003849.]: Samplename: 0 + [20250519_004611.]: Samplename: 0 Root: 2.016 --> Root in between the borders! Added to results. Coefficients: -5.44685185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_003849.]: Samplename: 12.5 + [20250519_004611.]: Samplename: 12.5 Root: 9.458 --> Root in between the borders! Added to results. Coefficients: -16.9301851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_003849.]: Samplename: 25 + [20250519_004611.]: Samplename: 25 Root: 26.35 --> Root in between the borders! Added to results. Coefficients: -23.3501851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_003849.]: Samplename: 37.5 + [20250519_004611.]: Samplename: 37.5 Root: 34.728 --> Root in between the borders! Added to results. Coefficients: -37.6251851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_003849.]: Samplename: 50 + [20250519_004611.]: Samplename: 50 Root: 51.781 --> Root in between the borders! Added to results. Coefficients: -48.8261851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_003849.]: Samplename: 62.5 + [20250519_004611.]: Samplename: 62.5 Root: 64.192 --> Root in between the borders! Added to results. Coefficients: -61.6676851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_003849.]: Samplename: 75 + [20250519_004611.]: Samplename: 75 Root: 77.804 --> Root in between the borders! Added to results. Coefficients: -64.5101851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_003849.]: Samplename: 87.5 + [20250519_004611.]: Samplename: 87.5 Root: 80.758 --> Root in between the borders! Added to results. Coefficients: -86.0601851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_003849.]: Samplename: 100 + [20250519_004611.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.834 --> '100 < root < 110' --> substitute 100 - [20250519_003849.]: Solving cubic regression for CpG#9 + [20250519_004611.]: Solving cubic regression for CpG#9 Coefficients: -0.989865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_003849.]: Samplename: 0 + [20250519_004611.]: Samplename: 0 Root: 1.475 --> Root in between the borders! Added to results. Coefficients: -6.39586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_003849.]: Samplename: 12.5 + [20250519_004611.]: Samplename: 12.5 Root: 10.12 --> Root in between the borders! Added to results. Coefficients: -14.7058653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_003849.]: Samplename: 25 + [20250519_004611.]: Samplename: 25 Root: 24.844 --> Root in between the borders! Added to results. Coefficients: -20.6238653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_003849.]: Samplename: 37.5 + [20250519_004611.]: Samplename: 37.5 Root: 35.327 --> Root in between the borders! Added to results. Coefficients: -31.3958653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_003849.]: Samplename: 50 + [20250519_004611.]: Samplename: 50 Root: 51.855 --> Root in between the borders! Added to results. Coefficients: -42.6858653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_003849.]: Samplename: 62.5 + [20250519_004611.]: Samplename: 62.5 Root: 65.265 --> Root in between the borders! Added to results. Coefficients: -52.9033653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_003849.]: Samplename: 75 + [20250519_004611.]: Samplename: 75 Root: 74.915 --> Root in between the borders! Added to results. Coefficients: -65.492531986532Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_003849.]: Samplename: 87.5 + [20250519_004611.]: Samplename: 87.5 Root: 84.67 --> Root in between the borders! Added to results. Coefficients: -92.9898653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_003849.]: Samplename: 100 + [20250519_004611.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.082 --> '100 < root < 110' --> substitute 100 - [20250519_003849.]: Solving cubic regression for row_means + [20250519_004611.]: Solving cubic regression for row_means Coefficients: -0.771215488215478Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_003849.]: Samplename: 0 + [20250519_004611.]: Samplename: 0 Root: 1.287 --> Root in between the borders! Added to results. Coefficients: -6.47247474747474Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_003849.]: Samplename: 12.5 + [20250519_004611.]: Samplename: 12.5 Root: 10.847 --> Root in between the borders! Added to results. Coefficients: -14.8972154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_003849.]: Samplename: 25 + [20250519_004611.]: Samplename: 25 Root: 24.737 --> Root in between the borders! Added to results. Coefficients: -22.1492154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_003849.]: Samplename: 37.5 + [20250519_004611.]: Samplename: 37.5 Root: 36.02 --> Root in between the borders! Added to results. Coefficients: -32.5259932659933Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_003849.]: Samplename: 50 + [20250519_004611.]: Samplename: 50 Root: 50.639 --> Root in between the borders! Added to results. Coefficients: -44.7218821548821Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_003849.]: Samplename: 62.5 + [20250519_004611.]: Samplename: 62.5 Root: 65.497 --> Root in between the borders! Added to results. Coefficients: -54.4032154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_003849.]: Samplename: 75 + [20250519_004611.]: Samplename: 75 Root: 75.751 --> Root in between the borders! Added to results. Coefficients: -62.4313636363636Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_003849.]: Samplename: 87.5 + [20250519_004611.]: Samplename: 87.5 Root: 83.403 --> Root in between the borders! Added to results. Coefficients: -84.7343265993266Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_003849.]: Samplename: 100 + [20250519_004611.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.573 --> '100 < root < 110' --> substitute 100 - [20250519_003849.]: + [20250519_004611.]: ### Starting with regression calculations ### - [20250519_003849.]: Entered 'regression_type1'-Function + [20250519_004611.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : @@ -18385,3028 +18382,3028 @@ step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient - [20250519_003849.]: # CpG-site: CpG#1 + [20250519_004612.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) - [20250519_003849.]: Logging df_agg: CpG#1 - [20250519_003849.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003849.]: c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)[20250519_003849.]: c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)[20250519_003849.]: c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) - [20250519_003849.]: Entered 'hyperbolic_regression'-Function - [20250519_003849.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003849.]: Entered 'cubic_regression'-Function - [20250519_003849.]: 'cubic_regression': minmax = FALSE - [20250519_003849.]: # CpG-site: CpG#2 + [20250519_004612.]: Logging df_agg: CpG#1 + [20250519_004612.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004612.]: c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)[20250519_004612.]: c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)[20250519_004612.]: c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) + [20250519_004612.]: Entered 'hyperbolic_regression'-Function + [20250519_004612.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004612.]: Entered 'cubic_regression'-Function + [20250519_004612.]: 'cubic_regression': minmax = FALSE + [20250519_004612.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) - [20250519_003849.]: Logging df_agg: CpG#2 - [20250519_003849.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003849.]: c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)[20250519_003849.]: c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)[20250519_003849.]: c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) - [20250519_003849.]: Entered 'hyperbolic_regression'-Function - [20250519_003849.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003850.]: Entered 'cubic_regression'-Function - [20250519_003850.]: 'cubic_regression': minmax = FALSE - [20250519_003850.]: # CpG-site: CpG#3 + [20250519_004612.]: Logging df_agg: CpG#2 + [20250519_004612.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004612.]: c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)[20250519_004612.]: c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)[20250519_004612.]: c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) + [20250519_004612.]: Entered 'hyperbolic_regression'-Function + [20250519_004612.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004612.]: Entered 'cubic_regression'-Function + [20250519_004612.]: 'cubic_regression': minmax = FALSE + [20250519_004612.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) - [20250519_003850.]: Logging df_agg: CpG#3 - [20250519_003850.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003850.]: c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)[20250519_003850.]: c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)[20250519_003850.]: c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) - [20250519_003850.]: Entered 'hyperbolic_regression'-Function - [20250519_003850.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003850.]: Entered 'cubic_regression'-Function - [20250519_003850.]: 'cubic_regression': minmax = FALSE - [20250519_003850.]: # CpG-site: CpG#4 + [20250519_004612.]: Logging df_agg: CpG#3 + [20250519_004612.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004612.]: c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)[20250519_004612.]: c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)[20250519_004612.]: c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) + [20250519_004612.]: Entered 'hyperbolic_regression'-Function + [20250519_004612.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004612.]: Entered 'cubic_regression'-Function + [20250519_004612.]: 'cubic_regression': minmax = FALSE + [20250519_004612.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) - [20250519_003850.]: Logging df_agg: CpG#4 - [20250519_003850.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003850.]: c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)[20250519_003850.]: c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)[20250519_003850.]: c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) - [20250519_003850.]: Entered 'hyperbolic_regression'-Function - [20250519_003850.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003850.]: Entered 'cubic_regression'-Function - [20250519_003850.]: 'cubic_regression': minmax = FALSE - [20250519_003850.]: # CpG-site: CpG#5 + [20250519_004612.]: Logging df_agg: CpG#4 + [20250519_004612.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004612.]: c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)[20250519_004612.]: c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)[20250519_004612.]: c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) + [20250519_004612.]: Entered 'hyperbolic_regression'-Function + [20250519_004612.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004613.]: Entered 'cubic_regression'-Function + [20250519_004613.]: 'cubic_regression': minmax = FALSE + [20250519_004613.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) - [20250519_003850.]: Logging df_agg: CpG#5 - [20250519_003850.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003850.]: c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)[20250519_003850.]: c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)[20250519_003850.]: c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) - [20250519_003850.]: Entered 'hyperbolic_regression'-Function - [20250519_003850.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003850.]: Entered 'cubic_regression'-Function - [20250519_003850.]: 'cubic_regression': minmax = FALSE - [20250519_003850.]: # CpG-site: CpG#6 + [20250519_004613.]: Logging df_agg: CpG#5 + [20250519_004613.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004613.]: c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)[20250519_004613.]: c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)[20250519_004613.]: c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) + [20250519_004613.]: Entered 'hyperbolic_regression'-Function + [20250519_004613.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004613.]: Entered 'cubic_regression'-Function + [20250519_004613.]: 'cubic_regression': minmax = FALSE + [20250519_004613.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) - [20250519_003850.]: Logging df_agg: CpG#6 - [20250519_003850.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003850.]: c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)[20250519_003850.]: c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)[20250519_003850.]: c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) - [20250519_003850.]: Entered 'hyperbolic_regression'-Function - [20250519_003850.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003851.]: Entered 'cubic_regression'-Function - [20250519_003851.]: 'cubic_regression': minmax = FALSE - [20250519_003851.]: # CpG-site: CpG#7 + [20250519_004613.]: Logging df_agg: CpG#6 + [20250519_004613.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004613.]: c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)[20250519_004613.]: c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)[20250519_004613.]: c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) + [20250519_004613.]: Entered 'hyperbolic_regression'-Function + [20250519_004613.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004613.]: Entered 'cubic_regression'-Function + [20250519_004613.]: 'cubic_regression': minmax = FALSE + [20250519_004613.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) - [20250519_003851.]: Logging df_agg: CpG#7 - [20250519_003851.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003851.]: c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)[20250519_003851.]: c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)[20250519_003851.]: c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) - [20250519_003851.]: Entered 'hyperbolic_regression'-Function - [20250519_003851.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003851.]: Entered 'cubic_regression'-Function - [20250519_003851.]: 'cubic_regression': minmax = FALSE - [20250519_003851.]: # CpG-site: CpG#8 + [20250519_004613.]: Logging df_agg: CpG#7 + [20250519_004613.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004613.]: c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)[20250519_004613.]: c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)[20250519_004613.]: c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) + [20250519_004613.]: Entered 'hyperbolic_regression'-Function + [20250519_004613.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004613.]: Entered 'cubic_regression'-Function + [20250519_004613.]: 'cubic_regression': minmax = FALSE + [20250519_004613.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) - [20250519_003851.]: Logging df_agg: CpG#8 - [20250519_003851.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003851.]: c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)[20250519_003851.]: c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)[20250519_003851.]: c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) - [20250519_003851.]: Entered 'hyperbolic_regression'-Function - [20250519_003851.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003851.]: Entered 'cubic_regression'-Function - [20250519_003851.]: 'cubic_regression': minmax = FALSE - [20250519_003851.]: # CpG-site: CpG#9 + [20250519_004613.]: Logging df_agg: CpG#8 + [20250519_004613.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004613.]: c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)[20250519_004613.]: c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)[20250519_004613.]: c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) + [20250519_004613.]: Entered 'hyperbolic_regression'-Function + [20250519_004613.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004614.]: Entered 'cubic_regression'-Function + [20250519_004614.]: 'cubic_regression': minmax = FALSE + [20250519_004614.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) - [20250519_003851.]: Logging df_agg: CpG#9 - [20250519_003851.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003851.]: c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)[20250519_003851.]: c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)[20250519_003851.]: c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) - [20250519_003851.]: Entered 'hyperbolic_regression'-Function - [20250519_003851.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003851.]: Entered 'cubic_regression'-Function - [20250519_003851.]: 'cubic_regression': minmax = FALSE - [20250519_003851.]: # CpG-site: row_means + [20250519_004614.]: Logging df_agg: CpG#9 + [20250519_004614.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004614.]: c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)[20250519_004614.]: c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)[20250519_004614.]: c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) + [20250519_004614.]: Entered 'hyperbolic_regression'-Function + [20250519_004614.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004614.]: Entered 'cubic_regression'-Function + [20250519_004614.]: 'cubic_regression': minmax = FALSE + [20250519_004614.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) - [20250519_003851.]: Logging df_agg: row_means - [20250519_003851.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003851.]: c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)[20250519_003851.]: c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)[20250519_003851.]: c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) - [20250519_003851.]: Entered 'hyperbolic_regression'-Function - [20250519_003851.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003852.]: Entered 'cubic_regression'-Function - [20250519_003852.]: 'cubic_regression': minmax = FALSE - [20250519_003852.]: ### Starting with plotting ### - [20250519_003852.]: Creating BiasCorrected (cubic) plot No. 1 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG1_corrected_c.png - [20250519_003852.]: # CpG-site: CpG#1 + [20250519_004614.]: Logging df_agg: row_means + [20250519_004614.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004614.]: c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)[20250519_004614.]: c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)[20250519_004614.]: c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) + [20250519_004614.]: Entered 'hyperbolic_regression'-Function + [20250519_004614.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004614.]: Entered 'cubic_regression'-Function + [20250519_004614.]: 'cubic_regression': minmax = FALSE + [20250519_004615.]: ### Starting with plotting ### + [20250519_004615.]: Creating BiasCorrected (cubic) plot No. 1 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG1_corrected_c.png + [20250519_004615.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -1164.78037112998 , a = 2039.49073397227 , b = -1164.78037112998 , d = 1955.827713817 - [20250519_003852.]: # CpG-site: CpG#1 + [20250519_004615.]: # CpG-site: CpG#1 Cubic: Using a = -3.42821196240366e-05 , b = 0.00462173155013798 , c = 0.850326258865826 , d = 0.500073874211125 - [20250519_003854.]: Creating BiasCorrected (cubic) plot No. 2 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG2_corrected_c.png - [20250519_003854.]: # CpG-site: CpG#2 + [20250519_004616.]: Creating BiasCorrected (cubic) plot No. 2 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG2_corrected_c.png + [20250519_004616.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = -464.14479041434 , a = 4988.85430873436 , b = -464.14479041434 , d = 4925.32709283236 - [20250519_003854.]: # CpG-site: CpG#2 + [20250519_004616.]: # CpG-site: CpG#2 Cubic: Using a = -1.40811796906314e-05 , b = 0.00190849431754485 , c = 0.933455870941473 , d = 0.363096210099674 - [20250519_003855.]: Creating BiasCorrected (cubic) plot No. 3 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG3_corrected_c.png - [20250519_003855.]: # CpG-site: CpG#3 + [20250519_004618.]: Creating BiasCorrected (cubic) plot No. 3 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG3_corrected_c.png + [20250519_004618.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = -745.616753604673 , a = 2026.5285182947 , b = -745.616753604673 , d = 1953.27811759735 - [20250519_003855.]: # CpG-site: CpG#3 + [20250519_004618.]: # CpG-site: CpG#3 Cubic: Using a = -2.78835051857186e-05 , b = 0.00366863707889089 , c = 0.880665171220893 , d = 0.510779308833653 - [20250519_003857.]: Creating BiasCorrected (cubic) plot No. 4 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG4_corrected_c.png - [20250519_003857.]: # CpG-site: CpG#4 + [20250519_004619.]: Creating BiasCorrected (cubic) plot No. 4 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG4_corrected_c.png + [20250519_004619.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = -833.077868829112 , a = 2871.13688289992 , b = -833.077868829112 , d = 2796.8115417971 - [20250519_003857.]: # CpG-site: CpG#4 + [20250519_004619.]: # CpG-site: CpG#4 Cubic: Using a = -2.30028865643e-05 , b = 0.00309017851940692 , c = 0.897073107777645 , d = 0.443455761841866 - [20250519_003858.]: Creating BiasCorrected (cubic) plot No. 5 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG5_corrected_c.png - [20250519_003858.]: # CpG-site: CpG#5 + [20250519_004621.]: Creating BiasCorrected (cubic) plot No. 5 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG5_corrected_c.png + [20250519_004621.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = 222.093251203934 , a = 12477.4109611091 , b = 222.093251203934 , d = 12432.0779293841 - [20250519_003858.]: # CpG-site: CpG#5 + [20250519_004621.]: # CpG-site: CpG#5 Cubic: Using a = -1.28632696989134e-05 , b = 0.00184822285299014 , c = 0.931001046631176 , d = 0.437963095185599 - [20250519_003900.]: Creating BiasCorrected (cubic) plot No. 6 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG6_corrected_c.png - [20250519_003900.]: # CpG-site: CpG#6 + [20250519_004622.]: Creating BiasCorrected (cubic) plot No. 6 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG6_corrected_c.png + [20250519_004622.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = -700.460688613084 , a = 21299.059407053 , b = -700.460688613084 , d = 21234.4652853188 - [20250519_003900.]: # CpG-site: CpG#6 + [20250519_004622.]: # CpG-site: CpG#6 Cubic: Using a = -5.838335151375e-06 , b = 0.000828447163218526 , c = 0.970047764475166 , d = 0.158074701412251 - [20250519_003901.]: Creating BiasCorrected (cubic) plot No. 7 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG7_corrected_c.png - [20250519_003901.]: # CpG-site: CpG#7 + [20250519_004624.]: Creating BiasCorrected (cubic) plot No. 7 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG7_corrected_c.png + [20250519_004624.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -1144.91191228206 , a = 1477.91098401837 , b = -1144.91191228206 , d = 1394.69940178972 - [20250519_003901.]: # CpG-site: CpG#7 + [20250519_004624.]: # CpG-site: CpG#7 Cubic: Using a = -4.54220535614047e-05 , b = 0.00607993130565131 , c = 0.805411493452389 , d = 0.615125382790254 - [20250519_003903.]: Creating BiasCorrected (cubic) plot No. 8 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG8_corrected_c.png - [20250519_003903.]: # CpG-site: CpG#8 + [20250519_004625.]: Creating BiasCorrected (cubic) plot No. 8 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG8_corrected_c.png + [20250519_004625.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = -349.37367288786 , a = 2377.38756952122 , b = -349.37367288786 , d = 2315.21494778767 - [20250519_003903.]: # CpG-site: CpG#8 + [20250519_004625.]: # CpG-site: CpG#8 Cubic: Using a = -2.64622306669639e-05 , b = 0.00353621593528015 , c = 0.877916844709074 , d = 0.698684606493051 - [20250519_003904.]: Creating BiasCorrected (cubic) plot No. 9 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG9_corrected_c.png - [20250519_003904.]: # CpG-site: CpG#9 + [20250519_004626.]: Creating BiasCorrected (cubic) plot No. 9 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG9_corrected_c.png + [20250519_004626.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -1536.91699417279 , a = 3745.17619112231 , b = -1536.91699417279 , d = 3656.26801968932 - [20250519_003904.]: # CpG-site: CpG#9 + [20250519_004626.]: # CpG-site: CpG#9 Cubic: Using a = -2.70976130611725e-05 , b = 0.00378478247182342 , c = 0.871825263051793 , d = 0.455475740974113 - [20250519_003906.]: Creating BiasCorrected (cubic) plot No. 10 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_rowmeans_corrected_c.png - [20250519_003906.]: # CpG-site: row_means + [20250519_004628.]: Creating BiasCorrected (cubic) plot No. 10 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_rowmeans_corrected_c.png + [20250519_004628.]: # CpG-site: row_means Hyperbolic: Using bias_weight = -820.643805549713 , a = 3198.46619445775 , b = -820.643805549713 , d = 3124.21083989374 - [20250519_003906.]: # CpG-site: row_means + [20250519_004628.]: # CpG-site: row_means Cubic: Using a = -2.34328362100009e-05 , b = 0.00319139073176266 , c = 0.891842168032129 , d = 0.493517689787135 - [20250519_003907.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG1_corrected_c.png - [20250519_003909.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG2_corrected_c.png - [20250519_003910.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG3_corrected_c.png - [20250519_003911.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG4_corrected_c.png - [20250519_003912.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG5_corrected_c.png - [20250519_003914.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG6_corrected_c.png - [20250519_003915.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG7_corrected_c.png - [20250519_003916.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG8_corrected_c.png - [20250519_003918.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG9_corrected_c.png - [20250519_003919.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_rowmeans_corrected_c.png - [20250519_003920.]: Entered 'solving_equations'-Function - [20250519_003920.]: Solving hyperbolic regression for CpG#1 + [20250519_004630.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG1_corrected_c.png + [20250519_004631.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG2_corrected_c.png + [20250519_004632.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG3_corrected_c.png + [20250519_004633.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG4_corrected_c.png + [20250519_004635.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG5_corrected_c.png + [20250519_004636.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG6_corrected_c.png + [20250519_004637.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG7_corrected_c.png + [20250519_004639.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG8_corrected_c.png + [20250519_004640.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG9_corrected_c.png + [20250519_004641.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_rowmeans_corrected_c.png + [20250519_004642.]: Entered 'solving_equations'-Function + [20250519_004642.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 79.867338305743 - [20250519_003920.]: Samplename: Sample#1 + [20250519_004642.]: Samplename: Sample#1 Root: 79.867 --> Root in between the borders! Added to results. Hyperbolic solved: 29.7900144307253 - [20250519_003920.]: Samplename: Sample#10 + [20250519_004642.]: Samplename: Sample#10 Root: 29.79 --> Root in between the borders! Added to results. Hyperbolic solved: 41.6525328583179 - [20250519_003920.]: Samplename: Sample#2 + [20250519_004642.]: Samplename: Sample#2 Root: 41.653 --> Root in between the borders! Added to results. Hyperbolic solved: 57.4651978180159 - [20250519_003920.]: Samplename: Sample#3 + [20250519_004642.]: Samplename: Sample#3 Root: 57.465 --> Root in between the borders! Added to results. Hyperbolic solved: 9.20072297150156 - [20250519_003920.]: Samplename: Sample#4 + [20250519_004642.]: Samplename: Sample#4 Root: 9.201 --> Root in between the borders! Added to results. Hyperbolic solved: 21.8059605790798 - [20250519_003920.]: Samplename: Sample#5 + [20250519_004642.]: Samplename: Sample#5 Root: 21.806 --> Root in between the borders! Added to results. Hyperbolic solved: 23.0837964625959 - [20250519_003920.]: Samplename: Sample#6 + [20250519_004642.]: Samplename: Sample#6 Root: 23.084 --> Root in between the borders! Added to results. Hyperbolic solved: 45.5034148455809 - [20250519_003920.]: Samplename: Sample#7 + [20250519_004642.]: Samplename: Sample#7 Root: 45.503 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6987854360424 - [20250519_003920.]: Samplename: Sample#8 + [20250519_004642.]: Samplename: Sample#8 Root: 85.699 --> Root in between the borders! Added to results. Hyperbolic solved: -3.66510576608508 - [20250519_003920.]: Samplename: Sample#9 + [20250519_004642.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -3.665 --> '-10 < root < 0' --> substitute 0 - [20250519_003921.]: Solving hyperbolic regression for CpG#2 + [20250519_004643.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 76.7227930253137 - [20250519_003921.]: Samplename: Sample#1 + [20250519_004643.]: Samplename: Sample#1 Root: 76.723 --> Root in between the borders! Added to results. Hyperbolic solved: 30.9924274267338 - [20250519_003921.]: Samplename: Sample#10 + [20250519_004643.]: Samplename: Sample#10 Root: 30.992 --> Root in between the borders! Added to results. Hyperbolic solved: 42.6810881616502 - [20250519_003921.]: Samplename: Sample#2 + [20250519_004643.]: Samplename: Sample#2 Root: 42.681 --> Root in between the borders! Added to results. Hyperbolic solved: 58.93379071467 - [20250519_003921.]: Samplename: Sample#3 + [20250519_004643.]: Samplename: Sample#3 Root: 58.934 --> Root in between the borders! Added to results. Hyperbolic solved: 4.98020463922415 - [20250519_003921.]: Samplename: Sample#4 + [20250519_004643.]: Samplename: Sample#4 Root: 4.98 --> Root in between the borders! Added to results. Hyperbolic solved: 20.2480263316954 - [20250519_003921.]: Samplename: Sample#5 + [20250519_004643.]: Samplename: Sample#5 Root: 20.248 --> Root in between the borders! Added to results. Hyperbolic solved: 17.8133085422138 - [20250519_003921.]: Samplename: Sample#6 + [20250519_004643.]: Samplename: Sample#6 Root: 17.813 --> Root in between the borders! Added to results. Hyperbolic solved: 40.917674420917 - [20250519_003921.]: Samplename: Sample#7 + [20250519_004643.]: Samplename: Sample#7 Root: 40.918 --> Root in between the borders! Added to results. Hyperbolic solved: 86.045071534744 - [20250519_003921.]: Samplename: Sample#8 + [20250519_004643.]: Samplename: Sample#8 Root: 86.045 --> Root in between the borders! Added to results. Hyperbolic solved: 3.1157977862424 - [20250519_003921.]: Samplename: Sample#9 + [20250519_004643.]: Samplename: Sample#9 Root: 3.116 --> Root in between the borders! Added to results. - [20250519_003921.]: Solving hyperbolic regression for CpG#3 + [20250519_004643.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 74.9349278251272 - [20250519_003921.]: Samplename: Sample#1 + [20250519_004643.]: Samplename: Sample#1 Root: 74.935 --> Root in between the borders! Added to results. Hyperbolic solved: 27.6844390885298 - [20250519_003921.]: Samplename: Sample#10 + [20250519_004643.]: Samplename: Sample#10 Root: 27.684 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8520223561161 - [20250519_003921.]: Samplename: Sample#2 + [20250519_004643.]: Samplename: Sample#2 Root: 41.852 --> Root in between the borders! Added to results. Hyperbolic solved: 55.8325219766746 - [20250519_003921.]: Samplename: Sample#3 + [20250519_004643.]: Samplename: Sample#3 Root: 55.833 --> Root in between the borders! Added to results. Hyperbolic solved: 8.03518759540723 - [20250519_003921.]: Samplename: Sample#4 + [20250519_004643.]: Samplename: Sample#4 Root: 8.035 --> Root in between the borders! Added to results. Hyperbolic solved: 24.1066316660805 - [20250519_003921.]: Samplename: Sample#5 + [20250519_004643.]: Samplename: Sample#5 Root: 24.107 --> Root in between the borders! Added to results. Hyperbolic solved: 26.2419826081677 - [20250519_003921.]: Samplename: Sample#6 + [20250519_004643.]: Samplename: Sample#6 Root: 26.242 --> Root in between the borders! Added to results. Hyperbolic solved: 44.0944957314077 - [20250519_003921.]: Samplename: Sample#7 + [20250519_004643.]: Samplename: Sample#7 Root: 44.094 --> Root in between the borders! Added to results. Hyperbolic solved: 85.8279384942248 - [20250519_003921.]: Samplename: Sample#8 + [20250519_004643.]: Samplename: Sample#8 Root: 85.828 --> Root in between the borders! Added to results. Hyperbolic solved: -0.66649088715512 - [20250519_003921.]: Samplename: Sample#9 + [20250519_004643.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.666 --> '-10 < root < 0' --> substitute 0 - [20250519_003921.]: Solving hyperbolic regression for CpG#4 + [20250519_004643.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 76.349525731503 - [20250519_003921.]: Samplename: Sample#1 + [20250519_004643.]: Samplename: Sample#1 Root: 76.35 --> Root in between the borders! Added to results. Hyperbolic solved: 28.2568543207918 - [20250519_003921.]: Samplename: Sample#10 + [20250519_004643.]: Samplename: Sample#10 Root: 28.257 --> Root in between the borders! Added to results. Hyperbolic solved: 43.408980967851 - [20250519_003921.]: Samplename: Sample#2 + [20250519_004643.]: Samplename: Sample#2 Root: 43.409 --> Root in between the borders! Added to results. Hyperbolic solved: 58.5435202418955 - [20250519_003921.]: Samplename: Sample#3 + [20250519_004643.]: Samplename: Sample#3 Root: 58.544 --> Root in between the borders! Added to results. Hyperbolic solved: 10.308707716736 - [20250519_003921.]: Samplename: Sample#4 + [20250519_004643.]: Samplename: Sample#4 Root: 10.309 --> Root in between the borders! Added to results. Hyperbolic solved: 22.1830453155629 - [20250519_003921.]: Samplename: Sample#5 + [20250519_004643.]: Samplename: Sample#5 Root: 22.183 --> Root in between the borders! Added to results. Hyperbolic solved: 27.133776120676 - [20250519_003921.]: Samplename: Sample#6 + [20250519_004643.]: Samplename: Sample#6 Root: 27.134 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8321067699793 - [20250519_003921.]: Samplename: Sample#7 + [20250519_004643.]: Samplename: Sample#7 Root: 41.832 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6890182703099 - [20250519_003921.]: Samplename: Sample#8 + [20250519_004643.]: Samplename: Sample#8 Root: 85.689 --> Root in between the borders! Added to results. Hyperbolic solved: 2.42232661674155 - [20250519_003921.]: Samplename: Sample#9 + [20250519_004643.]: Samplename: Sample#9 Root: 2.422 --> Root in between the borders! Added to results. - [20250519_003921.]: Solving hyperbolic regression for CpG#5 + [20250519_004643.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 72.8126911247073 - [20250519_003921.]: Samplename: Sample#1 + [20250519_004643.]: Samplename: Sample#1 Root: 72.813 --> Root in between the borders! Added to results. Hyperbolic solved: 26.4206935777278 - [20250519_003921.]: Samplename: Sample#10 + [20250519_004643.]: Samplename: Sample#10 Root: 26.421 --> Root in between the borders! Added to results. Hyperbolic solved: 44.2738761535648 - [20250519_003921.]: Samplename: Sample#2 + [20250519_004643.]: Samplename: Sample#2 Root: 44.274 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5324382038438 - [20250519_003921.]: Samplename: Sample#3 + [20250519_004643.]: Samplename: Sample#3 Root: 53.532 --> Root in between the borders! Added to results. Hyperbolic solved: 10.0373723654377 - [20250519_003921.]: Samplename: Sample#4 + [20250519_004643.]: Samplename: Sample#4 Root: 10.037 --> Root in between the borders! Added to results. Hyperbolic solved: 22.8733116049306 - [20250519_003921.]: Samplename: Sample#5 + [20250519_004643.]: Samplename: Sample#5 Root: 22.873 --> Root in between the borders! Added to results. Hyperbolic solved: 25.9417915879453 - [20250519_003921.]: Samplename: Sample#6 + [20250519_004643.]: Samplename: Sample#6 Root: 25.942 --> Root in between the borders! Added to results. Hyperbolic solved: 42.7945071576182 - [20250519_003921.]: Samplename: Sample#7 + [20250519_004643.]: Samplename: Sample#7 Root: 42.795 --> Root in between the borders! Added to results. Hyperbolic solved: 88.8741214991654 - [20250519_003921.]: Samplename: Sample#8 + [20250519_004643.]: Samplename: Sample#8 Root: 88.874 --> Root in between the borders! Added to results. Hyperbolic solved: 2.34647622155857 - [20250519_003921.]: Samplename: Sample#9 + [20250519_004643.]: Samplename: Sample#9 Root: 2.346 --> Root in between the borders! Added to results. - [20250519_003921.]: Solving hyperbolic regression for CpG#6 + [20250519_004643.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 79.278059253184 - [20250519_003921.]: Samplename: Sample#1 + [20250519_004643.]: Samplename: Sample#1 Root: 79.278 --> Root in between the borders! Added to results. Hyperbolic solved: 30.2012459144106 - [20250519_003921.]: Samplename: Sample#10 + [20250519_004643.]: Samplename: Sample#10 Root: 30.201 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8474392901329 - [20250519_003921.]: Samplename: Sample#2 + [20250519_004643.]: Samplename: Sample#2 Root: 41.847 --> Root in between the borders! Added to results. Hyperbolic solved: 56.8423515999057 - [20250519_003921.]: Samplename: Sample#3 + [20250519_004643.]: Samplename: Sample#3 Root: 56.842 --> Root in between the borders! Added to results. Hyperbolic solved: 8.87856073331453 - [20250519_003921.]: Samplename: Sample#4 + [20250519_004643.]: Samplename: Sample#4 Root: 8.879 --> Root in between the borders! Added to results. Hyperbolic solved: 18.6901596391315 - [20250519_003921.]: Samplename: Sample#5 + [20250519_004643.]: Samplename: Sample#5 Root: 18.69 --> Root in between the borders! Added to results. Hyperbolic solved: 29.9309263719602 - [20250519_003921.]: Samplename: Sample#6 + [20250519_004643.]: Samplename: Sample#6 Root: 29.931 --> Root in between the borders! Added to results. Hyperbolic solved: 42.814855924799 - [20250519_003921.]: Samplename: Sample#7 + [20250519_004643.]: Samplename: Sample#7 Root: 42.815 --> Root in between the borders! Added to results. Hyperbolic solved: 86.75018307028 - [20250519_003921.]: Samplename: Sample#8 + [20250519_004643.]: Samplename: Sample#8 Root: 86.75 --> Root in between the borders! Added to results. Hyperbolic solved: 1.51516233882239 - [20250519_003921.]: Samplename: Sample#9 + [20250519_004643.]: Samplename: Sample#9 Root: 1.515 --> Root in between the borders! Added to results. - [20250519_003921.]: Solving hyperbolic regression for CpG#7 + [20250519_004643.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 78.2565541874121 - [20250519_003921.]: Samplename: Sample#1 + [20250519_004643.]: Samplename: Sample#1 Root: 78.257 --> Root in between the borders! Added to results. Hyperbolic solved: 25.488740177574 - [20250519_003921.]: Samplename: Sample#10 + [20250519_004643.]: Samplename: Sample#10 Root: 25.489 --> Root in between the borders! Added to results. Hyperbolic solved: 47.3712173704701 - [20250519_003921.]: Samplename: Sample#2 + [20250519_004643.]: Samplename: Sample#2 Root: 47.371 --> Root in between the borders! Added to results. Hyperbolic solved: 58.3142577916519 - [20250519_003921.]: Samplename: Sample#3 + [20250519_004643.]: Samplename: Sample#3 Root: 58.314 --> Root in between the borders! Added to results. Hyperbolic solved: 11.7212348600366 - [20250519_003921.]: Samplename: Sample#4 + [20250519_004643.]: Samplename: Sample#4 Root: 11.721 --> Root in between the borders! Added to results. Hyperbolic solved: 25.3797494985646 - [20250519_003921.]: Samplename: Sample#5 + [20250519_004643.]: Samplename: Sample#5 Root: 25.38 --> Root in between the borders! Added to results. Hyperbolic solved: 29.4095117400265 - [20250519_003921.]: Samplename: Sample#6 + [20250519_004643.]: Samplename: Sample#6 Root: 29.41 --> Root in between the borders! Added to results. Hyperbolic solved: 44.5754992753069 - [20250519_003921.]: Samplename: Sample#7 + [20250519_004643.]: Samplename: Sample#7 Root: 44.575 --> Root in between the borders! Added to results. Hyperbolic solved: 85.9628719316752 - [20250519_003921.]: Samplename: Sample#8 + [20250519_004643.]: Samplename: Sample#8 Root: 85.963 --> Root in between the borders! Added to results. Hyperbolic solved: -4.16453560967523 - [20250519_003921.]: Samplename: Sample#9 + [20250519_004643.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -4.165 --> '-10 < root < 0' --> substitute 0 - [20250519_003921.]: Solving hyperbolic regression for CpG#8 + [20250519_004643.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 72.9696611539955 - [20250519_003921.]: Samplename: Sample#1 + [20250519_004643.]: Samplename: Sample#1 Root: 72.97 --> Root in between the borders! Added to results. Hyperbolic solved: 27.8250961443825 - [20250519_003921.]: Samplename: Sample#10 + [20250519_004643.]: Samplename: Sample#10 Root: 27.825 --> Root in between the borders! Added to results. Hyperbolic solved: 34.9068396054724 - [20250519_003921.]: Samplename: Sample#2 + [20250519_004643.]: Samplename: Sample#2 Root: 34.907 --> Root in between the borders! Added to results. Hyperbolic solved: 59.163744843639 - [20250519_003921.]: Samplename: Sample#3 + [20250519_004643.]: Samplename: Sample#3 Root: 59.164 --> Root in between the borders! Added to results. Hyperbolic solved: 9.64644348381889 - [20250519_003921.]: Samplename: Sample#4 + [20250519_004643.]: Samplename: Sample#4 Root: 9.646 --> Root in between the borders! Added to results. Hyperbolic solved: 19.3522608313684 - [20250519_003921.]: Samplename: Sample#5 + [20250519_004643.]: Samplename: Sample#5 Root: 19.352 --> Root in between the borders! Added to results. Hyperbolic solved: 38.5571445759356 - [20250519_003921.]: Samplename: Sample#6 + [20250519_004643.]: Samplename: Sample#6 Root: 38.557 --> Root in between the borders! Added to results. Hyperbolic solved: 44.7759635921689 - [20250519_003921.]: Samplename: Sample#7 + [20250519_004643.]: Samplename: Sample#7 Root: 44.776 --> Root in between the borders! Added to results. Hyperbolic solved: 84.8599672002532 - [20250519_003921.]: Samplename: Sample#8 + [20250519_004643.]: Samplename: Sample#8 Root: 84.86 --> Root in between the borders! Added to results. Hyperbolic solved: -2.05045066787228 - [20250519_003921.]: Samplename: Sample#9 + [20250519_004643.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.05 --> '-10 < root < 0' --> substitute 0 - [20250519_003921.]: Solving hyperbolic regression for CpG#9 + [20250519_004643.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 81.0416669543132 - [20250519_003921.]: Samplename: Sample#1 + [20250519_004643.]: Samplename: Sample#1 Root: 81.042 --> Root in between the borders! Added to results. Hyperbolic solved: 26.6441665578561 - [20250519_003921.]: Samplename: Sample#10 + [20250519_004643.]: Samplename: Sample#10 Root: 26.644 --> Root in between the borders! Added to results. Hyperbolic solved: 45.7046185029368 - [20250519_003921.]: Samplename: Sample#2 + [20250519_004643.]: Samplename: Sample#2 Root: 45.705 --> Root in between the borders! Added to results. Hyperbolic solved: 56.9767969678006 - [20250519_003921.]: Samplename: Sample#3 + [20250519_004643.]: Samplename: Sample#3 Root: 56.977 --> Root in between the borders! Added to results. Hyperbolic solved: 6.81231488238752 - [20250519_003921.]: Samplename: Sample#4 + [20250519_004643.]: Samplename: Sample#4 Root: 6.812 --> Root in between the borders! Added to results. Hyperbolic solved: 22.928478377057 - [20250519_003921.]: Samplename: Sample#5 + [20250519_004643.]: Samplename: Sample#5 Root: 22.928 --> Root in between the borders! Added to results. Hyperbolic solved: 38.8653192337298 - [20250519_003921.]: Samplename: Sample#6 + [20250519_004643.]: Samplename: Sample#6 Root: 38.865 --> Root in between the borders! Added to results. Hyperbolic solved: 43.6855618249121 - [20250519_003921.]: Samplename: Sample#7 + [20250519_004643.]: Samplename: Sample#7 Root: 43.686 --> Root in between the borders! Added to results. Hyperbolic solved: 88.0155362134065 - [20250519_003921.]: Samplename: Sample#8 + [20250519_004643.]: Samplename: Sample#8 Root: 88.016 --> Root in between the borders! Added to results. Hyperbolic solved: -3.35609615796437 - [20250519_003921.]: Samplename: Sample#9 + [20250519_004643.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -3.356 --> '-10 < root < 0' --> substitute 0 - [20250519_003921.]: Solving hyperbolic regression for row_means + [20250519_004643.]: Solving hyperbolic regression for row_means Hyperbolic solved: 77.0692788889392 - [20250519_003921.]: Samplename: Sample#1 + [20250519_004643.]: Samplename: Sample#1 Root: 77.069 --> Root in between the borders! Added to results. Hyperbolic solved: 28.3620037732087 - [20250519_003921.]: Samplename: Sample#10 + [20250519_004643.]: Samplename: Sample#10 Root: 28.362 --> Root in between the borders! Added to results. Hyperbolic solved: 42.5026157763218 - [20250519_003921.]: Samplename: Sample#2 + [20250519_004643.]: Samplename: Sample#2 Root: 42.503 --> Root in between the borders! Added to results. Hyperbolic solved: 57.2972029249363 - [20250519_003921.]: Samplename: Sample#3 + [20250519_004643.]: Samplename: Sample#3 Root: 57.297 --> Root in between the borders! Added to results. Hyperbolic solved: 8.82704268325745 - [20250519_003921.]: Samplename: Sample#4 + [20250519_004643.]: Samplename: Sample#4 Root: 8.827 --> Root in between the borders! Added to results. Hyperbolic solved: 21.8102595595999 - [20250519_003921.]: Samplename: Sample#5 + [20250519_004643.]: Samplename: Sample#5 Root: 21.81 --> Root in between the borders! Added to results. Hyperbolic solved: 28.7228654114699 - [20250519_003921.]: Samplename: Sample#6 + [20250519_004643.]: Samplename: Sample#6 Root: 28.723 --> Root in between the borders! Added to results. Hyperbolic solved: 43.4105084715017 - [20250519_003921.]: Samplename: Sample#7 + [20250519_004643.]: Samplename: Sample#7 Root: 43.411 --> Root in between the borders! Added to results. Hyperbolic solved: 86.4143551575745 - [20250519_003921.]: Samplename: Sample#8 + [20250519_004643.]: Samplename: Sample#8 Root: 86.414 --> Root in between the borders! Added to results. Hyperbolic solved: -0.237016010782264 - [20250519_003921.]: Samplename: Sample#9 + [20250519_004643.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.237 --> '-10 < root < 0' --> substitute 0 - [20250519_003921.]: on_start: using future::plan("sequential") - [20250519_003921.]: Entered 'clean_dt'-Function - [20250519_003921.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_003921.]: got experimental data - [20250519_003921.]: Entered 'clean_dt'-Function - [20250519_003921.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_003921.]: got calibration data - [20250519_003921.]: + [20250519_004643.]: on_start: using future::plan("sequential") + [20250519_004643.]: Entered 'clean_dt'-Function + [20250519_004643.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_004643.]: got experimental data + [20250519_004643.]: Entered 'clean_dt'-Function + [20250519_004643.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_004643.]: got calibration data + [20250519_004643.]: ### Starting with regression calculations ### - [20250519_003921.]: Entered 'regression_type1'-Function - [20250519_003921.]: # CpG-site: CpG#1 + [20250519_004643.]: Entered 'regression_type1'-Function + [20250519_004643.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_003921.]: Logging df_agg: CpG#1 - [20250519_003921.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003921.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_003921.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_003921.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_003921.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_003921.]: Entered 'hyperbolic_regression'-Function - [20250519_003921.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003921.]: Entered 'cubic_regression'-Function - [20250519_003921.]: 'cubic_regression': minmax = FALSE - [20250519_003921.]: # CpG-site: CpG#2 + [20250519_004643.]: Logging df_agg: CpG#1 + [20250519_004643.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004643.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_004643.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_004643.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_004643.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) + [20250519_004643.]: Entered 'hyperbolic_regression'-Function + [20250519_004643.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004643.]: Entered 'cubic_regression'-Function + [20250519_004643.]: 'cubic_regression': minmax = FALSE + [20250519_004643.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_003921.]: Logging df_agg: CpG#2 - [20250519_003921.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003921.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_003921.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_003921.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_003921.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_003921.]: Entered 'hyperbolic_regression'-Function - [20250519_003921.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003921.]: Entered 'cubic_regression'-Function - [20250519_003921.]: 'cubic_regression': minmax = FALSE - [20250519_003921.]: # CpG-site: CpG#3 + [20250519_004643.]: Logging df_agg: CpG#2 + [20250519_004643.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004643.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_004643.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_004643.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_004643.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) + [20250519_004643.]: Entered 'hyperbolic_regression'-Function + [20250519_004643.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004643.]: Entered 'cubic_regression'-Function + [20250519_004643.]: 'cubic_regression': minmax = FALSE + [20250519_004643.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_003921.]: Logging df_agg: CpG#3 - [20250519_003921.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003921.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_003921.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_003921.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_003921.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_003921.]: Entered 'hyperbolic_regression'-Function - [20250519_003921.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003921.]: Entered 'cubic_regression'-Function - [20250519_003921.]: 'cubic_regression': minmax = FALSE - [20250519_003921.]: # CpG-site: CpG#4 + [20250519_004643.]: Logging df_agg: CpG#3 + [20250519_004643.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004643.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_004643.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_004643.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_004643.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) + [20250519_004643.]: Entered 'hyperbolic_regression'-Function + [20250519_004643.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004643.]: Entered 'cubic_regression'-Function + [20250519_004643.]: 'cubic_regression': minmax = FALSE + [20250519_004643.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_003921.]: Logging df_agg: CpG#4 - [20250519_003921.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003921.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_003921.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_003921.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_003921.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_003921.]: Entered 'hyperbolic_regression'-Function - [20250519_003921.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003921.]: Entered 'cubic_regression'-Function - [20250519_003921.]: 'cubic_regression': minmax = FALSE - [20250519_003921.]: # CpG-site: CpG#5 + [20250519_004643.]: Logging df_agg: CpG#4 + [20250519_004643.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004643.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_004643.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_004643.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_004643.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) + [20250519_004643.]: Entered 'hyperbolic_regression'-Function + [20250519_004643.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004643.]: Entered 'cubic_regression'-Function + [20250519_004643.]: 'cubic_regression': minmax = FALSE + [20250519_004643.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_003921.]: Logging df_agg: CpG#5 - [20250519_003921.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003921.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_003921.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_003921.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_003921.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_003921.]: Entered 'hyperbolic_regression'-Function - [20250519_003921.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003921.]: Entered 'cubic_regression'-Function - [20250519_003921.]: 'cubic_regression': minmax = FALSE - [20250519_003921.]: # CpG-site: CpG#6 + [20250519_004643.]: Logging df_agg: CpG#5 + [20250519_004643.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004643.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_004643.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_004643.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_004643.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) + [20250519_004643.]: Entered 'hyperbolic_regression'-Function + [20250519_004643.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004643.]: Entered 'cubic_regression'-Function + [20250519_004643.]: 'cubic_regression': minmax = FALSE + [20250519_004643.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_003921.]: Logging df_agg: CpG#6 - [20250519_003921.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003921.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_003921.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_003921.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_003921.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_003921.]: Entered 'hyperbolic_regression'-Function - [20250519_003921.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003921.]: Entered 'cubic_regression'-Function - [20250519_003921.]: 'cubic_regression': minmax = FALSE - [20250519_003921.]: # CpG-site: CpG#7 + [20250519_004643.]: Logging df_agg: CpG#6 + [20250519_004643.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004643.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_004643.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_004643.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_004643.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) + [20250519_004643.]: Entered 'hyperbolic_regression'-Function + [20250519_004643.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004643.]: Entered 'cubic_regression'-Function + [20250519_004643.]: 'cubic_regression': minmax = FALSE + [20250519_004643.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_003921.]: Logging df_agg: CpG#7 - [20250519_003921.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003921.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_003921.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_003921.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_003921.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_003921.]: Entered 'hyperbolic_regression'-Function - [20250519_003921.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003921.]: Entered 'cubic_regression'-Function - [20250519_003921.]: 'cubic_regression': minmax = FALSE - [20250519_003921.]: # CpG-site: CpG#8 + [20250519_004643.]: Logging df_agg: CpG#7 + [20250519_004643.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004643.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_004643.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_004643.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_004643.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) + [20250519_004643.]: Entered 'hyperbolic_regression'-Function + [20250519_004643.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004643.]: Entered 'cubic_regression'-Function + [20250519_004643.]: 'cubic_regression': minmax = FALSE + [20250519_004643.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_003921.]: Logging df_agg: CpG#8 - [20250519_003921.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003921.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_003921.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_003921.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_003921.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_003921.]: Entered 'hyperbolic_regression'-Function - [20250519_003921.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003921.]: Entered 'cubic_regression'-Function - [20250519_003921.]: 'cubic_regression': minmax = FALSE - [20250519_003921.]: # CpG-site: CpG#9 + [20250519_004643.]: Logging df_agg: CpG#8 + [20250519_004643.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004643.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_004643.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_004643.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_004643.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) + [20250519_004643.]: Entered 'hyperbolic_regression'-Function + [20250519_004643.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004643.]: Entered 'cubic_regression'-Function + [20250519_004644.]: 'cubic_regression': minmax = FALSE + [20250519_004644.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_003921.]: Logging df_agg: CpG#9 - [20250519_003921.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003921.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_003921.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_003921.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_003921.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_003921.]: Entered 'hyperbolic_regression'-Function - [20250519_003921.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003921.]: Entered 'cubic_regression'-Function - [20250519_003921.]: 'cubic_regression': minmax = FALSE - [20250519_003921.]: # CpG-site: row_means + [20250519_004644.]: Logging df_agg: CpG#9 + [20250519_004644.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004644.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_004644.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_004644.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_004644.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) + [20250519_004644.]: Entered 'hyperbolic_regression'-Function + [20250519_004644.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004644.]: Entered 'cubic_regression'-Function + [20250519_004644.]: 'cubic_regression': minmax = FALSE + [20250519_004644.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723122, 2.47760998435345)c(5.94911111111111, 0.84962962962963, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703704, 19.6995555555555, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_003921.]: Logging df_agg: row_means - [20250519_003921.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003921.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_003921.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723122, 2.47760998435345)[20250519_003921.]: c(5.94911111111111, 0.84962962962963, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_003921.]: c(NA, 6.79703703703704, 19.6995555555555, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_003921.]: Entered 'hyperbolic_regression'-Function - [20250519_003921.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003922.]: Entered 'cubic_regression'-Function - [20250519_003922.]: 'cubic_regression': minmax = FALSE - [20250519_003922.]: ### Starting with plotting ### - [20250519_003922.]: Creating plot No. 1 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG1.png - [20250519_003922.]: # CpG-site: CpG#1 + [20250519_004644.]: Logging df_agg: row_means + [20250519_004644.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004644.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_004644.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723122, 2.47760998435345)[20250519_004644.]: c(5.94911111111111, 0.84962962962963, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_004644.]: c(NA, 6.79703703703704, 19.6995555555555, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) + [20250519_004644.]: Entered 'hyperbolic_regression'-Function + [20250519_004644.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004644.]: Entered 'cubic_regression'-Function + [20250519_004644.]: 'cubic_regression': minmax = FALSE + [20250519_004644.]: ### Starting with plotting ### + [20250519_004645.]: Creating plot No. 1 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG1.png + [20250519_004645.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -123249135.40483 , a = 49589952.0989949 , b = -123249135.40483 , d = 59280789.0121927 - [20250519_003922.]: # CpG-site: CpG#1 + [20250519_004645.]: # CpG-site: CpG#1 Cubic: Using a = 6.53413423120091e-05 , b = -0.0055806968734969 , c = 0.784061853455188 , d = 1.93182659932656 - [20250519_003924.]: Creating plot No. 2 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG2.png - [20250519_003924.]: # CpG-site: CpG#2 + [20250519_004646.]: Creating plot No. 2 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG2.png + [20250519_004646.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = 167929575.171327 , a = 42299444.0962795 , b = 167929575.171327 , d = 47897274.2220611 - [20250519_003924.]: # CpG-site: CpG#2 + [20250519_004646.]: # CpG-site: CpG#2 Cubic: Using a = 8.04237934904601e-06 , b = 0.00293158941798942 , c = 0.514821742825076 , d = 9.27667003367003 - [20250519_003925.]: Creating plot No. 3 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG3.png - [20250519_003925.]: # CpG-site: CpG#3 + [20250519_004647.]: Creating plot No. 3 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG3.png + [20250519_004647.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = 31004745.3756238 , a = 31050253.3779659 , b = 31004745.3756238 , d = 40665231.758814 - [20250519_003925.]: # CpG-site: CpG#3 + [20250519_004647.]: # CpG-site: CpG#3 Cubic: Using a = 2.30555869809204e-05 , b = -0.000797009331409346 , c = 0.62783129228796 , d = 3.88705218855218 - [20250519_003927.]: Creating plot No. 4 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG4.png - [20250519_003927.]: # CpG-site: CpG#4 + [20250519_004649.]: Creating plot No. 4 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG4.png + [20250519_004649.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = 128022258.276155 , a = 42779497.5538086 , b = 128022258.276155 , d = 50999439.9227065 - [20250519_003927.]: # CpG-site: CpG#4 + [20250519_004649.]: # CpG-site: CpG#4 Cubic: Using a = 3.2166356902357e-05 , b = -0.0015913142857143 , c = 0.697398364598366 , d = 6.17055050505048 - [20250519_003928.]: Creating plot No. 5 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG5.png - [20250519_003928.]: # CpG-site: CpG#5 + [20250519_004650.]: Creating plot No. 5 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG5.png + [20250519_004650.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = 29447513.6184781 , a = 41864399.8673718 , b = 29447513.6184781 , d = 55382346.8470806 - [20250519_003928.]: # CpG-site: CpG#5 + [20250519_004650.]: # CpG-site: CpG#5 Cubic: Using a = -4.48459708193036e-06 , b = 0.00375371236171235 , c = 0.422446384479718 , d = 5.17203872053872 - [20250519_003930.]: Creating plot No. 6 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG6.png - [20250519_003930.]: # CpG-site: CpG#6 + [20250519_004652.]: Creating plot No. 6 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG6.png + [20250519_004652.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = 17150080.5960938 , a = 40932834.1078748 , b = 17150080.5960938 , d = 46723471.6686748 - [20250519_003930.]: # CpG-site: CpG#6 + [20250519_004652.]: # CpG-site: CpG#6 Cubic: Using a = 2.38852884399552e-05 , b = 0.000918637037037021 , c = 0.561022132435467 , d = 6.1479276094276 - [20250519_003931.]: Creating plot No. 7 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG7.png - [20250519_003931.]: # CpG-site: CpG#7 + [20250519_004653.]: Creating plot No. 7 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG7.png + [20250519_004653.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -57204506.2862076 , a = 29554652.6579632 , b = -57204506.2862076 , d = 48548425.6483363 - [20250519_003931.]: # CpG-site: CpG#7 + [20250519_004653.]: # CpG-site: CpG#7 Cubic: Using a = 4.98010505050505e-05 , b = -0.00436152150072151 , c = 0.579588917748918 , d = 1.72104545454544 - [20250519_003933.]: Creating plot No. 8 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG8.png - [20250519_003933.]: # CpG-site: CpG#8 + [20250519_004655.]: Creating plot No. 8 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG8.png + [20250519_004655.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = 263871870.982016 , a = 38159683.1948542 , b = 263871870.982016 , d = 45284547.6704654 - [20250519_003933.]: # CpG-site: CpG#8 + [20250519_004655.]: # CpG-site: CpG#8 Cubic: Using a = -1.50060965207632e-05 , b = 0.00447978143338143 , c = 0.534897737694404 , d = 9.56981481481482 - [20250519_003934.]: Creating plot No. 9 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG9.png - [20250519_003934.]: # CpG-site: CpG#9 + [20250519_004656.]: Creating plot No. 9 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG9.png + [20250519_004656.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -224404013.660658 , a = 51672526.9851193 , b = -224404013.660658 , d = 60201205.3472543 - [20250519_003934.]: # CpG-site: CpG#9 + [20250519_004656.]: # CpG-site: CpG#9 Cubic: Using a = 7.72300426487093e-05 , b = -0.00542281173641174 , c = 0.67899229597563 , d = 2.72413468013467 - [20250519_003936.]: Creating plot No. 10 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_rowmeans.png - [20250519_003936.]: # CpG-site: row_means + [20250519_004658.]: Creating plot No. 10 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_rowmeans.png + [20250519_004658.]: # CpG-site: row_means Hyperbolic: Using bias_weight = 40333655.1288316 , a = 44937623.5310126 , b = 40333655.1288316 , d = 55678831.3245349 - [20250519_003936.]: # CpG-site: row_means + [20250519_004658.]: # CpG-site: row_means Cubic: Using a = 2.88923726150392e-05 , b = -0.0006299592752926 , c = 0.600117857944524 , d = 5.17789562289563 - [20250519_003937.]: Entered 'solving_equations'-Function - [20250519_003937.]: Solving hyperbolic regression for CpG#1 + [20250519_004659.]: Entered 'solving_equations'-Function + [20250519_004659.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 6.03337011705089 - [20250519_003937.]: Samplename: 0 + [20250519_004659.]: Samplename: 0 Root: 6.033 --> Root in between the borders! Added to results. Hyperbolic solved: 14.7663097441095 - [20250519_003937.]: Samplename: 12.5 + [20250519_004659.]: Samplename: 12.5 Root: 14.766 --> Root in between the borders! Added to results. Hyperbolic solved: 23.1900365713551 - [20250519_003937.]: Samplename: 25 + [20250519_004659.]: Samplename: 25 Root: 23.19 --> Root in between the borders! Added to results. Hyperbolic solved: 33.8197153352119 - [20250519_003937.]: Samplename: 37.5 + [20250519_004659.]: Samplename: 37.5 Root: 33.82 --> Root in between the borders! Added to results. Hyperbolic solved: 46.5156833662696 - [20250519_003937.]: Samplename: 50 + [20250519_004659.]: Samplename: 50 Root: 46.516 --> Root in between the borders! Added to results. Hyperbolic solved: 60.2074431300908 - [20250519_003937.]: Samplename: 62.5 + [20250519_004659.]: Samplename: 62.5 Root: 60.207 --> Root in between the borders! Added to results. Hyperbolic solved: 71.6105733239506 - [20250519_003937.]: Samplename: 75 + [20250519_004659.]: Samplename: 75 Root: 71.611 --> Root in between the borders! Added to results. Hyperbolic solved: 80.2235915426204 - [20250519_003937.]: Samplename: 87.5 + [20250519_004659.]: Samplename: 87.5 Root: 80.224 --> Root in between the borders! Added to results. Hyperbolic solved: 113.633279788745 - [20250519_003937.]: Samplename: 100 + [20250519_004659.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA - [20250519_003937.]: Solving hyperbolic regression for CpG#2 + [20250519_004659.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 6.85513576175294 - [20250519_003937.]: Samplename: 0 + [20250519_004659.]: Samplename: 0 Root: 6.855 --> Root in between the borders! Added to results. Hyperbolic solved: 13.7133339963707 - [20250519_003937.]: Samplename: 12.5 + [20250519_004659.]: Samplename: 12.5 Root: 13.713 --> Root in between the borders! Added to results. Hyperbolic solved: 24.5830349243156 - [20250519_003937.]: Samplename: 25 + [20250519_004659.]: Samplename: 25 Root: 24.583 --> Root in between the borders! Added to results. Hyperbolic solved: 31.8277435330554 - [20250519_003937.]: Samplename: 37.5 + [20250519_004659.]: Samplename: 37.5 Root: 31.828 --> Root in between the borders! Added to results. Hyperbolic solved: 43.0277185945539 - [20250519_003937.]: Samplename: 50 + [20250519_004659.]: Samplename: 50 Root: 43.028 --> Root in between the borders! Added to results. Hyperbolic solved: 63.1097818281222 - [20250519_003937.]: Samplename: 62.5 + [20250519_004659.]: Samplename: 62.5 Root: 63.11 --> Root in between the borders! Added to results. Hyperbolic solved: 73.3036858899805 - [20250519_003937.]: Samplename: 75 + [20250519_004659.]: Samplename: 75 Root: 73.304 --> Root in between the borders! Added to results. Hyperbolic solved: 84.4808447799272 - [20250519_003937.]: Samplename: 87.5 + [20250519_004659.]: Samplename: 87.5 Root: 84.481 --> Root in between the borders! Added to results. Hyperbolic solved: 109.0987325611 - [20250519_003937.]: Samplename: 100 + [20250519_004659.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 109.099 --> '100 < root < 110' --> substitute 100 - [20250519_003937.]: Solving hyperbolic regression for CpG#3 + [20250519_004659.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 5.27473119209009 - [20250519_003937.]: Samplename: 0 + [20250519_004659.]: Samplename: 0 Root: 5.275 --> Root in between the borders! Added to results. Hyperbolic solved: 12.7004989900866 - [20250519_003937.]: Samplename: 12.5 + [20250519_004659.]: Samplename: 12.5 Root: 12.7 --> Root in between the borders! Added to results. Hyperbolic solved: 24.2988465005425 - [20250519_003937.]: Samplename: 25 + [20250519_004659.]: Samplename: 25 Root: 24.299 --> Root in between the borders! Added to results. Hyperbolic solved: 33.7074476249853 - [20250519_003937.]: Samplename: 37.5 + [20250519_004659.]: Samplename: 37.5 Root: 33.707 --> Root in between the borders! Added to results. Hyperbolic solved: 47.0168800224423 - [20250519_003937.]: Samplename: 50 + [20250519_004659.]: Samplename: 50 Root: 47.017 --> Root in between the borders! Added to results. Hyperbolic solved: 61.5718102366584 - [20250519_003937.]: Samplename: 62.5 + [20250519_004659.]: Samplename: 62.5 Root: 61.572 --> Root in between the borders! Added to results. Hyperbolic solved: 76.091389954119 - [20250519_003937.]: Samplename: 75 + [20250519_004659.]: Samplename: 75 Root: 76.091 --> Root in between the borders! Added to results. Hyperbolic solved: 79.6023799465703 - [20250519_003937.]: Samplename: 87.5 + [20250519_004659.]: Samplename: 87.5 Root: 79.602 --> Root in between the borders! Added to results. Hyperbolic solved: 109.736019452694 - [20250519_003937.]: Samplename: 100 + [20250519_004659.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 109.736 --> '100 < root < 110' --> substitute 100 - [20250519_003937.]: Solving hyperbolic regression for CpG#4 + [20250519_004659.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 5.07584235183922 - [20250519_003937.]: Samplename: 0 + [20250519_004659.]: Samplename: 0 Root: 5.076 --> Root in between the borders! Added to results. Hyperbolic solved: 14.2021272601476 - [20250519_003937.]: Samplename: 12.5 + [20250519_004659.]: Samplename: 12.5 Root: 14.202 --> Root in between the borders! Added to results. Hyperbolic solved: 23.203637284743 - [20250519_003937.]: Samplename: 25 + [20250519_004659.]: Samplename: 25 Root: 23.204 --> Root in between the borders! Added to results. Hyperbolic solved: 34.7960863609446 - [20250519_003937.]: Samplename: 37.5 + [20250519_004659.]: Samplename: 37.5 Root: 34.796 --> Root in between the borders! Added to results. Hyperbolic solved: 44.4638162018573 - [20250519_003937.]: Samplename: 50 + [20250519_004659.]: Samplename: 50 Root: 44.464 --> Root in between the borders! Added to results. Hyperbolic solved: 64.2755507391393 - [20250519_003937.]: Samplename: 62.5 + [20250519_004659.]: Samplename: 62.5 Root: 64.276 --> Root in between the borders! Added to results. Hyperbolic solved: 71.7670214785718 - [20250519_003937.]: Samplename: 75 + [20250519_004659.]: Samplename: 75 Root: 71.767 --> Root in between the borders! Added to results. Hyperbolic solved: 82.5599568822802 - [20250519_003937.]: Samplename: 87.5 + [20250519_004659.]: Samplename: 87.5 Root: 82.56 --> Root in between the borders! Added to results. Hyperbolic solved: 109.655967198106 - [20250519_003937.]: Samplename: 100 + [20250519_004659.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 109.656 --> '100 < root < 110' --> substitute 100 - [20250519_003938.]: Solving hyperbolic regression for CpG#5 + [20250519_004659.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 6.96411772281142 - [20250519_003938.]: Samplename: 0 + [20250519_004659.]: Samplename: 0 Root: 6.964 --> Root in between the borders! Added to results. Hyperbolic solved: 12.4462106087166 - [20250519_003938.]: Samplename: 12.5 + [20250519_004659.]: Samplename: 12.5 Root: 12.446 --> Root in between the borders! Added to results. Hyperbolic solved: 22.9738427992134 - [20250519_003938.]: Samplename: 25 + [20250519_004659.]: Samplename: 25 Root: 22.974 --> Root in between the borders! Added to results. Hyperbolic solved: 34.1311793989123 - [20250519_003938.]: Samplename: 37.5 + [20250519_004659.]: Samplename: 37.5 Root: 34.131 --> Root in between the borders! Added to results. Hyperbolic solved: 46.7456955019114 - [20250519_003938.]: Samplename: 50 + [20250519_004659.]: Samplename: 50 Root: 46.746 --> Root in between the borders! Added to results. Hyperbolic solved: 58.6021949543013 - [20250519_003938.]: Samplename: 62.5 + [20250519_004659.]: Samplename: 62.5 Root: 58.602 --> Root in between the borders! Added to results. Hyperbolic solved: 74.5100864344263 - [20250519_003938.]: Samplename: 75 + [20250519_004659.]: Samplename: 75 Root: 74.51 --> Root in between the borders! Added to results. Hyperbolic solved: 86.8549692943716 - [20250519_003938.]: Samplename: 87.5 + [20250519_004659.]: Samplename: 87.5 Root: 86.855 --> Root in between the borders! Added to results. Hyperbolic solved: 106.771715286513 - [20250519_003938.]: Samplename: 100 + [20250519_004659.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 106.772 --> '100 < root < 110' --> substitute 100 - [20250519_003938.]: Solving hyperbolic regression for CpG#6 + [20250519_004659.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 6.82248549455073 - [20250519_003938.]: Samplename: 0 + [20250519_004659.]: Samplename: 0 Root: 6.822 --> Root in between the borders! Added to results. Hyperbolic solved: 14.2907258003629 - [20250519_003938.]: Samplename: 12.5 + [20250519_004659.]: Samplename: 12.5 Root: 14.291 --> Root in between the borders! Added to results. Hyperbolic solved: 24.7343949718638 - [20250519_003938.]: Samplename: 25 + [20250519_004659.]: Samplename: 25 Root: 24.734 --> Root in between the borders! Added to results. Hyperbolic solved: 31.7110486196861 - [20250519_003938.]: Samplename: 37.5 + [20250519_004659.]: Samplename: 37.5 Root: 31.711 --> Root in between the borders! Added to results. Hyperbolic solved: 44.7745870553137 - [20250519_003938.]: Samplename: 50 + [20250519_004659.]: Samplename: 50 Root: 44.775 --> Root in between the borders! Added to results. Hyperbolic solved: 60.1336283719223 - [20250519_003938.]: Samplename: 62.5 + [20250519_004659.]: Samplename: 62.5 Root: 60.134 --> Root in between the borders! Added to results. Hyperbolic solved: 70.3292384995337 - [20250519_003938.]: Samplename: 75 + [20250519_004659.]: Samplename: 75 Root: 70.329 --> Root in between the borders! Added to results. Hyperbolic solved: 88.2655480529845 - [20250519_003938.]: Samplename: 87.5 + [20250519_004659.]: Samplename: 87.5 Root: 88.266 --> Root in between the borders! Added to results. Hyperbolic solved: 108.938360440145 - [20250519_003938.]: Samplename: 100 + [20250519_004659.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 108.938 --> '100 < root < 110' --> substitute 100 - [20250519_003938.]: Solving hyperbolic regression for CpG#7 + [20250519_004659.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 6.75841824784972 - [20250519_003938.]: Samplename: 0 + [20250519_004659.]: Samplename: 0 Root: 6.758 --> Root in between the borders! Added to results. Hyperbolic solved: 13.6258602138932 - [20250519_003938.]: Samplename: 12.5 + [20250519_004659.]: Samplename: 12.5 Root: 13.626 --> Root in between the borders! Added to results. Hyperbolic solved: 23.2672219789518 - [20250519_003938.]: Samplename: 25 + [20250519_004659.]: Samplename: 25 Root: 23.267 --> Root in between the borders! Added to results. Hyperbolic solved: 33.4287658578254 - [20250519_003938.]: Samplename: 37.5 + [20250519_004659.]: Samplename: 37.5 Root: 33.429 --> Root in between the borders! Added to results. Hyperbolic solved: 47.5236851621158 - [20250519_003938.]: Samplename: 50 + [20250519_004659.]: Samplename: 50 Root: 47.524 --> Root in between the borders! Added to results. Hyperbolic solved: 60.5558050598499 - [20250519_003938.]: Samplename: 62.5 + [20250519_004659.]: Samplename: 62.5 Root: 60.556 --> Root in between the borders! Added to results. Hyperbolic solved: 72.3896034879297 - [20250519_003938.]: Samplename: 75 + [20250519_004659.]: Samplename: 75 Root: 72.39 --> Root in between the borders! Added to results. Hyperbolic solved: 77.2628610417933 - [20250519_003938.]: Samplename: 87.5 + [20250519_004659.]: Samplename: 87.5 Root: 77.263 --> Root in between the borders! Added to results. Hyperbolic solved: 115.187789384315 - [20250519_003938.]: Samplename: 100 + [20250519_004659.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA - [20250519_003938.]: Solving hyperbolic regression for CpG#8 + [20250519_004659.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 5.74253131510683 - [20250519_003938.]: Samplename: 0 + [20250519_004659.]: Samplename: 0 Root: 5.743 --> Root in between the borders! Added to results. Hyperbolic solved: 10.9055216254216 - [20250519_003938.]: Samplename: 12.5 + [20250519_004659.]: Samplename: 12.5 Root: 10.906 --> Root in between the borders! Added to results. Hyperbolic solved: 24.5329419436721 - [20250519_003938.]: Samplename: 25 + [20250519_004659.]: Samplename: 25 Root: 24.533 --> Root in between the borders! Added to results. Hyperbolic solved: 32.1516426249373 - [20250519_003938.]: Samplename: 37.5 + [20250519_004659.]: Samplename: 37.5 Root: 32.152 --> Root in between the borders! Added to results. Hyperbolic solved: 49.0919870496865 - [20250519_003938.]: Samplename: 50 + [20250519_004659.]: Samplename: 50 Root: 49.092 --> Root in between the borders! Added to results. Hyperbolic solved: 62.3843810658735 - [20250519_003938.]: Samplename: 62.5 + [20250519_004700.]: Samplename: 62.5 Root: 62.384 --> Root in between the borders! Added to results. Hyperbolic solved: 77.6235905386846 - [20250519_003938.]: Samplename: 75 + [20250519_004700.]: Samplename: 75 Root: 77.624 --> Root in between the borders! Added to results. Hyperbolic solved: 80.9968312605936 - [20250519_003938.]: Samplename: 87.5 + [20250519_004700.]: Samplename: 87.5 Root: 80.997 --> Root in between the borders! Added to results. Hyperbolic solved: 106.570581137258 - [20250519_003938.]: Samplename: 100 + [20250519_004700.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 106.571 --> '100 < root < 110' --> substitute 100 - [20250519_003938.]: Solving hyperbolic regression for CpG#9 + [20250519_004700.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 8.66981641228949 - [20250519_003938.]: Samplename: 0 + [20250519_004700.]: Samplename: 0 Root: 8.67 --> Root in between the borders! Added to results. Hyperbolic solved: 14.9680920996829 - [20250519_003938.]: Samplename: 12.5 + [20250519_004700.]: Samplename: 12.5 Root: 14.968 --> Root in between the borders! Added to results. Hyperbolic solved: 24.6496838227806 - [20250519_003938.]: Samplename: 25 + [20250519_004700.]: Samplename: 25 Root: 24.65 --> Root in between the borders! Added to results. Hyperbolic solved: 31.5444702961411 - [20250519_003938.]: Samplename: 37.5 + [20250519_004700.]: Samplename: 37.5 Root: 31.544 --> Root in between the borders! Added to results. Hyperbolic solved: 44.0944303975514 - [20250519_003938.]: Samplename: 50 + [20250519_004700.]: Samplename: 50 Root: 44.094 --> Root in between the borders! Added to results. Hyperbolic solved: 57.2478940106457 - [20250519_003938.]: Samplename: 62.5 + [20250519_004700.]: Samplename: 62.5 Root: 57.248 --> Root in between the borders! Added to results. Hyperbolic solved: 69.1518417879612 - [20250519_003938.]: Samplename: 75 + [20250519_004700.]: Samplename: 75 Root: 69.152 --> Root in between the borders! Added to results. Hyperbolic solved: 83.8189177560508 - [20250519_003938.]: Samplename: 87.5 + [20250519_004700.]: Samplename: 87.5 Root: 83.819 --> Root in between the borders! Added to results. Hyperbolic solved: 115.854857900091 - [20250519_003938.]: Samplename: 100 + [20250519_004700.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA - [20250519_003938.]: Solving hyperbolic regression for row_means + [20250519_004700.]: Solving hyperbolic regression for row_means Hyperbolic solved: 6.47354965864506 - [20250519_003938.]: Samplename: 0 + [20250519_004700.]: Samplename: 0 Root: 6.474 --> Root in between the borders! Added to results. Hyperbolic solved: 13.5375540918194 - [20250519_003938.]: Samplename: 12.5 + [20250519_004700.]: Samplename: 12.5 Root: 13.538 --> Root in between the borders! Added to results. Hyperbolic solved: 23.976024251167 - [20250519_003938.]: Samplename: 25 + [20250519_004700.]: Samplename: 25 Root: 23.976 --> Root in between the borders! Added to results. Hyperbolic solved: 32.9614416466039 - [20250519_003938.]: Samplename: 37.5 + [20250519_004700.]: Samplename: 37.5 Root: 32.961 --> Root in between the borders! Added to results. Hyperbolic solved: 45.8185453601259 - [20250519_003938.]: Samplename: 50 + [20250519_004700.]: Samplename: 50 Root: 45.819 --> Root in between the borders! Added to results. Hyperbolic solved: 60.9295837234236 - [20250519_003938.]: Samplename: 62.5 + [20250519_004700.]: Samplename: 62.5 Root: 60.93 --> Root in between the borders! Added to results. Hyperbolic solved: 72.9250251350637 - [20250519_003938.]: Samplename: 75 + [20250519_004700.]: Samplename: 75 Root: 72.925 --> Root in between the borders! Added to results. Hyperbolic solved: 82.8721280175692 - [20250519_003938.]: Samplename: 87.5 + [20250519_004700.]: Samplename: 87.5 Root: 82.872 --> Root in between the borders! Added to results. Hyperbolic solved: 110.506149316569 - [20250519_003938.]: Samplename: 100 + [20250519_004700.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA - [20250519_003938.]: + [20250519_004700.]: ### Starting with regression calculations ### - [20250519_003938.]: Entered 'regression_type1'-Function - [20250519_003938.]: # CpG-site: CpG#1 + [20250519_004700.]: Entered 'regression_type1'-Function + [20250519_004700.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)c(6.03337011705089, 14.7663097441095, 23.1900365713551, 33.8197153352119, 46.5156833662696, 60.2074431300908, 71.6105733239506, 80.2235915426204)c(6.03337011705089, 2.2663097441095, 1.8099634286449, 3.6802846647881, 3.4843166337304, 2.2925568699092, 3.3894266760494, 7.2764084573796)c(NA, 18.130477952876, 7.2398537145796, 9.81409243943492, 6.9686332674608, 3.66809099185472, 4.51923556806586, 8.3158953798624) - [20250519_003938.]: Logging df_agg: CpG#1 - [20250519_003938.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)[20250519_003938.]: c(6.03337011705089, 14.7663097441095, 23.1900365713551, 33.8197153352119, 46.5156833662696, 60.2074431300908, 71.6105733239506, 80.2235915426204)[20250519_003938.]: c(6.03337011705089, 2.2663097441095, 1.8099634286449, 3.6802846647881, 3.4843166337304, 2.2925568699092, 3.3894266760494, 7.2764084573796)[20250519_003938.]: c(NA, 18.130477952876, 7.2398537145796, 9.81409243943492, 6.9686332674608, 3.66809099185472, 4.51923556806586, 8.3158953798624) - [20250519_003938.]: Entered 'hyperbolic_regression'-Function - [20250519_003938.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003938.]: Entered 'cubic_regression'-Function - [20250519_003938.]: 'cubic_regression': minmax = FALSE - [20250519_003938.]: # CpG-site: CpG#2 + [20250519_004700.]: Logging df_agg: CpG#1 + [20250519_004700.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)[20250519_004700.]: c(6.03337011705089, 14.7663097441095, 23.1900365713551, 33.8197153352119, 46.5156833662696, 60.2074431300908, 71.6105733239506, 80.2235915426204)[20250519_004700.]: c(6.03337011705089, 2.2663097441095, 1.8099634286449, 3.6802846647881, 3.4843166337304, 2.2925568699092, 3.3894266760494, 7.2764084573796)[20250519_004700.]: c(NA, 18.130477952876, 7.2398537145796, 9.81409243943492, 6.9686332674608, 3.66809099185472, 4.51923556806586, 8.3158953798624) + [20250519_004700.]: Entered 'hyperbolic_regression'-Function + [20250519_004700.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004700.]: Entered 'cubic_regression'-Function + [20250519_004700.]: 'cubic_regression': minmax = FALSE + [20250519_004700.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.85513576175294, 13.7133339963707, 24.5830349243156, 31.8277435330554, 43.0277185945539, 63.1097818281222, 73.3036858899805, 84.4808447799272, 100)c(6.85513576175294, 1.2133339963707, 0.416965075684399, 5.6722564669446, 6.9722814054461, 0.609781828122202, 1.6963141100195, 3.0191552200728, 0)c(NA, 9.7066719709656, 1.66786030273759, 15.1260172451856, 13.9445628108922, 0.975650924995523, 2.26175214669267, 3.45046310865463, 0) - [20250519_003938.]: Logging df_agg: CpG#2 - [20250519_003938.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003938.]: c(6.85513576175294, 13.7133339963707, 24.5830349243156, 31.8277435330554, 43.0277185945539, 63.1097818281222, 73.3036858899805, 84.4808447799272, 100)[20250519_003938.]: c(6.85513576175294, 1.2133339963707, 0.416965075684399, 5.6722564669446, 6.9722814054461, 0.609781828122202, 1.6963141100195, 3.0191552200728, 0)[20250519_003938.]: c(NA, 9.7066719709656, 1.66786030273759, 15.1260172451856, 13.9445628108922, 0.975650924995523, 2.26175214669267, 3.45046310865463, 0) - [20250519_003938.]: Entered 'hyperbolic_regression'-Function - [20250519_003938.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003938.]: Entered 'cubic_regression'-Function - [20250519_003938.]: 'cubic_regression': minmax = FALSE - [20250519_003938.]: # CpG-site: CpG#3 + [20250519_004700.]: Logging df_agg: CpG#2 + [20250519_004700.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004700.]: c(6.85513576175294, 13.7133339963707, 24.5830349243156, 31.8277435330554, 43.0277185945539, 63.1097818281222, 73.3036858899805, 84.4808447799272, 100)[20250519_004700.]: c(6.85513576175294, 1.2133339963707, 0.416965075684399, 5.6722564669446, 6.9722814054461, 0.609781828122202, 1.6963141100195, 3.0191552200728, 0)[20250519_004700.]: c(NA, 9.7066719709656, 1.66786030273759, 15.1260172451856, 13.9445628108922, 0.975650924995523, 2.26175214669267, 3.45046310865463, 0) + [20250519_004700.]: Entered 'hyperbolic_regression'-Function + [20250519_004700.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004700.]: Entered 'cubic_regression'-Function + [20250519_004700.]: 'cubic_regression': minmax = FALSE + [20250519_004700.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.27473119209009, 12.7004989900866, 24.2988465005425, 33.7074476249853, 47.0168800224423, 61.5718102366584, 76.091389954119, 79.6023799465703, 100)c(5.27473119209009, 0.200498990086601, 0.701153499457501, 3.7925523750147, 2.9831199775577, 0.928189763341599, 1.09138995411899, 7.8976200534297, 0)c(NA, 1.60399192069281, 2.80461399783, 10.1134730000392, 5.9662399551154, 1.48510362134656, 1.45518660549199, 9.02585148963395, 0) - [20250519_003938.]: Logging df_agg: CpG#3 - [20250519_003938.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003938.]: c(5.27473119209009, 12.7004989900866, 24.2988465005425, 33.7074476249853, 47.0168800224423, 61.5718102366584, 76.091389954119, 79.6023799465703, 100)[20250519_003938.]: c(5.27473119209009, 0.200498990086601, 0.701153499457501, 3.7925523750147, 2.9831199775577, 0.928189763341599, 1.09138995411899, 7.8976200534297, 0)[20250519_003938.]: c(NA, 1.60399192069281, 2.80461399783, 10.1134730000392, 5.9662399551154, 1.48510362134656, 1.45518660549199, 9.02585148963395, 0) - [20250519_003938.]: Entered 'hyperbolic_regression'-Function - [20250519_003938.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003938.]: Entered 'cubic_regression'-Function - [20250519_003938.]: 'cubic_regression': minmax = FALSE - [20250519_003938.]: # CpG-site: CpG#4 + [20250519_004700.]: Logging df_agg: CpG#3 + [20250519_004700.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004700.]: c(5.27473119209009, 12.7004989900866, 24.2988465005425, 33.7074476249853, 47.0168800224423, 61.5718102366584, 76.091389954119, 79.6023799465703, 100)[20250519_004700.]: c(5.27473119209009, 0.200498990086601, 0.701153499457501, 3.7925523750147, 2.9831199775577, 0.928189763341599, 1.09138995411899, 7.8976200534297, 0)[20250519_004700.]: c(NA, 1.60399192069281, 2.80461399783, 10.1134730000392, 5.9662399551154, 1.48510362134656, 1.45518660549199, 9.02585148963395, 0) + [20250519_004700.]: Entered 'hyperbolic_regression'-Function + [20250519_004700.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004700.]: Entered 'cubic_regression'-Function + [20250519_004700.]: 'cubic_regression': minmax = FALSE + [20250519_004700.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.07584235183922, 14.2021272601476, 23.203637284743, 34.7960863609446, 44.4638162018573, 64.2755507391393, 71.7670214785718, 82.5599568822802, 100)c(5.07584235183922, 1.7021272601476, 1.796362715257, 2.7039136390554, 5.5361837981427, 1.77555073913931, 3.23297852142819, 4.9400431177198, 0)c(NA, 13.6170180811808, 7.185450861028, 7.2104363708144, 11.0723675962854, 2.84088118262289, 4.31063802857093, 5.64576356310834, 0) - [20250519_003938.]: Logging df_agg: CpG#4 - [20250519_003938.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003938.]: c(5.07584235183922, 14.2021272601476, 23.203637284743, 34.7960863609446, 44.4638162018573, 64.2755507391393, 71.7670214785718, 82.5599568822802, 100)[20250519_003938.]: c(5.07584235183922, 1.7021272601476, 1.796362715257, 2.7039136390554, 5.5361837981427, 1.77555073913931, 3.23297852142819, 4.9400431177198, 0)[20250519_003938.]: c(NA, 13.6170180811808, 7.185450861028, 7.2104363708144, 11.0723675962854, 2.84088118262289, 4.31063802857093, 5.64576356310834, 0) - [20250519_003938.]: Entered 'hyperbolic_regression'-Function - [20250519_003938.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003938.]: Entered 'cubic_regression'-Function - [20250519_003938.]: 'cubic_regression': minmax = FALSE - [20250519_003938.]: # CpG-site: CpG#5 + [20250519_004700.]: Logging df_agg: CpG#4 + [20250519_004700.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004700.]: c(5.07584235183922, 14.2021272601476, 23.203637284743, 34.7960863609446, 44.4638162018573, 64.2755507391393, 71.7670214785718, 82.5599568822802, 100)[20250519_004700.]: c(5.07584235183922, 1.7021272601476, 1.796362715257, 2.7039136390554, 5.5361837981427, 1.77555073913931, 3.23297852142819, 4.9400431177198, 0)[20250519_004700.]: c(NA, 13.6170180811808, 7.185450861028, 7.2104363708144, 11.0723675962854, 2.84088118262289, 4.31063802857093, 5.64576356310834, 0) + [20250519_004700.]: Entered 'hyperbolic_regression'-Function + [20250519_004700.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004700.]: Entered 'cubic_regression'-Function + [20250519_004700.]: 'cubic_regression': minmax = FALSE + [20250519_004700.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.96411772281142, 12.4462106087166, 22.9738427992134, 34.1311793989123, 46.7456955019114, 58.6021949543013, 74.5100864344263, 86.8549692943716, 100)c(6.96411772281142, 0.0537893912834004, 2.0261572007866, 3.3688206010877, 3.2543044980886, 3.8978050456987, 0.489913565573701, 0.6450307056284, 0)c(NA, 0.430315130267203, 8.1046288031464, 8.98352160290053, 6.5086089961772, 6.23648807311793, 0.653218087431602, 0.7371779492896, 0) - [20250519_003938.]: Logging df_agg: CpG#5 - [20250519_003938.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003938.]: c(6.96411772281142, 12.4462106087166, 22.9738427992134, 34.1311793989123, 46.7456955019114, 58.6021949543013, 74.5100864344263, 86.8549692943716, 100)[20250519_003938.]: c(6.96411772281142, 0.0537893912834004, 2.0261572007866, 3.3688206010877, 3.2543044980886, 3.8978050456987, 0.489913565573701, 0.6450307056284, 0)[20250519_003938.]: c(NA, 0.430315130267203, 8.1046288031464, 8.98352160290053, 6.5086089961772, 6.23648807311793, 0.653218087431602, 0.7371779492896, 0) - [20250519_003938.]: Entered 'hyperbolic_regression'-Function - [20250519_003938.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003938.]: Entered 'cubic_regression'-Function - [20250519_003938.]: 'cubic_regression': minmax = FALSE - [20250519_003938.]: # CpG-site: CpG#6 + [20250519_004700.]: Logging df_agg: CpG#5 + [20250519_004700.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004700.]: c(6.96411772281142, 12.4462106087166, 22.9738427992134, 34.1311793989123, 46.7456955019114, 58.6021949543013, 74.5100864344263, 86.8549692943716, 100)[20250519_004700.]: c(6.96411772281142, 0.0537893912834004, 2.0261572007866, 3.3688206010877, 3.2543044980886, 3.8978050456987, 0.489913565573701, 0.6450307056284, 0)[20250519_004700.]: c(NA, 0.430315130267203, 8.1046288031464, 8.98352160290053, 6.5086089961772, 6.23648807311793, 0.653218087431602, 0.7371779492896, 0) + [20250519_004700.]: Entered 'hyperbolic_regression'-Function + [20250519_004700.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004700.]: Entered 'cubic_regression'-Function + [20250519_004700.]: 'cubic_regression': minmax = FALSE + [20250519_004700.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.82248549455073, 14.2907258003629, 24.7343949718638, 31.7110486196861, 44.7745870553137, 60.1336283719223, 70.3292384995337, 88.2655480529845, 100)c(6.82248549455073, 1.7907258003629, 0.2656050281362, 5.7889513803139, 5.2254129446863, 2.3663716280777, 4.6707615004663, 0.765548052984499, 0)c(NA, 14.3258064029032, 1.0624201125448, 15.4372036808371, 10.4508258893726, 3.78619460492432, 6.22768200062173, 0.874912060553714, 0) - [20250519_003938.]: Logging df_agg: CpG#6 - [20250519_003938.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003938.]: c(6.82248549455073, 14.2907258003629, 24.7343949718638, 31.7110486196861, 44.7745870553137, 60.1336283719223, 70.3292384995337, 88.2655480529845, 100)[20250519_003938.]: c(6.82248549455073, 1.7907258003629, 0.2656050281362, 5.7889513803139, 5.2254129446863, 2.3663716280777, 4.6707615004663, 0.765548052984499, 0)[20250519_003938.]: c(NA, 14.3258064029032, 1.0624201125448, 15.4372036808371, 10.4508258893726, 3.78619460492432, 6.22768200062173, 0.874912060553714, 0) - [20250519_003938.]: Entered 'hyperbolic_regression'-Function - [20250519_003938.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003938.]: Entered 'cubic_regression'-Function - [20250519_003938.]: 'cubic_regression': minmax = FALSE - [20250519_003938.]: # CpG-site: CpG#7 + [20250519_004700.]: Logging df_agg: CpG#6 + [20250519_004700.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004700.]: c(6.82248549455073, 14.2907258003629, 24.7343949718638, 31.7110486196861, 44.7745870553137, 60.1336283719223, 70.3292384995337, 88.2655480529845, 100)[20250519_004700.]: c(6.82248549455073, 1.7907258003629, 0.2656050281362, 5.7889513803139, 5.2254129446863, 2.3663716280777, 4.6707615004663, 0.765548052984499, 0)[20250519_004700.]: c(NA, 14.3258064029032, 1.0624201125448, 15.4372036808371, 10.4508258893726, 3.78619460492432, 6.22768200062173, 0.874912060553714, 0) + [20250519_004700.]: Entered 'hyperbolic_regression'-Function + [20250519_004700.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004700.]: Entered 'cubic_regression'-Function + [20250519_004700.]: 'cubic_regression': minmax = FALSE + [20250519_004700.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)c(6.75841824784972, 13.6258602138932, 23.2672219789518, 33.4287658578254, 47.5236851621158, 60.5558050598499, 72.3896034879297, 77.2628610417933)c(6.75841824784972, 1.1258602138932, 1.7327780210482, 4.0712341421746, 2.4763148378842, 1.9441949401501, 2.6103965120703, 10.2371389582067)c(NA, 9.0068817111456, 6.9311120841928, 10.8566243791323, 4.95262967576841, 3.11071190424016, 3.48052868276039, 11.6995873808077) - [20250519_003938.]: Logging df_agg: CpG#7 - [20250519_003938.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)[20250519_003938.]: c(6.75841824784972, 13.6258602138932, 23.2672219789518, 33.4287658578254, 47.5236851621158, 60.5558050598499, 72.3896034879297, 77.2628610417933)[20250519_003938.]: c(6.75841824784972, 1.1258602138932, 1.7327780210482, 4.0712341421746, 2.4763148378842, 1.9441949401501, 2.6103965120703, 10.2371389582067)[20250519_003938.]: c(NA, 9.0068817111456, 6.9311120841928, 10.8566243791323, 4.95262967576841, 3.11071190424016, 3.48052868276039, 11.6995873808077) - [20250519_003938.]: Entered 'hyperbolic_regression'-Function - [20250519_003938.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003938.]: Entered 'cubic_regression'-Function - [20250519_003938.]: 'cubic_regression': minmax = FALSE - [20250519_003938.]: # CpG-site: CpG#8 + [20250519_004700.]: Logging df_agg: CpG#7 + [20250519_004700.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)[20250519_004700.]: c(6.75841824784972, 13.6258602138932, 23.2672219789518, 33.4287658578254, 47.5236851621158, 60.5558050598499, 72.3896034879297, 77.2628610417933)[20250519_004700.]: c(6.75841824784972, 1.1258602138932, 1.7327780210482, 4.0712341421746, 2.4763148378842, 1.9441949401501, 2.6103965120703, 10.2371389582067)[20250519_004700.]: c(NA, 9.0068817111456, 6.9311120841928, 10.8566243791323, 4.95262967576841, 3.11071190424016, 3.48052868276039, 11.6995873808077) + [20250519_004700.]: Entered 'hyperbolic_regression'-Function + [20250519_004700.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004700.]: Entered 'cubic_regression'-Function + [20250519_004700.]: 'cubic_regression': minmax = FALSE + [20250519_004700.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.74253131510683, 10.9055216254216, 24.5329419436721, 32.1516426249373, 49.0919870496865, 62.3843810658735, 77.6235905386846, 80.9968312605936, 100)c(5.74253131510683, 1.5944783745784, 0.4670580563279, 5.3483573750627, 0.908012950313498, 0.115618934126502, 2.6235905386846, 6.50316873940641, 0)c(NA, 12.7558269966272, 1.8682322253116, 14.2622863335005, 1.816025900627, 0.184990294602403, 3.49812071824613, 7.43219284503589, 0) - [20250519_003938.]: Logging df_agg: CpG#8 - [20250519_003938.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_003938.]: c(5.74253131510683, 10.9055216254216, 24.5329419436721, 32.1516426249373, 49.0919870496865, 62.3843810658735, 77.6235905386846, 80.9968312605936, 100)[20250519_003938.]: c(5.74253131510683, 1.5944783745784, 0.4670580563279, 5.3483573750627, 0.908012950313498, 0.115618934126502, 2.6235905386846, 6.50316873940641, 0)[20250519_003938.]: c(NA, 12.7558269966272, 1.8682322253116, 14.2622863335005, 1.816025900627, 0.184990294602403, 3.49812071824613, 7.43219284503589, 0) - [20250519_003938.]: Entered 'hyperbolic_regression'-Function - [20250519_003938.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003938.]: Entered 'cubic_regression'-Function - [20250519_003938.]: 'cubic_regression': minmax = FALSE - [20250519_003938.]: # CpG-site: CpG#9 + [20250519_004700.]: Logging df_agg: CpG#8 + [20250519_004700.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004700.]: c(5.74253131510683, 10.9055216254216, 24.5329419436721, 32.1516426249373, 49.0919870496865, 62.3843810658735, 77.6235905386846, 80.9968312605936, 100)[20250519_004700.]: c(5.74253131510683, 1.5944783745784, 0.4670580563279, 5.3483573750627, 0.908012950313498, 0.115618934126502, 2.6235905386846, 6.50316873940641, 0)[20250519_004700.]: c(NA, 12.7558269966272, 1.8682322253116, 14.2622863335005, 1.816025900627, 0.184990294602403, 3.49812071824613, 7.43219284503589, 0) + [20250519_004700.]: Entered 'hyperbolic_regression'-Function + [20250519_004700.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004700.]: Entered 'cubic_regression'-Function + [20250519_004700.]: 'cubic_regression': minmax = FALSE + [20250519_004700.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)c(8.66981641228949, 14.9680920996829, 24.6496838227806, 31.5444702961411, 44.0944303975514, 57.2478940106457, 69.1518417879612, 83.8189177560508)c(8.66981641228949, 2.4680920996829, 0.350316177219401, 5.9555297038589, 5.9055696024486, 5.2521059893543, 5.8481582120388, 3.68108224394921)c(NA, 19.7447367974632, 1.4012647088776, 15.8814125436237, 11.8111392048972, 8.40336958296688, 7.7975442827184, 4.20695113594195) - [20250519_003938.]: Logging df_agg: CpG#9 - [20250519_003938.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)[20250519_003938.]: c(8.66981641228949, 14.9680920996829, 24.6496838227806, 31.5444702961411, 44.0944303975514, 57.2478940106457, 69.1518417879612, 83.8189177560508)[20250519_003938.]: c(8.66981641228949, 2.4680920996829, 0.350316177219401, 5.9555297038589, 5.9055696024486, 5.2521059893543, 5.8481582120388, 3.68108224394921)[20250519_003938.]: c(NA, 19.7447367974632, 1.4012647088776, 15.8814125436237, 11.8111392048972, 8.40336958296688, 7.7975442827184, 4.20695113594195) - [20250519_003938.]: Entered 'hyperbolic_regression'-Function - [20250519_003938.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003938.]: Entered 'cubic_regression'-Function - [20250519_003938.]: 'cubic_regression': minmax = FALSE - [20250519_003938.]: # CpG-site: row_means + [20250519_004700.]: Logging df_agg: CpG#9 + [20250519_004700.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)[20250519_004700.]: c(8.66981641228949, 14.9680920996829, 24.6496838227806, 31.5444702961411, 44.0944303975514, 57.2478940106457, 69.1518417879612, 83.8189177560508)[20250519_004700.]: c(8.66981641228949, 2.4680920996829, 0.350316177219401, 5.9555297038589, 5.9055696024486, 5.2521059893543, 5.8481582120388, 3.68108224394921)[20250519_004700.]: c(NA, 19.7447367974632, 1.4012647088776, 15.8814125436237, 11.8111392048972, 8.40336958296688, 7.7975442827184, 4.20695113594195) + [20250519_004700.]: Entered 'hyperbolic_regression'-Function + [20250519_004700.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004700.]: Entered 'cubic_regression'-Function + [20250519_004700.]: 'cubic_regression': minmax = FALSE + [20250519_004700.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)c(6.47354965864506, 13.5375540918194, 23.976024251167, 32.9614416466039, 45.8185453601259, 60.9295837234236, 72.9250251350637, 82.8721280175692)c(6.47354965864506, 1.0375540918194, 1.023975748833, 4.5385583533961, 4.1814546398741, 1.5704162765764, 2.07497486493629, 4.6278719824308)c(NA, 8.3004327345552, 4.095902995332, 12.1028222757229, 8.36290927974819, 2.51266604252224, 2.76663315324839, 5.28899655134949) - [20250519_003938.]: Logging df_agg: row_means - [20250519_003938.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)[20250519_003938.]: c(6.47354965864506, 13.5375540918194, 23.976024251167, 32.9614416466039, 45.8185453601259, 60.9295837234236, 72.9250251350637, 82.8721280175692)[20250519_003938.]: c(6.47354965864506, 1.0375540918194, 1.023975748833, 4.5385583533961, 4.1814546398741, 1.5704162765764, 2.07497486493629, 4.6278719824308)[20250519_003938.]: c(NA, 8.3004327345552, 4.095902995332, 12.1028222757229, 8.36290927974819, 2.51266604252224, 2.76663315324839, 5.28899655134949) - [20250519_003938.]: Entered 'hyperbolic_regression'-Function - [20250519_003938.]: 'hyperbolic_regression': minmax = FALSE - [20250519_003938.]: Entered 'cubic_regression'-Function - [20250519_003938.]: 'cubic_regression': minmax = FALSE - [20250519_003939.]: ### Starting with plotting ### - [20250519_003939.]: Creating BiasCorrected (hyperbolic) plot No. 1 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG1_corrected_h.png - [20250519_003939.]: # CpG-site: CpG#1 + [20250519_004700.]: Logging df_agg: row_means + [20250519_004700.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)[20250519_004700.]: c(6.47354965864506, 13.5375540918194, 23.976024251167, 32.9614416466039, 45.8185453601259, 60.9295837234236, 72.9250251350637, 82.8721280175692)[20250519_004700.]: c(6.47354965864506, 1.0375540918194, 1.023975748833, 4.5385583533961, 4.1814546398741, 1.5704162765764, 2.07497486493629, 4.6278719824308)[20250519_004700.]: c(NA, 8.3004327345552, 4.095902995332, 12.1028222757229, 8.36290927974819, 2.51266604252224, 2.76663315324839, 5.28899655134949) + [20250519_004700.]: Entered 'hyperbolic_regression'-Function + [20250519_004700.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004700.]: Entered 'cubic_regression'-Function + [20250519_004700.]: 'cubic_regression': minmax = FALSE + [20250519_004701.]: ### Starting with plotting ### + [20250519_004701.]: Creating BiasCorrected (hyperbolic) plot No. 1 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG1_corrected_h.png + [20250519_004701.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = 170413234.729972 , a = 44157086.707734 , b = 170413234.729972 , d = 49999790.9017601 - [20250519_003939.]: # CpG-site: CpG#1 + [20250519_004701.]: # CpG-site: CpG#1 Cubic: Using a = -8.02902858014277e-05 , b = 0.0119973831380665 , c = 0.410459267079284 , d = 6.65096545514267 - [20250519_003941.]: Creating BiasCorrected (hyperbolic) plot No. 2 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG2_corrected_h.png - [20250519_003941.]: # CpG-site: CpG#2 + [20250519_004702.]: Creating BiasCorrected (hyperbolic) plot No. 2 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG2_corrected_h.png + [20250519_004702.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = 63519198.3583519 , a = 42702123.5062977 , b = 63519198.3583519 , d = 44879882.4102916 - [20250519_003941.]: # CpG-site: CpG#2 + [20250519_004702.]: # CpG-site: CpG#2 Cubic: Using a = -4.57917988885898e-05 , b = 0.00978975326683099 , c = 0.400365566888902 , d = 7.17765330717532 - [20250519_003942.]: Creating BiasCorrected (hyperbolic) plot No. 3 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG3_corrected_h.png - [20250519_003942.]: # CpG-site: CpG#3 + [20250519_004704.]: Creating BiasCorrected (hyperbolic) plot No. 3 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG3_corrected_h.png + [20250519_004704.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = 60249784.2121432 , a = 37717764.1245422 , b = 60249784.2121432 , d = 39783429.2823954 - [20250519_003942.]: # CpG-site: CpG#3 + [20250519_004704.]: # CpG-site: CpG#3 Cubic: Using a = -2.8548625146914e-05 , b = 0.00587956303749884 , c = 0.626869802626483 , d = 4.78057876315863 - [20250519_003943.]: Creating BiasCorrected (hyperbolic) plot No. 4 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG4_corrected_h.png - [20250519_003943.]: # CpG-site: CpG#4 + [20250519_004705.]: Creating BiasCorrected (hyperbolic) plot No. 4 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG4_corrected_h.png + [20250519_004705.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = 66787089.0359508 , a = 42175722.8129271 , b = 66787089.0359508 , d = 44465523.7860812 - [20250519_003943.]: # CpG-site: CpG#4 + [20250519_004705.]: # CpG-site: CpG#4 Cubic: Using a = -1.99135996570308e-05 , b = 0.00496936895290719 , c = 0.637632604471187 , d = 5.04633670060351 - [20250519_003945.]: Creating BiasCorrected (hyperbolic) plot No. 5 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG5_corrected_h.png - [20250519_003945.]: # CpG-site: CpG#5 + [20250519_004707.]: Creating BiasCorrected (hyperbolic) plot No. 5 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG5_corrected_h.png + [20250519_004707.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = 57501729.1236441 , a = 52618093.853152 , b = 57501729.1236441 , d = 54589535.3828564 - [20250519_003945.]: # CpG-site: CpG#5 + [20250519_004707.]: # CpG-site: CpG#5 Cubic: Using a = -4.67907948499236e-05 , b = 0.00978122250124343 , c = 0.42296364991001 , d = 6.61748738798833 - [20250519_003946.]: Creating BiasCorrected (hyperbolic) plot No. 6 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG6_corrected_h.png - [20250519_003946.]: # CpG-site: CpG#6 + [20250519_004708.]: Creating BiasCorrected (hyperbolic) plot No. 6 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG6_corrected_h.png + [20250519_004708.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = 62067256.5784193 , a = 42512767.1502548 , b = 62067256.5784193 , d = 44640742.1604705 - [20250519_003946.]: # CpG-site: CpG#6 + [20250519_004708.]: # CpG-site: CpG#6 Cubic: Using a = -2.66665530276184e-05 , b = 0.00740474965950072 , c = 0.461019694351388 , d = 7.23067904029629 - [20250519_003948.]: Creating BiasCorrected (hyperbolic) plot No. 7 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG7_corrected_h.png - [20250519_003948.]: # CpG-site: CpG#7 + [20250519_004710.]: Creating BiasCorrected (hyperbolic) plot No. 7 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG7_corrected_h.png + [20250519_004710.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = 162158036.04427 , a = 37148305.7394653 , b = 162158036.04427 , d = 42707972.2066412 - [20250519_003948.]: # CpG-site: CpG#7 + [20250519_004710.]: # CpG-site: CpG#7 Cubic: Using a = -0.000133937721266082 , b = 0.0182624909642956 , c = 0.234528921247686 , d = 7.29090555727988 - [20250519_003949.]: Creating BiasCorrected (hyperbolic) plot No. 8 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG8_corrected_h.png - [20250519_003949.]: # CpG-site: CpG#8 + [20250519_004711.]: Creating BiasCorrected (hyperbolic) plot No. 8 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG8_corrected_h.png + [20250519_004711.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = 54899408.7962443 , a = 51832403.6152701 , b = 54899408.7962443 , d = 53714624.7099072 - [20250519_003949.]: # CpG-site: CpG#8 + [20250519_004711.]: # CpG-site: CpG#8 Cubic: Using a = -5.74524696829791e-05 , b = 0.00998862677056794 , c = 0.502915955578275 , d = 4.90625922077111 - [20250519_003951.]: Creating BiasCorrected (hyperbolic) plot No. 9 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG9_corrected_h.png - [20250519_003951.]: # CpG-site: CpG#9 + [20250519_004712.]: Creating BiasCorrected (hyperbolic) plot No. 9 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG9_corrected_h.png + [20250519_004712.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = 170227220.836014 , a = 37110575.5394583 , b = 170227220.836014 , d = 42946902.7624502 - [20250519_003951.]: # CpG-site: CpG#9 + [20250519_004712.]: # CpG-site: CpG#9 Cubic: Using a = -1.38503636743099e-05 , b = 0.00591895875024497 , c = 0.445742549227169 , d = 8.73330013353405 - [20250519_003952.]: Creating BiasCorrected (hyperbolic) plot No. 10 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_rowmeans_corrected_h.png - [20250519_003952.]: # CpG-site: row_means + [20250519_004714.]: Creating BiasCorrected (hyperbolic) plot No. 10 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_rowmeans_corrected_h.png + [20250519_004714.]: # CpG-site: row_means Hyperbolic: Using bias_weight = 131846991.813373 , a = 45678337.0783695 , b = 131846991.813373 , d = 50198768.4229237 - [20250519_003952.]: # CpG-site: row_means + [20250519_004714.]: # CpG-site: row_means Cubic: Using a = -7.67916401132796e-05 , b = 0.0124890194887903 , c = 0.369098504101218 , d = 6.83740913189534 - [20250519_003953.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG1_corrected_h.png - [20250519_003955.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG2_corrected_h.png - [20250519_003956.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG3_corrected_h.png - [20250519_003957.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG4_corrected_h.png - [20250519_003959.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG5_corrected_h.png - [20250519_004000.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG6_corrected_h.png - [20250519_004001.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG7_corrected_h.png - [20250519_004003.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG8_corrected_h.png - [20250519_004004.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG9_corrected_h.png - [20250519_004005.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_rowmeans_corrected_h.png - [20250519_004007.]: Entered 'solving_equations'-Function - [20250519_004007.]: Solving cubic regression for CpG#1 + [20250519_004715.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG1_corrected_h.png + [20250519_004717.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG2_corrected_h.png + [20250519_004718.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG3_corrected_h.png + [20250519_004719.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG4_corrected_h.png + [20250519_004720.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG5_corrected_h.png + [20250519_004722.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG6_corrected_h.png + [20250519_004723.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG7_corrected_h.png + [20250519_004724.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG8_corrected_h.png + [20250519_004726.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG9_corrected_h.png + [20250519_004727.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_rowmeans_corrected_h.png + [20250519_004728.]: Entered 'solving_equations'-Function + [20250519_004728.]: Solving cubic regression for CpG#1 Coefficients: -1.03617340067344Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_004007.]: Samplename: 0 + [20250519_004728.]: Samplename: 0 Root: 1.334 --> Root in between the borders! Added to results. Coefficients: -8.34150673400678Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_004007.]: Samplename: 12.5 + [20250519_004728.]: Samplename: 12.5 Root: 11.446 --> Root in between the borders! Added to results. Coefficients: -15.3881734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_004007.]: Samplename: 25 + [20250519_004728.]: Samplename: 25 Root: 22.228 --> Root in between the borders! Added to results. Coefficients: -24.2801734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_004007.]: Samplename: 37.5 + [20250519_004728.]: Samplename: 37.5 Root: 36.374 --> Root in between the borders! Added to results. Coefficients: -34.9006734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_004007.]: Samplename: 50 + [20250519_004728.]: Samplename: 50 Root: 52.044 --> Root in between the borders! Added to results. Coefficients: -46.3541734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_004007.]: Samplename: 62.5 + [20250519_004728.]: Samplename: 62.5 Root: 66.144 --> Root in between the borders! Added to results. Coefficients: -55.8931734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_004007.]: Samplename: 75 + [20250519_004728.]: Samplename: 75 Root: 75.864 --> Root in between the borders! Added to results. Coefficients: -63.0981734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_004007.]: Samplename: 87.5 + [20250519_004728.]: Samplename: 87.5 Root: 82.254 --> Root in between the borders! Added to results. Coefficients: -91.0461734006735Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_004007.]: Samplename: 100 + [20250519_004728.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.877 --> '100 < root < 110' --> substitute 100 - [20250519_004007.]: Solving cubic regression for CpG#2 + [20250519_004728.]: Solving cubic regression for CpG#2 Coefficients: -0.283329966329966Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_004007.]: Samplename: 0 + [20250519_004728.]: Samplename: 0 Root: 0.549 --> Root in between the borders! Added to results. Coefficients: -6.33999663299663Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_004007.]: Samplename: 12.5 + [20250519_004728.]: Samplename: 12.5 Root: 11.533 --> Root in between the borders! Added to results. Coefficients: -15.93932996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_004007.]: Samplename: 25 + [20250519_004728.]: Samplename: 25 Root: 26.628 --> Root in between the borders! Added to results. Coefficients: -22.33732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_004007.]: Samplename: 37.5 + [20250519_004728.]: Samplename: 37.5 Root: 35.509 --> Root in between the borders! Added to results. Coefficients: -32.22832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_004007.]: Samplename: 50 + [20250519_004728.]: Samplename: 50 Root: 47.851 --> Root in between the borders! Added to results. Coefficients: -49.96332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_004007.]: Samplename: 62.5 + [20250519_004728.]: Samplename: 62.5 Root: 66.893 --> Root in between the borders! Added to results. Coefficients: -58.96582996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_004007.]: Samplename: 75 + [20250519_004728.]: Samplename: 75 Root: 75.431 --> Root in between the borders! Added to results. Coefficients: -68.8366632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_004007.]: Samplename: 87.5 + [20250519_004728.]: Samplename: 87.5 Root: 84.118 --> Root in between the borders! Added to results. Coefficients: -90.57732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_004007.]: Samplename: 100 + [20250519_004728.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.287 --> '100 < root < 110' --> substitute 100 - [20250519_004007.]: Solving cubic regression for CpG#3 + [20250519_004728.]: Solving cubic regression for CpG#3 Coefficients: -0.90294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_004007.]: Samplename: 0 + [20250519_004728.]: Samplename: 0 Root: 1.441 --> Root in between the borders! Added to results. Coefficients: -6.57294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_004007.]: Samplename: 12.5 + [20250519_004728.]: Samplename: 12.5 Root: 10.568 --> Root in between the borders! Added to results. Coefficients: -15.4289478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_004007.]: Samplename: 25 + [20250519_004728.]: Samplename: 25 Root: 24.796 --> Root in between the borders! Added to results. Coefficients: -22.6129478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_004007.]: Samplename: 37.5 + [20250519_004728.]: Samplename: 37.5 Root: 35.952 --> Root in between the borders! Added to results. Coefficients: -32.7754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_004007.]: Samplename: 50 + [20250519_004728.]: Samplename: 50 Root: 50.684 --> Root in between the borders! Added to results. Coefficients: -43.8889478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_004007.]: Samplename: 62.5 + [20250519_004728.]: Samplename: 62.5 Root: 65.142 --> Root in between the borders! Added to results. Coefficients: -54.9754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_004007.]: Samplename: 75 + [20250519_004728.]: Samplename: 75 Root: 77.905 --> Root in between the borders! Added to results. Coefficients: -57.6562811447812Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_004007.]: Samplename: 87.5 + [20250519_004728.]: Samplename: 87.5 Root: 80.767 --> Root in between the borders! Added to results. Coefficients: -80.6649478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_004007.]: Samplename: 100 + [20250519_004728.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.38 --> '100 < root < 110' --> substitute 100 - [20250519_004007.]: Solving cubic regression for CpG#4 + [20250519_004728.]: Solving cubic regression for CpG#4 Coefficients: -0.597449494949524Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_004007.]: Samplename: 0 + [20250519_004728.]: Samplename: 0 Root: 0.858 --> Root in between the borders! Added to results. Coefficients: -8.25278282828286Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_004007.]: Samplename: 12.5 + [20250519_004728.]: Samplename: 12.5 Root: 12.086 --> Root in between the borders! Added to results. Coefficients: -15.8034494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_004007.]: Samplename: 25 + [20250519_004728.]: Samplename: 25 Root: 23.316 --> Root in between the borders! Added to results. Coefficients: -25.5274494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_004007.]: Samplename: 37.5 + [20250519_004728.]: Samplename: 37.5 Root: 37.383 --> Root in between the borders! Added to results. Coefficients: -33.6369494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_004007.]: Samplename: 50 + [20250519_004728.]: Samplename: 50 Root: 48.353 --> Root in between the borders! Added to results. Coefficients: -50.2554494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_004007.]: Samplename: 62.5 + [20250519_004728.]: Samplename: 62.5 Root: 68.082 --> Root in between the borders! Added to results. Coefficients: -56.5394494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_004007.]: Samplename: 75 + [20250519_004728.]: Samplename: 75 Root: 74.615 --> Root in between the borders! Added to results. Coefficients: -65.5927828282829Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_004007.]: Samplename: 87.5 + [20250519_004728.]: Samplename: 87.5 Root: 83.254 --> Root in between the borders! Added to results. Coefficients: -88.3214494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_004007.]: Samplename: 100 + [20250519_004728.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.715 --> '100 < root < 110' --> substitute 100 - [20250519_004007.]: Solving cubic regression for CpG#5 + [20250519_004728.]: Solving cubic regression for CpG#5 Coefficients: -0.623961279461278Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_004007.]: Samplename: 0 + [20250519_004728.]: Samplename: 0 Root: 1.458 --> Root in between the borders! Added to results. Coefficients: -4.76796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_004007.]: Samplename: 12.5 + [20250519_004728.]: Samplename: 12.5 Root: 10.347 --> Root in between the borders! Added to results. Coefficients: -12.7259612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_004007.]: Samplename: 25 + [20250519_004728.]: Samplename: 25 Root: 24.815 --> Root in between the borders! Added to results. Coefficients: -21.1599612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_004007.]: Samplename: 37.5 + [20250519_004728.]: Samplename: 37.5 Root: 37.902 --> Root in between the borders! Added to results. Coefficients: -30.6954612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_004007.]: Samplename: 50 + [20250519_004728.]: Samplename: 50 Root: 50.977 --> Root in between the borders! Added to results. Coefficients: -39.6579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_004007.]: Samplename: 62.5 + [20250519_004728.]: Samplename: 62.5 Root: 62.126 --> Root in between the borders! Added to results. Coefficients: -51.6829612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_004007.]: Samplename: 75 + [20250519_004728.]: Samplename: 75 Root: 75.852 --> Root in between the borders! Added to results. Coefficients: -61.0146279461279Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_004007.]: Samplename: 87.5 + [20250519_004728.]: Samplename: 87.5 Root: 85.767 --> Root in between the borders! Added to results. Coefficients: -76.0699612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_004007.]: Samplename: 100 + [20250519_004728.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.743 --> '100 < root < 110' --> substitute 100 - [20250519_004007.]: Solving cubic regression for CpG#6 + [20250519_004728.]: Solving cubic regression for CpG#6 Coefficients: -0.196072390572403Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_004007.]: Samplename: 0 + [20250519_004728.]: Samplename: 0 Root: 0.349 --> Root in between the borders! Added to results. Coefficients: -6.73873905723907Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_004007.]: Samplename: 12.5 + [20250519_004728.]: Samplename: 12.5 Root: 11.718 --> Root in between the borders! Added to results. Coefficients: -15.8880723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_004007.]: Samplename: 25 + [20250519_004728.]: Samplename: 25 Root: 26.396 --> Root in between the borders! Added to results. Coefficients: -22.0000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_004007.]: Samplename: 37.5 + [20250519_004728.]: Samplename: 37.5 Root: 35.301 --> Root in between the borders! Added to results. Coefficients: -33.4445723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_004007.]: Samplename: 50 + [20250519_004728.]: Samplename: 50 Root: 50.134 --> Root in between the borders! Added to results. Coefficients: -46.9000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_004007.]: Samplename: 62.5 + [20250519_004728.]: Samplename: 62.5 Root: 64.993 --> Root in between the borders! Added to results. Coefficients: -55.8320723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_004007.]: Samplename: 75 + [20250519_004728.]: Samplename: 75 Root: 73.639 --> Root in between the borders! Added to results. Coefficients: -71.5454057239057Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_004007.]: Samplename: 87.5 + [20250519_004728.]: Samplename: 87.5 Root: 87.043 --> Root in between the borders! Added to results. Coefficients: -89.6560723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_004007.]: Samplename: 100 + [20250519_004728.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.329 --> '100 < root < 110' --> substitute 100 - [20250519_004007.]: Solving cubic regression for CpG#7 + [20250519_004728.]: Solving cubic regression for CpG#7 Coefficients: -1.21495454545456Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_004007.]: Samplename: 0 + [20250519_004728.]: Samplename: 0 Root: 2.13 --> Root in between the borders! Added to results. Coefficients: -5.39562121212123Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_004007.]: Samplename: 12.5 + [20250519_004728.]: Samplename: 12.5 Root: 9.973 --> Root in between the borders! Added to results. Coefficients: -11.2649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_004007.]: Samplename: 25 + [20250519_004728.]: Samplename: 25 Root: 22.206 --> Root in between the borders! Added to results. Coefficients: -17.4509545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_004007.]: Samplename: 37.5 + [20250519_004728.]: Samplename: 37.5 Root: 35.814 --> Root in between the borders! Added to results. Coefficients: -26.0314545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_004007.]: Samplename: 50 + [20250519_004728.]: Samplename: 50 Root: 53.28 --> Root in between the borders! Added to results. Coefficients: -33.9649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_004007.]: Samplename: 62.5 + [20250519_004728.]: Samplename: 62.5 Root: 66.598 --> Root in between the borders! Added to results. Coefficients: -41.1689545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_004007.]: Samplename: 75 + [20250519_004728.]: Samplename: 75 Root: 76.575 --> Root in between the borders! Added to results. Coefficients: -44.1356212121212Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_004007.]: Samplename: 87.5 + [20250519_004728.]: Samplename: 87.5 Root: 80.219 --> Root in between the borders! Added to results. Coefficients: -67.2229545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_004007.]: Samplename: 100 + [20250519_004728.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.506 --> '100 < root < 110' --> substitute 100 - [20250519_004007.]: Solving cubic regression for CpG#8 + [20250519_004728.]: Solving cubic regression for CpG#8 Coefficients: -1.09618518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_004007.]: Samplename: 0 + [20250519_004728.]: Samplename: 0 Root: 2.016 --> Root in between the borders! Added to results. Coefficients: -5.44685185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_004007.]: Samplename: 12.5 + [20250519_004728.]: Samplename: 12.5 Root: 9.458 --> Root in between the borders! Added to results. Coefficients: -16.9301851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_004007.]: Samplename: 25 + [20250519_004728.]: Samplename: 25 Root: 26.35 --> Root in between the borders! Added to results. Coefficients: -23.3501851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_004007.]: Samplename: 37.5 + [20250519_004728.]: Samplename: 37.5 Root: 34.728 --> Root in between the borders! Added to results. Coefficients: -37.6251851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_004007.]: Samplename: 50 + [20250519_004728.]: Samplename: 50 Root: 51.781 --> Root in between the borders! Added to results. Coefficients: -48.8261851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_004007.]: Samplename: 62.5 + [20250519_004728.]: Samplename: 62.5 Root: 64.192 --> Root in between the borders! Added to results. Coefficients: -61.6676851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_004007.]: Samplename: 75 + [20250519_004728.]: Samplename: 75 Root: 77.804 --> Root in between the borders! Added to results. Coefficients: -64.5101851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_004007.]: Samplename: 87.5 + [20250519_004728.]: Samplename: 87.5 Root: 80.758 --> Root in between the borders! Added to results. Coefficients: -86.0601851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_004007.]: Samplename: 100 + [20250519_004728.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.834 --> '100 < root < 110' --> substitute 100 - [20250519_004007.]: Solving cubic regression for CpG#9 + [20250519_004728.]: Solving cubic regression for CpG#9 Coefficients: -0.989865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_004007.]: Samplename: 0 + [20250519_004728.]: Samplename: 0 Root: 1.475 --> Root in between the borders! Added to results. Coefficients: -6.39586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_004007.]: Samplename: 12.5 + [20250519_004728.]: Samplename: 12.5 Root: 10.12 --> Root in between the borders! Added to results. Coefficients: -14.7058653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_004007.]: Samplename: 25 + [20250519_004728.]: Samplename: 25 Root: 24.844 --> Root in between the borders! Added to results. Coefficients: -20.6238653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_004007.]: Samplename: 37.5 + [20250519_004728.]: Samplename: 37.5 Root: 35.327 --> Root in between the borders! Added to results. Coefficients: -31.3958653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_004007.]: Samplename: 50 + [20250519_004728.]: Samplename: 50 Root: 51.855 --> Root in between the borders! Added to results. Coefficients: -42.6858653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_004007.]: Samplename: 62.5 + [20250519_004728.]: Samplename: 62.5 Root: 65.265 --> Root in between the borders! Added to results. Coefficients: -52.9033653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_004007.]: Samplename: 75 + [20250519_004728.]: Samplename: 75 Root: 74.915 --> Root in between the borders! Added to results. Coefficients: -65.492531986532Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_004007.]: Samplename: 87.5 + [20250519_004728.]: Samplename: 87.5 Root: 84.67 --> Root in between the borders! Added to results. Coefficients: -92.9898653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_004007.]: Samplename: 100 + [20250519_004728.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.082 --> '100 < root < 110' --> substitute 100 - [20250519_004007.]: Solving cubic regression for row_means + [20250519_004728.]: Solving cubic regression for row_means Coefficients: -0.771215488215478Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_004007.]: Samplename: 0 + [20250519_004728.]: Samplename: 0 Root: 1.287 --> Root in between the borders! Added to results. Coefficients: -6.47247474747474Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_004007.]: Samplename: 12.5 + [20250519_004728.]: Samplename: 12.5 Root: 10.847 --> Root in between the borders! Added to results. Coefficients: -14.8972154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_004007.]: Samplename: 25 + [20250519_004728.]: Samplename: 25 Root: 24.737 --> Root in between the borders! Added to results. Coefficients: -22.1492154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_004007.]: Samplename: 37.5 + [20250519_004728.]: Samplename: 37.5 Root: 36.02 --> Root in between the borders! Added to results. Coefficients: -32.5259932659933Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_004007.]: Samplename: 50 + [20250519_004728.]: Samplename: 50 Root: 50.639 --> Root in between the borders! Added to results. Coefficients: -44.7218821548821Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_004007.]: Samplename: 62.5 + [20250519_004728.]: Samplename: 62.5 Root: 65.497 --> Root in between the borders! Added to results. Coefficients: -54.4032154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_004007.]: Samplename: 75 + [20250519_004728.]: Samplename: 75 Root: 75.751 --> Root in between the borders! Added to results. Coefficients: -62.4313636363636Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_004007.]: Samplename: 87.5 + [20250519_004728.]: Samplename: 87.5 Root: 83.403 --> Root in between the borders! Added to results. Coefficients: -84.7343265993266Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_004007.]: Samplename: 100 + [20250519_004728.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.573 --> '100 < root < 110' --> substitute 100 - [20250519_004007.]: + [20250519_004728.]: ### Starting with regression calculations ### - [20250519_004007.]: Entered 'regression_type1'-Function - [20250519_004007.]: # CpG-site: CpG#1 + [20250519_004728.]: Entered 'regression_type1'-Function + [20250519_004729.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) - [20250519_004007.]: Logging df_agg: CpG#1 - [20250519_004007.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004007.]: c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)[20250519_004007.]: c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)[20250519_004007.]: c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) - [20250519_004007.]: Entered 'hyperbolic_regression'-Function - [20250519_004007.]: 'hyperbolic_regression': minmax = FALSE - [20250519_004007.]: Entered 'cubic_regression'-Function - [20250519_004007.]: 'cubic_regression': minmax = FALSE - [20250519_004007.]: # CpG-site: CpG#2 + [20250519_004729.]: Logging df_agg: CpG#1 + [20250519_004729.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004729.]: c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)[20250519_004729.]: c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)[20250519_004729.]: c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) + [20250519_004729.]: Entered 'hyperbolic_regression'-Function + [20250519_004729.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004729.]: Entered 'cubic_regression'-Function + [20250519_004729.]: 'cubic_regression': minmax = FALSE + [20250519_004729.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) - [20250519_004007.]: Logging df_agg: CpG#2 - [20250519_004007.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004007.]: c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)[20250519_004007.]: c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)[20250519_004007.]: c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) - [20250519_004007.]: Entered 'hyperbolic_regression'-Function - [20250519_004007.]: 'hyperbolic_regression': minmax = FALSE - [20250519_004007.]: Entered 'cubic_regression'-Function - [20250519_004007.]: 'cubic_regression': minmax = FALSE - [20250519_004007.]: # CpG-site: CpG#3 + [20250519_004729.]: Logging df_agg: CpG#2 + [20250519_004729.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004729.]: c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)[20250519_004729.]: c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)[20250519_004729.]: c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) + [20250519_004729.]: Entered 'hyperbolic_regression'-Function + [20250519_004729.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004729.]: Entered 'cubic_regression'-Function + [20250519_004729.]: 'cubic_regression': minmax = FALSE + [20250519_004729.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) - [20250519_004007.]: Logging df_agg: CpG#3 - [20250519_004007.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004007.]: c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)[20250519_004007.]: c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)[20250519_004007.]: c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) - [20250519_004007.]: Entered 'hyperbolic_regression'-Function - [20250519_004007.]: 'hyperbolic_regression': minmax = FALSE - [20250519_004007.]: Entered 'cubic_regression'-Function - [20250519_004007.]: 'cubic_regression': minmax = FALSE - [20250519_004007.]: # CpG-site: CpG#4 + [20250519_004729.]: Logging df_agg: CpG#3 + [20250519_004729.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004729.]: c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)[20250519_004729.]: c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)[20250519_004729.]: c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) + [20250519_004729.]: Entered 'hyperbolic_regression'-Function + [20250519_004729.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004729.]: Entered 'cubic_regression'-Function + [20250519_004729.]: 'cubic_regression': minmax = FALSE + [20250519_004729.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) - [20250519_004007.]: Logging df_agg: CpG#4 - [20250519_004007.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004007.]: c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)[20250519_004007.]: c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)[20250519_004007.]: c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) - [20250519_004007.]: Entered 'hyperbolic_regression'-Function - [20250519_004007.]: 'hyperbolic_regression': minmax = FALSE - [20250519_004007.]: Entered 'cubic_regression'-Function - [20250519_004007.]: 'cubic_regression': minmax = FALSE - [20250519_004007.]: # CpG-site: CpG#5 + [20250519_004729.]: Logging df_agg: CpG#4 + [20250519_004729.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004729.]: c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)[20250519_004729.]: c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)[20250519_004729.]: c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) + [20250519_004729.]: Entered 'hyperbolic_regression'-Function + [20250519_004729.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004729.]: Entered 'cubic_regression'-Function + [20250519_004729.]: 'cubic_regression': minmax = FALSE + [20250519_004729.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) - [20250519_004007.]: Logging df_agg: CpG#5 - [20250519_004007.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004007.]: c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)[20250519_004007.]: c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)[20250519_004007.]: c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) - [20250519_004007.]: Entered 'hyperbolic_regression'-Function - [20250519_004007.]: 'hyperbolic_regression': minmax = FALSE - [20250519_004007.]: Entered 'cubic_regression'-Function - [20250519_004007.]: 'cubic_regression': minmax = FALSE - [20250519_004007.]: # CpG-site: CpG#6 + [20250519_004729.]: Logging df_agg: CpG#5 + [20250519_004729.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004729.]: c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)[20250519_004729.]: c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)[20250519_004729.]: c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) + [20250519_004729.]: Entered 'hyperbolic_regression'-Function + [20250519_004729.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004729.]: Entered 'cubic_regression'-Function + [20250519_004729.]: 'cubic_regression': minmax = FALSE + [20250519_004729.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) - [20250519_004007.]: Logging df_agg: CpG#6 - [20250519_004007.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004007.]: c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)[20250519_004007.]: c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)[20250519_004007.]: c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) - [20250519_004007.]: Entered 'hyperbolic_regression'-Function - [20250519_004007.]: 'hyperbolic_regression': minmax = FALSE - [20250519_004007.]: Entered 'cubic_regression'-Function - [20250519_004007.]: 'cubic_regression': minmax = FALSE - [20250519_004007.]: # CpG-site: CpG#7 + [20250519_004729.]: Logging df_agg: CpG#6 + [20250519_004729.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004729.]: c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)[20250519_004729.]: c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)[20250519_004729.]: c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) + [20250519_004729.]: Entered 'hyperbolic_regression'-Function + [20250519_004729.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004729.]: Entered 'cubic_regression'-Function + [20250519_004729.]: 'cubic_regression': minmax = FALSE + [20250519_004729.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) - [20250519_004007.]: Logging df_agg: CpG#7 - [20250519_004007.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004007.]: c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)[20250519_004007.]: c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)[20250519_004007.]: c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) - [20250519_004007.]: Entered 'hyperbolic_regression'-Function - [20250519_004007.]: 'hyperbolic_regression': minmax = FALSE - [20250519_004007.]: Entered 'cubic_regression'-Function - [20250519_004007.]: 'cubic_regression': minmax = FALSE - [20250519_004007.]: # CpG-site: CpG#8 + [20250519_004729.]: Logging df_agg: CpG#7 + [20250519_004729.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004729.]: c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)[20250519_004729.]: c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)[20250519_004729.]: c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) + [20250519_004729.]: Entered 'hyperbolic_regression'-Function + [20250519_004729.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004729.]: Entered 'cubic_regression'-Function + [20250519_004729.]: 'cubic_regression': minmax = FALSE + [20250519_004729.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) - [20250519_004007.]: Logging df_agg: CpG#8 - [20250519_004007.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004007.]: c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)[20250519_004007.]: c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)[20250519_004007.]: c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) - [20250519_004007.]: Entered 'hyperbolic_regression'-Function - [20250519_004007.]: 'hyperbolic_regression': minmax = FALSE - [20250519_004007.]: Entered 'cubic_regression'-Function - [20250519_004007.]: 'cubic_regression': minmax = FALSE - [20250519_004007.]: # CpG-site: CpG#9 + [20250519_004729.]: Logging df_agg: CpG#8 + [20250519_004729.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004729.]: c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)[20250519_004729.]: c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)[20250519_004729.]: c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) + [20250519_004729.]: Entered 'hyperbolic_regression'-Function + [20250519_004729.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004729.]: Entered 'cubic_regression'-Function + [20250519_004729.]: 'cubic_regression': minmax = FALSE + [20250519_004729.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) - [20250519_004007.]: Logging df_agg: CpG#9 - [20250519_004007.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004007.]: c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)[20250519_004007.]: c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)[20250519_004007.]: c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) - [20250519_004007.]: Entered 'hyperbolic_regression'-Function - [20250519_004007.]: 'hyperbolic_regression': minmax = FALSE - [20250519_004007.]: Entered 'cubic_regression'-Function - [20250519_004007.]: 'cubic_regression': minmax = FALSE - [20250519_004007.]: # CpG-site: row_means + [20250519_004729.]: Logging df_agg: CpG#9 + [20250519_004729.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004729.]: c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)[20250519_004729.]: c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)[20250519_004729.]: c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) + [20250519_004729.]: Entered 'hyperbolic_regression'-Function + [20250519_004729.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004729.]: Entered 'cubic_regression'-Function + [20250519_004729.]: 'cubic_regression': minmax = FALSE + [20250519_004729.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) - [20250519_004007.]: Logging df_agg: row_means - [20250519_004007.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004007.]: c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)[20250519_004007.]: c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)[20250519_004007.]: c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) - [20250519_004008.]: Entered 'hyperbolic_regression'-Function - [20250519_004008.]: 'hyperbolic_regression': minmax = FALSE - [20250519_004008.]: Entered 'cubic_regression'-Function - [20250519_004008.]: 'cubic_regression': minmax = FALSE - [20250519_004008.]: ### Starting with plotting ### - [20250519_004008.]: Creating BiasCorrected (cubic) plot No. 1 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG1_corrected_c.png - [20250519_004008.]: # CpG-site: CpG#1 + [20250519_004729.]: Logging df_agg: row_means + [20250519_004729.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004729.]: c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)[20250519_004729.]: c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)[20250519_004729.]: c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) + [20250519_004729.]: Entered 'hyperbolic_regression'-Function + [20250519_004729.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004729.]: Entered 'cubic_regression'-Function + [20250519_004729.]: 'cubic_regression': minmax = FALSE + [20250519_004729.]: ### Starting with plotting ### + [20250519_004730.]: Creating BiasCorrected (cubic) plot No. 1 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG1_corrected_c.png + [20250519_004730.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -1164.7742156272 , a = 2039.5018241568 , b = -1164.7742156272 , d = 1955.83895682798 - [20250519_004008.]: # CpG-site: CpG#1 + [20250519_004730.]: # CpG-site: CpG#1 Cubic: Using a = -3.42821196240366e-05 , b = 0.00462173155013798 , c = 0.850326258865826 , d = 0.500073874211125 - [20250519_004010.]: Creating BiasCorrected (cubic) plot No. 2 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG2_corrected_c.png - [20250519_004010.]: # CpG-site: CpG#2 + [20250519_004731.]: Creating BiasCorrected (cubic) plot No. 2 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG2_corrected_c.png + [20250519_004731.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = -464.155645361511 , a = 4988.81163391504 , b = -464.155645361511 , d = 4925.28408403839 - [20250519_004010.]: # CpG-site: CpG#2 + [20250519_004731.]: # CpG-site: CpG#2 Cubic: Using a = -1.40811796906314e-05 , b = 0.00190849431754485 , c = 0.933455870941473 , d = 0.363096210099674 - [20250519_004011.]: Creating BiasCorrected (cubic) plot No. 3 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG3_corrected_c.png - [20250519_004011.]: # CpG-site: CpG#3 + [20250519_004733.]: Creating BiasCorrected (cubic) plot No. 3 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG3_corrected_c.png + [20250519_004733.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = -745.602352161431 , a = 2026.54103071808 , b = -745.602352161431 , d = 1953.2909386646 - [20250519_004011.]: # CpG-site: CpG#3 + [20250519_004733.]: # CpG-site: CpG#3 Cubic: Using a = -2.78835051857186e-05 , b = 0.00366863707889089 , c = 0.880665171220893 , d = 0.510779308833653 - [20250519_004013.]: Creating BiasCorrected (cubic) plot No. 4 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG4_corrected_c.png - [20250519_004013.]: # CpG-site: CpG#4 + [20250519_004734.]: Creating BiasCorrected (cubic) plot No. 4 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG4_corrected_c.png + [20250519_004734.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = -833.06710731958 , a = 2871.12609903594 , b = -833.06710731958 , d = 2796.80085807845 - [20250519_004013.]: # CpG-site: CpG#4 + [20250519_004734.]: # CpG-site: CpG#4 Cubic: Using a = -2.30028865643e-05 , b = 0.00309017851940692 , c = 0.897073107777645 , d = 0.443455761841866 - [20250519_004014.]: Creating BiasCorrected (cubic) plot No. 5 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG5_corrected_c.png - [20250519_004014.]: # CpG-site: CpG#5 + [20250519_004735.]: Creating BiasCorrected (cubic) plot No. 5 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG5_corrected_c.png + [20250519_004735.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = 221.879400146024 , a = 12476.5710927987 , b = 221.879400146024 , d = 12431.2329555778 - [20250519_004014.]: # CpG-site: CpG#5 + [20250519_004736.]: # CpG-site: CpG#5 Cubic: Using a = -1.28632696989134e-05 , b = 0.00184822285299014 , c = 0.931001046631176 , d = 0.437963095185599 - [20250519_004015.]: Creating BiasCorrected (cubic) plot No. 6 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG6_corrected_c.png - [20250519_004015.]: # CpG-site: CpG#6 + [20250519_004737.]: Creating BiasCorrected (cubic) plot No. 6 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG6_corrected_c.png + [20250519_004737.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = -700.537394936109 , a = 21298.052707184 , b = -700.537394936109 , d = 21233.4563350159 - [20250519_004015.]: # CpG-site: CpG#6 + [20250519_004737.]: # CpG-site: CpG#6 Cubic: Using a = -5.838335151375e-06 , b = 0.000828447163218526 , c = 0.970047764475166 , d = 0.158074701412251 - [20250519_004017.]: Creating BiasCorrected (cubic) plot No. 7 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG7_corrected_c.png - [20250519_004017.]: # CpG-site: CpG#7 + [20250519_004738.]: Creating BiasCorrected (cubic) plot No. 7 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG7_corrected_c.png + [20250519_004738.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -1144.91075475684 , a = 1477.92088633595 , b = -1144.91075475684 , d = 1394.70940380214 - [20250519_004017.]: # CpG-site: CpG#7 + [20250519_004738.]: # CpG-site: CpG#7 Cubic: Using a = -4.54220535614047e-05 , b = 0.00607993130565131 , c = 0.805411493452389 , d = 0.615125382790254 - [20250519_004018.]: Creating BiasCorrected (cubic) plot No. 8 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG8_corrected_c.png - [20250519_004018.]: # CpG-site: CpG#8 + [20250519_004740.]: Creating BiasCorrected (cubic) plot No. 8 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG8_corrected_c.png + [20250519_004740.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = -349.322022819744 , a = 2377.42555416743 , b = -349.322022819744 , d = 2315.25402084122 - [20250519_004018.]: # CpG-site: CpG#8 + [20250519_004740.]: # CpG-site: CpG#8 Cubic: Using a = -2.64622306669639e-05 , b = 0.00353621593528015 , c = 0.877916844709074 , d = 0.698684606493051 - [20250519_004020.]: Creating BiasCorrected (cubic) plot No. 9 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_CpG9_corrected_c.png - [20250519_004020.]: # CpG-site: CpG#9 + [20250519_004741.]: Creating BiasCorrected (cubic) plot No. 9 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_CpG9_corrected_c.png + [20250519_004741.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -1536.91807795025 , a = 3745.12601816225 , b = -1536.91807795025 , d = 3656.21766932252 - [20250519_004020.]: # CpG-site: CpG#9 + [20250519_004741.]: # CpG-site: CpG#9 Cubic: Using a = -2.70976130611725e-05 , b = 0.00378478247182342 , c = 0.871825263051793 , d = 0.455475740974113 - [20250519_004021.]: Creating BiasCorrected (cubic) plot No. 10 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_rowmeans_corrected_c.png - [20250519_004021.]: # CpG-site: row_means + [20250519_004743.]: Creating BiasCorrected (cubic) plot No. 10 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_rowmeans_corrected_c.png + [20250519_004743.]: # CpG-site: row_means Hyperbolic: Using bias_weight = -820.655299479511 , a = 3198.47118291101 , b = -820.655299479511 , d = 3124.21569118411 - [20250519_004021.]: # CpG-site: row_means + [20250519_004743.]: # CpG-site: row_means Cubic: Using a = -2.34328362100009e-05 , b = 0.00319139073176266 , c = 0.891842168032129 , d = 0.493517689787135 - [20250519_004023.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG1_corrected_c.png - [20250519_004024.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG2_corrected_c.png - [20250519_004025.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG3_corrected_c.png - [20250519_004027.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG4_corrected_c.png - [20250519_004028.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG5_corrected_c.png - [20250519_004029.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG6_corrected_c.png - [20250519_004031.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG7_corrected_c.png - [20250519_004032.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG8_corrected_c.png - [20250519_004033.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_CpG9_corrected_c.png - [20250519_004035.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Testlocus_error_rowmeans_corrected_c.png - [20250519_004036.]: Entered 'solving_equations'-Function - [20250519_004036.]: Solving hyperbolic regression for CpG#1 + [20250519_004744.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG1_corrected_c.png + [20250519_004746.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG2_corrected_c.png + [20250519_004747.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG3_corrected_c.png + [20250519_004748.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG4_corrected_c.png + [20250519_004749.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG5_corrected_c.png + [20250519_004751.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG6_corrected_c.png + [20250519_004752.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG7_corrected_c.png + [20250519_004753.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG8_corrected_c.png + [20250519_004755.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_CpG9_corrected_c.png + [20250519_004756.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Testlocus_error_rowmeans_corrected_c.png + [20250519_004757.]: Entered 'solving_equations'-Function + [20250519_004757.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 77.9602582684049 - [20250519_004036.]: Samplename: Sample#1 + [20250519_004757.]: Samplename: Sample#1 Root: 77.96 --> Root in between the borders! Added to results. Hyperbolic solved: 27.1420961794087 - [20250519_004036.]: Samplename: Sample#10 + [20250519_004757.]: Samplename: Sample#10 Root: 27.142 --> Root in between the borders! Added to results. Hyperbolic solved: 36.7640366116659 - [20250519_004036.]: Samplename: Sample#2 + [20250519_004757.]: Samplename: Sample#2 Root: 36.764 --> Root in between the borders! Added to results. Hyperbolic solved: 51.6231208412737 - [20250519_004036.]: Samplename: Sample#3 + [20250519_004757.]: Samplename: Sample#3 Root: 51.623 --> Root in between the borders! Added to results. Hyperbolic solved: 12.873561614208 - [20250519_004036.]: Samplename: Sample#4 + [20250519_004757.]: Samplename: Sample#4 Root: 12.874 --> Root in between the borders! Added to results. Hyperbolic solved: 21.2773642339947 - [20250519_004036.]: Samplename: Sample#5 + [20250519_004757.]: Samplename: Sample#5 Root: 21.277 --> Root in between the borders! Added to results. Hyperbolic solved: 22.1858835788516 - [20250519_004036.]: Samplename: Sample#6 + [20250519_004757.]: Samplename: Sample#6 Root: 22.186 --> Root in between the borders! Added to results. Hyperbolic solved: 40.1506639226714 - [20250519_004036.]: Samplename: Sample#7 + [20250519_004757.]: Samplename: Sample#7 Root: 40.151 --> Root in between the borders! Added to results. Hyperbolic solved: 86.1385429083643 - [20250519_004036.]: Samplename: Sample#8 + [20250519_004757.]: Samplename: Sample#8 Root: 86.139 --> Root in between the borders! Added to results. Hyperbolic solved: 5.22287566816154 - [20250519_004036.]: Samplename: Sample#9 + [20250519_004757.]: Samplename: Sample#9 Root: 5.223 --> Root in between the borders! Added to results. - [20250519_004036.]: Solving hyperbolic regression for CpG#2 + [20250519_004757.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 74.4935883316308 - [20250519_004036.]: Samplename: Sample#1 + [20250519_004757.]: Samplename: Sample#1 Root: 74.494 --> Root in between the borders! Added to results. Hyperbolic solved: 28.4239304939459 - [20250519_004036.]: Samplename: Sample#10 + [20250519_004757.]: Samplename: Sample#10 Root: 28.424 --> Root in between the borders! Added to results. Hyperbolic solved: 38.422868577211 - [20250519_004036.]: Samplename: Sample#2 + [20250519_004757.]: Samplename: Sample#2 Root: 38.423 --> Root in between the borders! Added to results. Hyperbolic solved: 54.1812729208768 - [20250519_004036.]: Samplename: Sample#3 + [20250519_004757.]: Samplename: Sample#3 Root: 54.181 --> Root in between the borders! Added to results. Hyperbolic solved: 9.35760412501846 - [20250519_004036.]: Samplename: Sample#4 + [20250519_004757.]: Samplename: Sample#4 Root: 9.358 --> Root in between the borders! Added to results. Hyperbolic solved: 20.0638687855045 - [20250519_004036.]: Samplename: Sample#5 + [20250519_004757.]: Samplename: Sample#5 Root: 20.064 --> Root in between the borders! Added to results. Hyperbolic solved: 18.2691112345675 - [20250519_004036.]: Samplename: Sample#6 + [20250519_004757.]: Samplename: Sample#6 Root: 18.269 --> Root in between the borders! Added to results. Hyperbolic solved: 36.8485384997942 - [20250519_004036.]: Samplename: Sample#7 + [20250519_004757.]: Samplename: Sample#7 Root: 36.849 --> Root in between the borders! Added to results. Hyperbolic solved: 86.7213727244426 - [20250519_004036.]: Samplename: Sample#8 + [20250519_004757.]: Samplename: Sample#8 Root: 86.721 --> Root in between the borders! Added to results. Hyperbolic solved: 8.13467837636389 - [20250519_004036.]: Samplename: Sample#9 + [20250519_004757.]: Samplename: Sample#9 Root: 8.135 --> Root in between the borders! Added to results. - [20250519_004036.]: Solving hyperbolic regression for CpG#3 + [20250519_004757.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 72.9754823399013 - [20250519_004036.]: Samplename: Sample#1 + [20250519_004757.]: Samplename: Sample#1 Root: 72.975 --> Root in between the borders! Added to results. Hyperbolic solved: 26.09308106721 - [20250519_004036.]: Samplename: Sample#10 + [20250519_004757.]: Samplename: Sample#10 Root: 26.093 --> Root in between the borders! Added to results. Hyperbolic solved: 38.5204516929959 - [20250519_004036.]: Samplename: Sample#2 + [20250519_004757.]: Samplename: Sample#2 Root: 38.52 --> Root in between the borders! Added to results. Hyperbolic solved: 52.0558039343263 - [20250519_004036.]: Samplename: Sample#3 + [20250519_004757.]: Samplename: Sample#3 Root: 52.056 --> Root in between the borders! Added to results. Hyperbolic solved: 10.6836235174275 - [20250519_004036.]: Samplename: Sample#4 + [20250519_004757.]: Samplename: Sample#4 Root: 10.684 --> Root in between the borders! Added to results. Hyperbolic solved: 23.1384875877206 - [20250519_004036.]: Samplename: Sample#5 + [20250519_004757.]: Samplename: Sample#5 Root: 23.138 --> Root in between the borders! Added to results. Hyperbolic solved: 24.8934322445107 - [20250519_004036.]: Samplename: Sample#6 + [20250519_004757.]: Samplename: Sample#6 Root: 24.893 --> Root in between the borders! Added to results. Hyperbolic solved: 40.6015033285401 - [20250519_004036.]: Samplename: Sample#7 + [20250519_004757.]: Samplename: Sample#7 Root: 40.602 --> Root in between the borders! Added to results. Hyperbolic solved: 86.3873125270529 - [20250519_004036.]: Samplename: Sample#8 + [20250519_004757.]: Samplename: Sample#8 Root: 86.387 --> Root in between the borders! Added to results. Hyperbolic solved: 4.44964604860886 - [20250519_004036.]: Samplename: Sample#9 + [20250519_004757.]: Samplename: Sample#9 Root: 4.45 --> Root in between the borders! Added to results. - [20250519_004036.]: Solving hyperbolic regression for CpG#4 + [20250519_004757.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 74.4016735507272 - [20250519_004036.]: Samplename: Sample#1 + [20250519_004757.]: Samplename: Sample#1 Root: 74.402 --> Root in between the borders! Added to results. Hyperbolic solved: 26.4295897789912 - [20250519_004036.]: Samplename: Sample#10 + [20250519_004757.]: Samplename: Sample#10 Root: 26.43 --> Root in between the borders! Added to results. Hyperbolic solved: 39.6008425259518 - [20250519_004036.]: Samplename: Sample#2 + [20250519_004757.]: Samplename: Sample#2 Root: 39.601 --> Root in between the borders! Added to results. Hyperbolic solved: 54.4093205134833 - [20250519_004036.]: Samplename: Sample#3 + [20250519_004757.]: Samplename: Sample#3 Root: 54.409 --> Root in between the borders! Added to results. Hyperbolic solved: 12.6125973016001 - [20250519_004036.]: Samplename: Sample#4 + [20250519_004757.]: Samplename: Sample#4 Root: 12.613 --> Root in between the borders! Added to results. Hyperbolic solved: 21.5537046072918 - [20250519_004036.]: Samplename: Sample#5 + [20250519_004757.]: Samplename: Sample#5 Root: 21.554 --> Root in between the borders! Added to results. Hyperbolic solved: 25.5116357756709 - [20250519_004036.]: Samplename: Sample#6 + [20250519_004757.]: Samplename: Sample#6 Root: 25.512 --> Root in between the borders! Added to results. Hyperbolic solved: 38.1579452647512 - [20250519_004036.]: Samplename: Sample#7 + [20250519_004757.]: Samplename: Sample#7 Root: 38.158 --> Root in between the borders! Added to results. Hyperbolic solved: 86.2182702652395 - [20250519_004036.]: Samplename: Sample#8 + [20250519_004757.]: Samplename: Sample#8 Root: 86.218 --> Root in between the borders! Added to results. Hyperbolic solved: 7.06911234650237 - [20250519_004036.]: Samplename: Sample#9 + [20250519_004757.]: Samplename: Sample#9 Root: 7.069 --> Root in between the borders! Added to results. - [20250519_004036.]: Solving hyperbolic regression for CpG#5 + [20250519_004757.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 70.2481376198381 - [20250519_004036.]: Samplename: Sample#1 + [20250519_004757.]: Samplename: Sample#1 Root: 70.248 --> Root in between the borders! Added to results. Hyperbolic solved: 24.9396715205194 - [20250519_004036.]: Samplename: Sample#10 + [20250519_004757.]: Samplename: Sample#10 Root: 24.94 --> Root in between the borders! Added to results. Hyperbolic solved: 40.4614457485807 - [20250519_004036.]: Samplename: Sample#2 + [20250519_004757.]: Samplename: Sample#2 Root: 40.461 --> Root in between the borders! Added to results. Hyperbolic solved: 49.3815751365795 - [20250519_004036.]: Samplename: Sample#3 + [20250519_004757.]: Samplename: Sample#3 Root: 49.382 --> Root in between the borders! Added to results. Hyperbolic solved: 12.353607679335 - [20250519_004036.]: Samplename: Sample#4 + [20250519_004757.]: Samplename: Sample#4 Root: 12.354 --> Root in between the borders! Added to results. Hyperbolic solved: 22.0901459497739 - [20250519_004036.]: Samplename: Sample#5 + [20250519_004757.]: Samplename: Sample#5 Root: 22.09 --> Root in between the borders! Added to results. Hyperbolic solved: 24.5507390437226 - [20250519_004036.]: Samplename: Sample#6 + [20250519_004757.]: Samplename: Sample#6 Root: 24.551 --> Root in between the borders! Added to results. Hyperbolic solved: 39.0947007538418 - [20250519_004036.]: Samplename: Sample#7 + [20250519_004757.]: Samplename: Sample#7 Root: 39.095 --> Root in between the borders! Added to results. Hyperbolic solved: 90.4841321892294 - [20250519_004036.]: Samplename: Sample#8 + [20250519_004757.]: Samplename: Sample#8 Root: 90.484 --> Root in between the borders! Added to results. Hyperbolic solved: 6.91649336851909 - [20250519_004036.]: Samplename: Sample#9 + [20250519_004757.]: Samplename: Sample#9 Root: 6.916 --> Root in between the borders! Added to results. - [20250519_004036.]: Solving hyperbolic regression for CpG#6 + [20250519_004757.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 77.4558189028663 - [20250519_004036.]: Samplename: Sample#1 + [20250519_004757.]: Samplename: Sample#1 Root: 77.456 --> Root in between the borders! Added to results. Hyperbolic solved: 27.5880652239037 - [20250519_004036.]: Samplename: Sample#10 + [20250519_004757.]: Samplename: Sample#10 Root: 27.588 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2657605119474 - [20250519_004036.]: Samplename: Sample#2 + [20250519_004757.]: Samplename: Sample#2 Root: 37.266 --> Root in between the borders! Added to results. Hyperbolic solved: 51.469303759821 - [20250519_004036.]: Samplename: Sample#3 + [20250519_004757.]: Samplename: Sample#3 Root: 51.469 --> Root in between the borders! Added to results. Hyperbolic solved: 12.3197919642261 - [20250519_004036.]: Samplename: Sample#4 + [20250519_004757.]: Samplename: Sample#4 Root: 12.32 --> Root in between the borders! Added to results. Hyperbolic solved: 18.9916700132306 - [20250519_004036.]: Samplename: Sample#5 + [20250519_004757.]: Samplename: Sample#5 Root: 18.992 --> Root in between the borders! Added to results. Hyperbolic solved: 27.3757521451495 - [20250519_004036.]: Samplename: Sample#6 + [20250519_004757.]: Samplename: Sample#6 Root: 27.376 --> Root in between the borders! Added to results. Hyperbolic solved: 38.1192529388954 - [20250519_004036.]: Samplename: Sample#7 + [20250519_004757.]: Samplename: Sample#7 Root: 38.119 --> Root in between the borders! Added to results. Hyperbolic solved: 87.7229687796537 - [20250519_004036.]: Samplename: Sample#8 + [20250519_004757.]: Samplename: Sample#8 Root: 87.723 --> Root in between the borders! Added to results. Hyperbolic solved: 7.66260533640959 - [20250519_004036.]: Samplename: Sample#9 + [20250519_004757.]: Samplename: Sample#9 Root: 7.663 --> Root in between the borders! Added to results. - [20250519_004036.]: Solving hyperbolic regression for CpG#7 + [20250519_004757.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 75.6858966925732 - [20250519_004036.]: Samplename: Sample#1 + [20250519_004757.]: Samplename: Sample#1 Root: 75.686 --> Root in between the borders! Added to results. Hyperbolic solved: 23.9111476853231 - [20250519_004036.]: Samplename: Sample#10 + [20250519_004757.]: Samplename: Sample#10 Root: 23.911 --> Root in between the borders! Added to results. Hyperbolic solved: 41.6834100561109 - [20250519_004036.]: Samplename: Sample#2 + [20250519_004757.]: Samplename: Sample#2 Root: 41.683 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3038531152796 - [20250519_004036.]: Samplename: Sample#3 + [20250519_004757.]: Samplename: Sample#3 Root: 52.304 --> Root in between the borders! Added to results. Hyperbolic solved: 14.584082616843 - [20250519_004036.]: Samplename: Sample#4 + [20250519_004757.]: Samplename: Sample#4 Root: 14.584 --> Root in between the borders! Added to results. Hyperbolic solved: 23.8322996387272 - [20250519_004036.]: Samplename: Sample#5 + [20250519_004757.]: Samplename: Sample#5 Root: 23.832 --> Root in between the borders! Added to results. Hyperbolic solved: 26.8055282397054 - [20250519_004036.]: Samplename: Sample#6 + [20250519_004757.]: Samplename: Sample#6 Root: 26.806 --> Root in between the borders! Added to results. Hyperbolic solved: 39.1748201958722 - [20250519_004036.]: Samplename: Sample#7 + [20250519_004757.]: Samplename: Sample#7 Root: 39.175 --> Root in between the borders! Added to results. Hyperbolic solved: 86.4968654540458 - [20250519_004036.]: Samplename: Sample#8 + [20250519_004757.]: Samplename: Sample#8 Root: 86.497 --> Root in between the borders! Added to results. Hyperbolic solved: 5.20445580813888 - [20250519_004036.]: Samplename: Sample#9 + [20250519_004757.]: Samplename: Sample#9 Root: 5.204 --> Root in between the borders! Added to results. - [20250519_004036.]: Solving hyperbolic regression for CpG#8 + [20250519_004757.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 71.4358559327679 - [20250519_004036.]: Samplename: Sample#1 + [20250519_004757.]: Samplename: Sample#1 Root: 71.436 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5978231834828 - [20250519_004036.]: Samplename: Sample#10 + [20250519_004757.]: Samplename: Sample#10 Root: 26.598 --> Root in between the borders! Added to results. Hyperbolic solved: 32.9687797883659 - [20250519_004036.]: Samplename: Sample#2 + [20250519_004757.]: Samplename: Sample#2 Root: 32.969 --> Root in between the borders! Added to results. Hyperbolic solved: 56.5920244960613 - [20250519_004036.]: Samplename: Sample#3 + [20250519_004757.]: Samplename: Sample#3 Root: 56.592 --> Root in between the borders! Added to results. Hyperbolic solved: 11.2061554830111 - [20250519_004036.]: Samplename: Sample#4 + [20250519_004757.]: Samplename: Sample#4 Root: 11.206 --> Root in between the borders! Added to results. Hyperbolic solved: 19.2580019302788 - [20250519_004036.]: Samplename: Sample#5 + [20250519_004757.]: Samplename: Sample#5 Root: 19.258 --> Root in between the borders! Added to results. Hyperbolic solved: 36.3407423784248 - [20250519_004036.]: Samplename: Sample#6 + [20250519_004757.]: Samplename: Sample#6 Root: 36.341 --> Root in between the borders! Added to results. Hyperbolic solved: 42.2292176199449 - [20250519_004036.]: Samplename: Sample#7 + [20250519_004757.]: Samplename: Sample#7 Root: 42.229 --> Root in between the borders! Added to results. Hyperbolic solved: 85.1479651236815 - [20250519_004036.]: Samplename: Sample#8 + [20250519_004757.]: Samplename: Sample#8 Root: 85.148 --> Root in between the borders! Added to results. Hyperbolic solved: 1.97353330914679 - [20250519_004036.]: Samplename: Sample#9 + [20250519_004757.]: Samplename: Sample#9 Root: 1.974 --> Root in between the borders! Added to results. - [20250519_004036.]: Solving hyperbolic regression for CpG#9 + [20250519_004757.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 78.362574368733 - [20250519_004036.]: Samplename: Sample#1 + [20250519_004757.]: Samplename: Sample#1 Root: 78.363 --> Root in between the borders! Added to results. Hyperbolic solved: 24.4725957151698 - [20250519_004036.]: Samplename: Sample#10 + [20250519_004757.]: Samplename: Sample#10 Root: 24.473 --> Root in between the borders! Added to results. Hyperbolic solved: 38.5471090507298 - [20250519_004036.]: Samplename: Sample#2 + [20250519_004757.]: Samplename: Sample#2 Root: 38.547 --> Root in between the borders! Added to results. Hyperbolic solved: 48.850764118569 - [20250519_004036.]: Samplename: Sample#3 + [20250519_004757.]: Samplename: Sample#3 Root: 48.851 --> Root in between the borders! Added to results. Hyperbolic solved: 13.0341042345297 - [20250519_004036.]: Samplename: Sample#4 + [20250519_004757.]: Samplename: Sample#4 Root: 13.034 --> Root in between the borders! Added to results. Hyperbolic solved: 22.1156929084775 - [20250519_004036.]: Samplename: Sample#5 + [20250519_004757.]: Samplename: Sample#5 Root: 22.116 --> Root in between the borders! Added to results. Hyperbolic solved: 33.0730206710577 - [20250519_004036.]: Samplename: Sample#6 + [20250519_004757.]: Samplename: Sample#6 Root: 33.073 --> Root in between the borders! Added to results. Hyperbolic solved: 36.8757560868803 - [20250519_004036.]: Samplename: Sample#7 + [20250519_004757.]: Samplename: Sample#7 Root: 36.876 --> Root in between the borders! Added to results. Hyperbolic solved: 89.6667208986789 - [20250519_004036.]: Samplename: Sample#8 + [20250519_004757.]: Samplename: Sample#8 Root: 89.667 --> Root in between the borders! Added to results. Hyperbolic solved: 8.10593089892949 - [20250519_004036.]: Samplename: Sample#9 + [20250519_004757.]: Samplename: Sample#9 Root: 8.106 --> Root in between the borders! Added to results. - [20250519_004036.]: Solving hyperbolic regression for row_means + [20250519_004757.]: Solving hyperbolic regression for row_means Hyperbolic solved: 74.8404708557895 - [20250519_004036.]: Samplename: Sample#1 + [20250519_004757.]: Samplename: Sample#1 Root: 74.84 --> Root in between the borders! Added to results. Hyperbolic solved: 26.2806125509682 - [20250519_004036.]: Samplename: Sample#10 + [20250519_004757.]: Samplename: Sample#10 Root: 26.281 --> Root in between the borders! Added to results. Hyperbolic solved: 38.1683813928189 - [20250519_004036.]: Samplename: Sample#2 + [20250519_004757.]: Samplename: Sample#2 Root: 38.168 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3554454867083 - [20250519_004036.]: Samplename: Sample#3 + [20250519_004757.]: Samplename: Sample#3 Root: 52.355 --> Root in between the borders! Added to results. Hyperbolic solved: 12.0337443526331 - [20250519_004036.]: Samplename: Sample#4 + [20250519_004757.]: Samplename: Sample#4 Root: 12.034 --> Root in between the borders! Added to results. Hyperbolic solved: 21.2452112654646 - [20250519_004036.]: Samplename: Sample#5 + [20250519_004757.]: Samplename: Sample#5 Root: 21.245 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5661392150144 - [20250519_004036.]: Samplename: Sample#6 + [20250519_004757.]: Samplename: Sample#6 Root: 26.566 --> Root in between the borders! Added to results. Hyperbolic solved: 38.9839362125804 - [20250519_004036.]: Samplename: Sample#7 + [20250519_004757.]: Samplename: Sample#7 Root: 38.984 --> Root in between the borders! Added to results. Hyperbolic solved: 87.2201089452416 - [20250519_004036.]: Samplename: Sample#8 + [20250519_004757.]: Samplename: Sample#8 Root: 87.22 --> Root in between the borders! Added to results. Hyperbolic solved: 6.14149092380673 - [20250519_004036.]: Samplename: Sample#9 + [20250519_004757.]: Samplename: Sample#9 Root: 6.141 --> Root in between the borders! Added to results. - [20250519_004036.]: Entered 'clean_dt'-Function - [20250519_004036.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_004036.]: got experimental data - [20250519_004036.]: Entered 'clean_dt'-Function - [20250519_004036.]: Importing data of type 2: Many loci in one sample (e.g., next-gen seq or microarray data) - [20250519_004036.]: got experimental data - [20250519_004037.]: Entered 'clean_dt'-Function - [20250519_004037.]: Entered 'clean_dt'-Function - [20250519_004037.]: Entered 'clean_dt'-Function - [20250519_004037.]: Entered 'clean_dt'-Function - [20250519_004037.]: Entered 'clean_dt'-Function - [20250519_004037.]: Entered 'clean_dt'-Function - [20250519_004037.]: Entered 'clean_dt'-Function - [20250519_004037.]: Entered 'clean_dt'-Function - [20250519_004037.]: Entered 'clean_dt'-Function - [20250519_004037.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_004037.]: got calibration data + [20250519_004758.]: Entered 'clean_dt'-Function + [20250519_004758.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_004758.]: got experimental data + [20250519_004758.]: Entered 'clean_dt'-Function + [20250519_004758.]: Importing data of type 2: Many loci in one sample (e.g., next-gen seq or microarray data) + [20250519_004758.]: got experimental data + [20250519_004758.]: Entered 'clean_dt'-Function + [20250519_004758.]: Entered 'clean_dt'-Function + [20250519_004758.]: Entered 'clean_dt'-Function + [20250519_004758.]: Entered 'clean_dt'-Function + [20250519_004758.]: Entered 'clean_dt'-Function + [20250519_004758.]: Entered 'clean_dt'-Function + [20250519_004758.]: Entered 'clean_dt'-Function + [20250519_004758.]: Entered 'clean_dt'-Function + [20250519_004758.]: Entered 'clean_dt'-Function + [20250519_004758.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_004758.]: got calibration data ### ERROR 54: first column cannot be parsed to numeric ### - [20250519_004037.]: Entered 'clean_dt'-Function - [20250519_004037.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_004037.]: got calibration data + [20250519_004758.]: Entered 'clean_dt'-Function + [20250519_004758.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_004758.]: got calibration data ### ERROR 54: first column cannot be parsed to numeric ### - [20250519_004037.]: Entered 'clean_dt'-Function - [20250519_004037.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_004037.]: got calibration data - [20250519_004037.]: ### ERROR ### + [20250519_004758.]: Entered 'clean_dt'-Function + [20250519_004758.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_004758.]: got calibration data + [20250519_004758.]: ### ERROR ### The data provided contains less than four calibration steps. At least four distinct calibration steps are required to perform bias correction. - [20250519_004037.]: Entered 'clean_dt'-Function - [20250519_004037.]: Importing data of type 2: Many loci in one sample (e.g., next-gen seq or microarray data) - [20250519_004037.]: got calibration data - [20250519_004037.]: ### ERROR ### + [20250519_004758.]: Entered 'clean_dt'-Function + [20250519_004758.]: Importing data of type 2: Many loci in one sample (e.g., next-gen seq or microarray data) + [20250519_004758.]: got calibration data + [20250519_004758.]: ### ERROR ### The data provided contains locus ids with heterogeneous counts of CpG-sites. - [20250519_004037.]: Entered 'clean_dt'-Function - [20250519_004037.]: Importing data of type 2: Many loci in one sample (e.g., next-gen seq or microarray data) - [20250519_004037.]: got experimental data - [20250519_004037.]: ### ERROR ### + [20250519_004758.]: Entered 'clean_dt'-Function + [20250519_004758.]: Importing data of type 2: Many loci in one sample (e.g., next-gen seq or microarray data) + [20250519_004758.]: got experimental data + [20250519_004758.]: ### ERROR ### The data provided contains locus ids with heterogeneous counts of CpG-sites. - [20250519_004037.]: Entered 'clean_dt'-Function - [20250519_004037.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_004037.]: got calibration data - [20250519_004037.]: Entered 'clean_dt'-Function - [20250519_004037.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_004037.]: got calibration data - [20250519_004037.]: Entered 'cubic_regression'-Function - [20250519_004037.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_004037.]: Entered 'cubic_regression'-Function - [20250519_004037.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_004037.]: Entered 'clean_dt'-Function - [20250519_004037.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_004037.]: got calibration data - [20250519_004037.]: Entered 'hyperbolic_regression'-Function - [20250519_004037.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_004037.]: Entered 'hyperbolic_regression'-Function - [20250519_004037.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_004038.]: on_start: using future::plan("sequential") - [20250519_004038.]: Entered 'clean_dt'-Function - [20250519_004038.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_004038.]: got experimental data - [20250519_004038.]: Entered 'clean_dt'-Function - [20250519_004038.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_004038.]: got calibration data - [20250519_004038.]: + [20250519_004758.]: Entered 'clean_dt'-Function + [20250519_004758.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_004758.]: got calibration data + [20250519_004758.]: Entered 'clean_dt'-Function + [20250519_004758.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_004758.]: got calibration data + [20250519_004758.]: Entered 'cubic_regression'-Function + [20250519_004758.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004758.]: Entered 'cubic_regression'-Function + [20250519_004758.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004759.]: Entered 'clean_dt'-Function + [20250519_004759.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_004759.]: got calibration data + [20250519_004759.]: Entered 'hyperbolic_regression'-Function + [20250519_004759.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004759.]: Entered 'hyperbolic_regression'-Function + [20250519_004759.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_004759.]: on_start: using future::plan("sequential") + [20250519_004759.]: Entered 'clean_dt'-Function + [20250519_004759.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_004759.]: got experimental data + [20250519_004759.]: Entered 'clean_dt'-Function + [20250519_004759.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_004759.]: got calibration data + [20250519_004759.]: ### Starting with regression calculations ### - [20250519_004038.]: Entered 'regression_type1'-Function - [20250519_004038.]: # CpG-site: CpG#1 + [20250519_004759.]: Entered 'regression_type1'-Function + [20250519_004759.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_004038.]: Logging df_agg: CpG#1 - [20250519_004038.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004038.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_004038.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_004038.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_004038.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_004038.]: Entered 'hyperbolic_regression'-Function - [20250519_004038.]: 'hyperbolic_regression': minmax = FALSE - [20250519_004038.]: Entered 'cubic_regression'-Function - [20250519_004038.]: 'cubic_regression': minmax = FALSE - [20250519_004038.]: # CpG-site: CpG#2 + [20250519_004759.]: Logging df_agg: CpG#1 + [20250519_004759.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004759.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_004759.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_004759.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_004759.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) + [20250519_004759.]: Entered 'hyperbolic_regression'-Function + [20250519_004759.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004759.]: Entered 'cubic_regression'-Function + [20250519_004759.]: 'cubic_regression': minmax = FALSE + [20250519_004759.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_004038.]: Logging df_agg: CpG#2 - [20250519_004038.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004038.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_004038.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_004038.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_004038.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_004038.]: Entered 'hyperbolic_regression'-Function - [20250519_004038.]: 'hyperbolic_regression': minmax = FALSE - [20250519_004038.]: Entered 'cubic_regression'-Function - [20250519_004038.]: 'cubic_regression': minmax = FALSE - [20250519_004038.]: # CpG-site: CpG#3 + [20250519_004759.]: Logging df_agg: CpG#2 + [20250519_004759.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004759.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_004759.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_004759.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_004759.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) + [20250519_004759.]: Entered 'hyperbolic_regression'-Function + [20250519_004759.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004759.]: Entered 'cubic_regression'-Function + [20250519_004759.]: 'cubic_regression': minmax = FALSE + [20250519_004759.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_004038.]: Logging df_agg: CpG#3 - [20250519_004038.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004038.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_004038.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_004038.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_004038.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_004038.]: Entered 'hyperbolic_regression'-Function - [20250519_004038.]: 'hyperbolic_regression': minmax = FALSE - [20250519_004038.]: Entered 'cubic_regression'-Function - [20250519_004038.]: 'cubic_regression': minmax = FALSE - [20250519_004038.]: # CpG-site: CpG#4 + [20250519_004759.]: Logging df_agg: CpG#3 + [20250519_004759.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004759.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_004759.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_004759.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_004759.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) + [20250519_004759.]: Entered 'hyperbolic_regression'-Function + [20250519_004759.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004759.]: Entered 'cubic_regression'-Function + [20250519_004759.]: 'cubic_regression': minmax = FALSE + [20250519_004759.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_004038.]: Logging df_agg: CpG#4 - [20250519_004038.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004038.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_004038.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_004038.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_004038.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_004038.]: Entered 'hyperbolic_regression'-Function - [20250519_004038.]: 'hyperbolic_regression': minmax = FALSE - [20250519_004038.]: Entered 'cubic_regression'-Function - [20250519_004038.]: 'cubic_regression': minmax = FALSE - [20250519_004038.]: # CpG-site: CpG#5 + [20250519_004759.]: Logging df_agg: CpG#4 + [20250519_004759.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004759.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_004759.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_004759.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_004759.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) + [20250519_004759.]: Entered 'hyperbolic_regression'-Function + [20250519_004759.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004759.]: Entered 'cubic_regression'-Function + [20250519_004759.]: 'cubic_regression': minmax = FALSE + [20250519_004759.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_004038.]: Logging df_agg: CpG#5 - [20250519_004038.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004038.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_004038.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_004038.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_004038.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_004038.]: Entered 'hyperbolic_regression'-Function - [20250519_004038.]: 'hyperbolic_regression': minmax = FALSE - [20250519_004038.]: Entered 'cubic_regression'-Function - [20250519_004038.]: 'cubic_regression': minmax = FALSE - [20250519_004038.]: # CpG-site: CpG#6 + [20250519_004759.]: Logging df_agg: CpG#5 + [20250519_004759.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004759.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_004759.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_004759.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_004759.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) + [20250519_004759.]: Entered 'hyperbolic_regression'-Function + [20250519_004759.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004759.]: Entered 'cubic_regression'-Function + [20250519_004759.]: 'cubic_regression': minmax = FALSE + [20250519_004759.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_004038.]: Logging df_agg: CpG#6 - [20250519_004038.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004038.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_004038.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_004038.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_004038.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_004038.]: Entered 'hyperbolic_regression'-Function - [20250519_004038.]: 'hyperbolic_regression': minmax = FALSE - [20250519_004038.]: Entered 'cubic_regression'-Function - [20250519_004038.]: 'cubic_regression': minmax = FALSE - [20250519_004038.]: # CpG-site: CpG#7 + [20250519_004759.]: Logging df_agg: CpG#6 + [20250519_004759.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004759.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_004759.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_004759.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_004759.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) + [20250519_004759.]: Entered 'hyperbolic_regression'-Function + [20250519_004759.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004759.]: Entered 'cubic_regression'-Function + [20250519_004759.]: 'cubic_regression': minmax = FALSE + [20250519_004759.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_004038.]: Logging df_agg: CpG#7 - [20250519_004038.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004038.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_004038.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_004038.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_004038.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_004038.]: Entered 'hyperbolic_regression'-Function - [20250519_004038.]: 'hyperbolic_regression': minmax = FALSE - [20250519_004038.]: Entered 'cubic_regression'-Function - [20250519_004038.]: 'cubic_regression': minmax = FALSE - [20250519_004038.]: # CpG-site: CpG#8 + [20250519_004759.]: Logging df_agg: CpG#7 + [20250519_004759.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004759.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_004759.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_004759.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_004759.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) + [20250519_004759.]: Entered 'hyperbolic_regression'-Function + [20250519_004759.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004759.]: Entered 'cubic_regression'-Function + [20250519_004759.]: 'cubic_regression': minmax = FALSE + [20250519_004759.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_004038.]: Logging df_agg: CpG#8 - [20250519_004038.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004038.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_004038.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_004038.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_004038.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_004038.]: Entered 'hyperbolic_regression'-Function - [20250519_004038.]: 'hyperbolic_regression': minmax = FALSE - [20250519_004038.]: Entered 'cubic_regression'-Function - [20250519_004038.]: 'cubic_regression': minmax = FALSE - [20250519_004038.]: # CpG-site: CpG#9 + [20250519_004759.]: Logging df_agg: CpG#8 + [20250519_004759.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004759.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_004759.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_004759.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_004759.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) + [20250519_004759.]: Entered 'hyperbolic_regression'-Function + [20250519_004759.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004759.]: Entered 'cubic_regression'-Function + [20250519_004759.]: 'cubic_regression': minmax = FALSE + [20250519_004759.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_004038.]: Logging df_agg: CpG#9 - [20250519_004038.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004038.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_004038.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_004038.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_004038.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_004038.]: Entered 'hyperbolic_regression'-Function - [20250519_004038.]: 'hyperbolic_regression': minmax = FALSE - [20250519_004038.]: Entered 'cubic_regression'-Function - [20250519_004038.]: 'cubic_regression': minmax = FALSE - [20250519_004038.]: # CpG-site: row_means + [20250519_004759.]: Logging df_agg: CpG#9 + [20250519_004759.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004759.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_004759.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_004759.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_004759.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) + [20250519_004759.]: Entered 'hyperbolic_regression'-Function + [20250519_004759.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004759.]: Entered 'cubic_regression'-Function + [20250519_004759.]: 'cubic_regression': minmax = FALSE + [20250519_004759.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_004038.]: Logging df_agg: row_means - [20250519_004038.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004038.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_004038.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_004038.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_004038.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_004038.]: Entered 'hyperbolic_regression'-Function - [20250519_004038.]: 'hyperbolic_regression': minmax = FALSE - [20250519_004038.]: Entered 'cubic_regression'-Function - [20250519_004038.]: 'cubic_regression': minmax = FALSE - [20250519_004039.]: ### Starting with plotting ### - [20250519_004039.]: Creating plot No. 1 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Test_CpG1.png - [20250519_004039.]: # CpG-site: CpG#1 + [20250519_004759.]: Logging df_agg: row_means + [20250519_004759.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004759.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_004759.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_004759.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_004759.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) + [20250519_004759.]: Entered 'hyperbolic_regression'-Function + [20250519_004759.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004759.]: Entered 'cubic_regression'-Function + [20250519_004759.]: 'cubic_regression': minmax = FALSE + [20250519_004800.]: ### Starting with plotting ### + [20250519_004800.]: Creating plot No. 1 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Test_CpG1.png + [20250519_004800.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -123249135.40483 , a = 49589952.0989949 , b = -123249135.40483 , d = 59280789.0121927 - [20250519_004039.]: # CpG-site: CpG#1 + [20250519_004800.]: # CpG-site: CpG#1 Cubic: Using a = 6.53413423120091e-05 , b = -0.0055806968734969 , c = 0.784061853455188 , d = 1.93182659932656 - [20250519_004041.]: Creating plot No. 2 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Test_CpG2.png - [20250519_004041.]: # CpG-site: CpG#2 + [20250519_004802.]: Creating plot No. 2 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Test_CpG2.png + [20250519_004802.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = 167929575.171327 , a = 42299444.0962795 , b = 167929575.171327 , d = 47897274.2220611 - [20250519_004041.]: # CpG-site: CpG#2 + [20250519_004802.]: # CpG-site: CpG#2 Cubic: Using a = 8.04237934904601e-06 , b = 0.00293158941798942 , c = 0.514821742825076 , d = 9.27667003367003 - [20250519_004042.]: Creating plot No. 3 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Test_CpG3.png - [20250519_004042.]: # CpG-site: CpG#3 + [20250519_004803.]: Creating plot No. 3 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Test_CpG3.png + [20250519_004803.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = 31004745.3756238 , a = 31050253.3779659 , b = 31004745.3756238 , d = 40665231.758814 - [20250519_004042.]: # CpG-site: CpG#3 + [20250519_004803.]: # CpG-site: CpG#3 Cubic: Using a = 2.30555869809204e-05 , b = -0.000797009331409346 , c = 0.62783129228796 , d = 3.88705218855218 - [20250519_004044.]: Creating plot No. 4 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Test_CpG4.png - [20250519_004044.]: # CpG-site: CpG#4 + [20250519_004805.]: Creating plot No. 4 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Test_CpG4.png + [20250519_004805.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = 128022258.276155 , a = 42779497.5538086 , b = 128022258.276155 , d = 50999439.9227065 - [20250519_004044.]: # CpG-site: CpG#4 + [20250519_004805.]: # CpG-site: CpG#4 Cubic: Using a = 3.2166356902357e-05 , b = -0.0015913142857143 , c = 0.697398364598366 , d = 6.17055050505048 - [20250519_004045.]: Creating plot No. 5 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Test_CpG5.png - [20250519_004045.]: # CpG-site: CpG#5 + [20250519_004806.]: Creating plot No. 5 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Test_CpG5.png + [20250519_004806.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = 29447513.6184781 , a = 41864399.8673718 , b = 29447513.6184781 , d = 55382346.8470806 - [20250519_004045.]: # CpG-site: CpG#5 + [20250519_004806.]: # CpG-site: CpG#5 Cubic: Using a = -4.48459708193036e-06 , b = 0.00375371236171235 , c = 0.422446384479718 , d = 5.17203872053872 - [20250519_004047.]: Creating plot No. 6 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Test_CpG6.png - [20250519_004047.]: # CpG-site: CpG#6 + [20250519_004808.]: Creating plot No. 6 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Test_CpG6.png + [20250519_004808.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = 17150080.5960938 , a = 40932834.1078748 , b = 17150080.5960938 , d = 46723471.6686748 - [20250519_004047.]: # CpG-site: CpG#6 + [20250519_004808.]: # CpG-site: CpG#6 Cubic: Using a = 2.38852884399552e-05 , b = 0.000918637037037021 , c = 0.561022132435467 , d = 6.1479276094276 - [20250519_004048.]: Creating plot No. 7 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Test_CpG7.png - [20250519_004048.]: # CpG-site: CpG#7 + [20250519_004809.]: Creating plot No. 7 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Test_CpG7.png + [20250519_004809.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -57204506.2862076 , a = 29554652.6579632 , b = -57204506.2862076 , d = 48548425.6483363 - [20250519_004048.]: # CpG-site: CpG#7 + [20250519_004809.]: # CpG-site: CpG#7 Cubic: Using a = 4.98010505050505e-05 , b = -0.00436152150072151 , c = 0.579588917748918 , d = 1.72104545454544 - [20250519_004050.]: Creating plot No. 8 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Test_CpG8.png - [20250519_004050.]: # CpG-site: CpG#8 + [20250519_004811.]: Creating plot No. 8 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Test_CpG8.png + [20250519_004811.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = 263871870.982016 , a = 38159683.1948542 , b = 263871870.982016 , d = 45284547.6704654 - [20250519_004050.]: # CpG-site: CpG#8 + [20250519_004811.]: # CpG-site: CpG#8 Cubic: Using a = -1.50060965207632e-05 , b = 0.00447978143338143 , c = 0.534897737694404 , d = 9.56981481481482 - [20250519_004051.]: Creating plot No. 9 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Test_CpG9.png - [20250519_004051.]: # CpG-site: CpG#9 + [20250519_004812.]: Creating plot No. 9 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Test_CpG9.png + [20250519_004812.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -224404013.660658 , a = 51672526.9851193 , b = -224404013.660658 , d = 60201205.3472543 - [20250519_004051.]: # CpG-site: CpG#9 + [20250519_004812.]: # CpG-site: CpG#9 Cubic: Using a = 7.72300426487093e-05 , b = -0.00542281173641174 , c = 0.67899229597563 , d = 2.72413468013467 - [20250519_004053.]: Creating plot No. 10 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Test_rowmeans.png - [20250519_004053.]: # CpG-site: row_means + [20250519_004814.]: Creating plot No. 10 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Test_rowmeans.png + [20250519_004814.]: # CpG-site: row_means Hyperbolic: Using bias_weight = 29838275.3876239 , a = 33244770.7608578 , b = 29838275.3876239 , d = 41191067.4974404 - [20250519_004053.]: # CpG-site: row_means + [20250519_004814.]: # CpG-site: row_means Cubic: Using a = 2.88923726150392e-05 , b = -0.000629959275292592 , c = 0.600117857944524 , d = 5.17789562289563 - [20250519_004054.]: on_start: using future::plan("sequential") - [20250519_004054.]: Entered 'clean_dt'-Function - [20250519_004054.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_004054.]: got experimental data - [20250519_004054.]: Entered 'clean_dt'-Function - [20250519_004054.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_004054.]: got calibration data - [20250519_004054.]: + [20250519_004815.]: on_start: using future::plan("sequential") + [20250519_004815.]: Entered 'clean_dt'-Function + [20250519_004815.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_004815.]: got experimental data + [20250519_004815.]: Entered 'clean_dt'-Function + [20250519_004815.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_004815.]: got calibration data + [20250519_004815.]: ### Starting with regression calculations ### - [20250519_004054.]: Entered 'regression_type1'-Function - [20250519_004054.]: # CpG-site: CpG#1 + [20250519_004815.]: Entered 'regression_type1'-Function + [20250519_004815.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_004054.]: Logging df_agg: CpG#1 - [20250519_004054.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004054.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_004054.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_004054.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_004054.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_004055.]: Entered 'hyperbolic_regression'-Function - [20250519_004055.]: 'hyperbolic_regression': minmax = FALSE - [20250519_004055.]: Entered 'cubic_regression'-Function - [20250519_004055.]: 'cubic_regression': minmax = FALSE - [20250519_004055.]: # CpG-site: CpG#2 + [20250519_004816.]: Logging df_agg: CpG#1 + [20250519_004816.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004816.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_004816.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_004816.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_004816.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) + [20250519_004816.]: Entered 'hyperbolic_regression'-Function + [20250519_004816.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004816.]: Entered 'cubic_regression'-Function + [20250519_004816.]: 'cubic_regression': minmax = FALSE + [20250519_004816.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_004055.]: Logging df_agg: CpG#2 - [20250519_004055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004055.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_004055.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_004055.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_004055.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_004055.]: Entered 'hyperbolic_regression'-Function - [20250519_004055.]: 'hyperbolic_regression': minmax = FALSE - [20250519_004055.]: Entered 'cubic_regression'-Function - [20250519_004055.]: 'cubic_regression': minmax = FALSE - [20250519_004055.]: # CpG-site: CpG#3 + [20250519_004816.]: Logging df_agg: CpG#2 + [20250519_004816.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004816.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_004816.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_004816.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_004816.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) + [20250519_004816.]: Entered 'hyperbolic_regression'-Function + [20250519_004816.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004816.]: Entered 'cubic_regression'-Function + [20250519_004816.]: 'cubic_regression': minmax = FALSE + [20250519_004816.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_004055.]: Logging df_agg: CpG#3 - [20250519_004055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004055.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_004055.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_004055.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_004055.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_004055.]: Entered 'hyperbolic_regression'-Function - [20250519_004055.]: 'hyperbolic_regression': minmax = FALSE - [20250519_004055.]: Entered 'cubic_regression'-Function - [20250519_004055.]: 'cubic_regression': minmax = FALSE - [20250519_004055.]: # CpG-site: CpG#4 + [20250519_004816.]: Logging df_agg: CpG#3 + [20250519_004816.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004816.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_004816.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_004816.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_004816.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) + [20250519_004816.]: Entered 'hyperbolic_regression'-Function + [20250519_004816.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004816.]: Entered 'cubic_regression'-Function + [20250519_004816.]: 'cubic_regression': minmax = FALSE + [20250519_004816.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_004055.]: Logging df_agg: CpG#4 - [20250519_004055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004055.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_004055.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_004055.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_004055.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_004055.]: Entered 'hyperbolic_regression'-Function - [20250519_004055.]: 'hyperbolic_regression': minmax = FALSE - [20250519_004055.]: Entered 'cubic_regression'-Function - [20250519_004055.]: 'cubic_regression': minmax = FALSE - [20250519_004055.]: # CpG-site: CpG#5 + [20250519_004816.]: Logging df_agg: CpG#4 + [20250519_004816.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004816.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_004816.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_004816.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_004816.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) + [20250519_004816.]: Entered 'hyperbolic_regression'-Function + [20250519_004816.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004816.]: Entered 'cubic_regression'-Function + [20250519_004816.]: 'cubic_regression': minmax = FALSE + [20250519_004816.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_004055.]: Logging df_agg: CpG#5 - [20250519_004055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004055.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_004055.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_004055.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_004055.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_004055.]: Entered 'hyperbolic_regression'-Function - [20250519_004055.]: 'hyperbolic_regression': minmax = FALSE - [20250519_004055.]: Entered 'cubic_regression'-Function - [20250519_004055.]: 'cubic_regression': minmax = FALSE - [20250519_004055.]: # CpG-site: CpG#6 + [20250519_004816.]: Logging df_agg: CpG#5 + [20250519_004816.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004816.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_004816.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_004816.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_004816.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) + [20250519_004816.]: Entered 'hyperbolic_regression'-Function + [20250519_004816.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004816.]: Entered 'cubic_regression'-Function + [20250519_004816.]: 'cubic_regression': minmax = FALSE + [20250519_004816.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_004055.]: Logging df_agg: CpG#6 - [20250519_004055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004055.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_004055.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_004055.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_004055.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_004055.]: Entered 'hyperbolic_regression'-Function - [20250519_004055.]: 'hyperbolic_regression': minmax = FALSE - [20250519_004055.]: Entered 'cubic_regression'-Function - [20250519_004055.]: 'cubic_regression': minmax = FALSE - [20250519_004055.]: # CpG-site: CpG#7 + [20250519_004816.]: Logging df_agg: CpG#6 + [20250519_004816.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004816.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_004816.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_004816.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_004816.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) + [20250519_004816.]: Entered 'hyperbolic_regression'-Function + [20250519_004816.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004816.]: Entered 'cubic_regression'-Function + [20250519_004816.]: 'cubic_regression': minmax = FALSE + [20250519_004816.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_004055.]: Logging df_agg: CpG#7 - [20250519_004055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004055.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_004055.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_004055.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_004055.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_004055.]: Entered 'hyperbolic_regression'-Function - [20250519_004055.]: 'hyperbolic_regression': minmax = FALSE - [20250519_004055.]: Entered 'cubic_regression'-Function - [20250519_004055.]: 'cubic_regression': minmax = FALSE - [20250519_004055.]: # CpG-site: CpG#8 + [20250519_004816.]: Logging df_agg: CpG#7 + [20250519_004816.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004816.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_004816.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_004816.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_004816.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) + [20250519_004816.]: Entered 'hyperbolic_regression'-Function + [20250519_004816.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004816.]: Entered 'cubic_regression'-Function + [20250519_004816.]: 'cubic_regression': minmax = FALSE + [20250519_004816.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_004055.]: Logging df_agg: CpG#8 - [20250519_004055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004055.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_004055.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_004055.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_004055.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_004055.]: Entered 'hyperbolic_regression'-Function - [20250519_004055.]: 'hyperbolic_regression': minmax = FALSE - [20250519_004055.]: Entered 'cubic_regression'-Function - [20250519_004055.]: 'cubic_regression': minmax = FALSE - [20250519_004055.]: # CpG-site: CpG#9 + [20250519_004816.]: Logging df_agg: CpG#8 + [20250519_004816.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004816.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_004816.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_004816.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_004816.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) + [20250519_004816.]: Entered 'hyperbolic_regression'-Function + [20250519_004816.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004816.]: Entered 'cubic_regression'-Function + [20250519_004816.]: 'cubic_regression': minmax = FALSE + [20250519_004816.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_004055.]: Logging df_agg: CpG#9 - [20250519_004055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004055.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_004055.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_004055.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_004055.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_004055.]: Entered 'hyperbolic_regression'-Function - [20250519_004055.]: 'hyperbolic_regression': minmax = FALSE - [20250519_004055.]: Entered 'cubic_regression'-Function - [20250519_004055.]: 'cubic_regression': minmax = FALSE - [20250519_004055.]: # CpG-site: row_means + [20250519_004816.]: Logging df_agg: CpG#9 + [20250519_004816.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004816.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_004816.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_004816.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_004816.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) + [20250519_004816.]: Entered 'hyperbolic_regression'-Function + [20250519_004816.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004816.]: Entered 'cubic_regression'-Function + [20250519_004816.]: 'cubic_regression': minmax = FALSE + [20250519_004816.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_004055.]: Logging df_agg: row_means - [20250519_004055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004055.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_004055.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_004055.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_004055.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_004055.]: Entered 'hyperbolic_regression'-Function - [20250519_004055.]: 'hyperbolic_regression': minmax = FALSE - [20250519_004055.]: Entered 'cubic_regression'-Function - [20250519_004055.]: 'cubic_regression': minmax = FALSE - [20250519_004056.]: Entered 'solving_equations'-Function - [20250519_004056.]: Solving cubic regression for CpG#1 + [20250519_004816.]: Logging df_agg: row_means + [20250519_004816.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004816.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_004816.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_004816.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_004816.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) + [20250519_004816.]: Entered 'hyperbolic_regression'-Function + [20250519_004816.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004816.]: Entered 'cubic_regression'-Function + [20250519_004816.]: 'cubic_regression': minmax = FALSE + [20250519_004817.]: Entered 'solving_equations'-Function + [20250519_004817.]: Solving cubic regression for CpG#1 Coefficients: -1.03617340067344Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_004056.]: Samplename: 0 + [20250519_004817.]: Samplename: 0 Root: 1.334 --> Root in between the borders! Added to results. Coefficients: -8.34150673400678Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_004056.]: Samplename: 12.5 + [20250519_004817.]: Samplename: 12.5 Root: 11.446 --> Root in between the borders! Added to results. Coefficients: -15.3881734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_004056.]: Samplename: 25 + [20250519_004817.]: Samplename: 25 Root: 22.228 --> Root in between the borders! Added to results. Coefficients: -24.2801734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_004056.]: Samplename: 37.5 + [20250519_004817.]: Samplename: 37.5 Root: 36.374 --> Root in between the borders! Added to results. Coefficients: -34.9006734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_004056.]: Samplename: 50 + [20250519_004817.]: Samplename: 50 Root: 52.044 --> Root in between the borders! Added to results. Coefficients: -46.3541734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_004056.]: Samplename: 62.5 + [20250519_004817.]: Samplename: 62.5 Root: 66.144 --> Root in between the borders! Added to results. Coefficients: -55.8931734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_004056.]: Samplename: 75 + [20250519_004817.]: Samplename: 75 Root: 75.864 --> Root in between the borders! Added to results. Coefficients: -63.0981734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_004056.]: Samplename: 87.5 + [20250519_004817.]: Samplename: 87.5 Root: 82.254 --> Root in between the borders! Added to results. Coefficients: -91.0461734006735Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_004056.]: Samplename: 100 + [20250519_004817.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.877 --> '100 < root < 110' --> substitute 100 - [20250519_004056.]: Solving cubic regression for CpG#2 + [20250519_004817.]: Solving cubic regression for CpG#2 Coefficients: -0.283329966329966Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_004056.]: Samplename: 0 + [20250519_004817.]: Samplename: 0 Root: 0.549 --> Root in between the borders! Added to results. Coefficients: -6.33999663299663Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_004056.]: Samplename: 12.5 + [20250519_004817.]: Samplename: 12.5 Root: 11.533 --> Root in between the borders! Added to results. Coefficients: -15.93932996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_004056.]: Samplename: 25 + [20250519_004817.]: Samplename: 25 Root: 26.628 --> Root in between the borders! Added to results. Coefficients: -22.33732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_004056.]: Samplename: 37.5 + [20250519_004817.]: Samplename: 37.5 Root: 35.509 --> Root in between the borders! Added to results. Coefficients: -32.22832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_004056.]: Samplename: 50 + [20250519_004817.]: Samplename: 50 Root: 47.851 --> Root in between the borders! Added to results. Coefficients: -49.96332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_004056.]: Samplename: 62.5 + [20250519_004817.]: Samplename: 62.5 Root: 66.893 --> Root in between the borders! Added to results. Coefficients: -58.96582996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_004056.]: Samplename: 75 + [20250519_004817.]: Samplename: 75 Root: 75.431 --> Root in between the borders! Added to results. Coefficients: -68.8366632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_004056.]: Samplename: 87.5 + [20250519_004817.]: Samplename: 87.5 Root: 84.118 --> Root in between the borders! Added to results. Coefficients: -90.57732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_004056.]: Samplename: 100 + [20250519_004817.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.287 --> '100 < root < 110' --> substitute 100 - [20250519_004056.]: Solving cubic regression for CpG#3 + [20250519_004817.]: Solving cubic regression for CpG#3 Coefficients: -0.90294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_004056.]: Samplename: 0 + [20250519_004817.]: Samplename: 0 Root: 1.441 --> Root in between the borders! Added to results. Coefficients: -6.57294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_004056.]: Samplename: 12.5 + [20250519_004817.]: Samplename: 12.5 Root: 10.568 --> Root in between the borders! Added to results. Coefficients: -15.4289478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_004056.]: Samplename: 25 + [20250519_004817.]: Samplename: 25 Root: 24.796 --> Root in between the borders! Added to results. Coefficients: -22.6129478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_004056.]: Samplename: 37.5 + [20250519_004817.]: Samplename: 37.5 Root: 35.952 --> Root in between the borders! Added to results. Coefficients: -32.7754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_004056.]: Samplename: 50 + [20250519_004817.]: Samplename: 50 Root: 50.684 --> Root in between the borders! Added to results. Coefficients: -43.8889478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_004056.]: Samplename: 62.5 + [20250519_004817.]: Samplename: 62.5 Root: 65.142 --> Root in between the borders! Added to results. Coefficients: -54.9754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_004056.]: Samplename: 75 + [20250519_004817.]: Samplename: 75 Root: 77.905 --> Root in between the borders! Added to results. Coefficients: -57.6562811447812Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_004056.]: Samplename: 87.5 + [20250519_004817.]: Samplename: 87.5 Root: 80.767 --> Root in between the borders! Added to results. Coefficients: -80.6649478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_004056.]: Samplename: 100 + [20250519_004817.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.38 --> '100 < root < 110' --> substitute 100 - [20250519_004056.]: Solving cubic regression for CpG#4 + [20250519_004817.]: Solving cubic regression for CpG#4 Coefficients: -0.597449494949524Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_004056.]: Samplename: 0 + [20250519_004817.]: Samplename: 0 Root: 0.858 --> Root in between the borders! Added to results. Coefficients: -8.25278282828286Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_004056.]: Samplename: 12.5 + [20250519_004817.]: Samplename: 12.5 Root: 12.086 --> Root in between the borders! Added to results. Coefficients: -15.8034494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_004056.]: Samplename: 25 + [20250519_004817.]: Samplename: 25 Root: 23.316 --> Root in between the borders! Added to results. Coefficients: -25.5274494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_004056.]: Samplename: 37.5 + [20250519_004817.]: Samplename: 37.5 Root: 37.383 --> Root in between the borders! Added to results. Coefficients: -33.6369494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_004056.]: Samplename: 50 + [20250519_004817.]: Samplename: 50 Root: 48.353 --> Root in between the borders! Added to results. Coefficients: -50.2554494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_004056.]: Samplename: 62.5 + [20250519_004817.]: Samplename: 62.5 Root: 68.082 --> Root in between the borders! Added to results. Coefficients: -56.5394494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_004056.]: Samplename: 75 + [20250519_004817.]: Samplename: 75 Root: 74.615 --> Root in between the borders! Added to results. Coefficients: -65.5927828282829Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_004056.]: Samplename: 87.5 + [20250519_004817.]: Samplename: 87.5 Root: 83.254 --> Root in between the borders! Added to results. Coefficients: -88.3214494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_004056.]: Samplename: 100 + [20250519_004817.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.715 --> '100 < root < 110' --> substitute 100 - [20250519_004056.]: Solving cubic regression for CpG#5 + [20250519_004817.]: Solving cubic regression for CpG#5 Coefficients: -0.623961279461278Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_004056.]: Samplename: 0 + [20250519_004817.]: Samplename: 0 Root: 1.458 --> Root in between the borders! Added to results. Coefficients: -4.76796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_004056.]: Samplename: 12.5 + [20250519_004817.]: Samplename: 12.5 Root: 10.347 --> Root in between the borders! Added to results. Coefficients: -12.7259612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_004056.]: Samplename: 25 + [20250519_004817.]: Samplename: 25 Root: 24.815 --> Root in between the borders! Added to results. Coefficients: -21.1599612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_004056.]: Samplename: 37.5 + [20250519_004817.]: Samplename: 37.5 Root: 37.902 --> Root in between the borders! Added to results. Coefficients: -30.6954612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_004056.]: Samplename: 50 + [20250519_004817.]: Samplename: 50 Root: 50.977 --> Root in between the borders! Added to results. Coefficients: -39.6579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_004056.]: Samplename: 62.5 + [20250519_004817.]: Samplename: 62.5 Root: 62.126 --> Root in between the borders! Added to results. Coefficients: -51.6829612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_004056.]: Samplename: 75 + [20250519_004817.]: Samplename: 75 Root: 75.852 --> Root in between the borders! Added to results. Coefficients: -61.0146279461279Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_004056.]: Samplename: 87.5 + [20250519_004817.]: Samplename: 87.5 Root: 85.767 --> Root in between the borders! Added to results. Coefficients: -76.0699612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_004056.]: Samplename: 100 + [20250519_004817.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.743 --> '100 < root < 110' --> substitute 100 - [20250519_004056.]: Solving cubic regression for CpG#6 + [20250519_004817.]: Solving cubic regression for CpG#6 Coefficients: -0.196072390572403Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_004056.]: Samplename: 0 + [20250519_004817.]: Samplename: 0 Root: 0.349 --> Root in between the borders! Added to results. Coefficients: -6.73873905723907Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_004056.]: Samplename: 12.5 + [20250519_004817.]: Samplename: 12.5 Root: 11.718 --> Root in between the borders! Added to results. Coefficients: -15.8880723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_004056.]: Samplename: 25 + [20250519_004817.]: Samplename: 25 Root: 26.396 --> Root in between the borders! Added to results. Coefficients: -22.0000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_004056.]: Samplename: 37.5 + [20250519_004817.]: Samplename: 37.5 Root: 35.301 --> Root in between the borders! Added to results. Coefficients: -33.4445723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_004056.]: Samplename: 50 + [20250519_004817.]: Samplename: 50 Root: 50.134 --> Root in between the borders! Added to results. Coefficients: -46.9000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_004056.]: Samplename: 62.5 + [20250519_004817.]: Samplename: 62.5 Root: 64.993 --> Root in between the borders! Added to results. Coefficients: -55.8320723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_004056.]: Samplename: 75 + [20250519_004817.]: Samplename: 75 Root: 73.639 --> Root in between the borders! Added to results. Coefficients: -71.5454057239057Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_004056.]: Samplename: 87.5 + [20250519_004817.]: Samplename: 87.5 Root: 87.043 --> Root in between the borders! Added to results. Coefficients: -89.6560723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_004056.]: Samplename: 100 + [20250519_004817.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.329 --> '100 < root < 110' --> substitute 100 - [20250519_004056.]: Solving cubic regression for CpG#7 + [20250519_004817.]: Solving cubic regression for CpG#7 Coefficients: -1.21495454545456Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_004056.]: Samplename: 0 + [20250519_004817.]: Samplename: 0 Root: 2.13 --> Root in between the borders! Added to results. Coefficients: -5.39562121212123Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_004056.]: Samplename: 12.5 + [20250519_004817.]: Samplename: 12.5 Root: 9.973 --> Root in between the borders! Added to results. Coefficients: -11.2649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_004056.]: Samplename: 25 + [20250519_004817.]: Samplename: 25 Root: 22.206 --> Root in between the borders! Added to results. Coefficients: -17.4509545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_004056.]: Samplename: 37.5 + [20250519_004817.]: Samplename: 37.5 Root: 35.814 --> Root in between the borders! Added to results. Coefficients: -26.0314545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_004056.]: Samplename: 50 + [20250519_004817.]: Samplename: 50 Root: 53.28 --> Root in between the borders! Added to results. Coefficients: -33.9649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_004056.]: Samplename: 62.5 + [20250519_004817.]: Samplename: 62.5 Root: 66.598 --> Root in between the borders! Added to results. Coefficients: -41.1689545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_004056.]: Samplename: 75 + [20250519_004817.]: Samplename: 75 Root: 76.575 --> Root in between the borders! Added to results. Coefficients: -44.1356212121212Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_004056.]: Samplename: 87.5 + [20250519_004817.]: Samplename: 87.5 Root: 80.219 --> Root in between the borders! Added to results. Coefficients: -67.2229545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_004056.]: Samplename: 100 + [20250519_004817.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.506 --> '100 < root < 110' --> substitute 100 - [20250519_004056.]: Solving cubic regression for CpG#8 + [20250519_004817.]: Solving cubic regression for CpG#8 Coefficients: -1.09618518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_004056.]: Samplename: 0 + [20250519_004817.]: Samplename: 0 Root: 2.016 --> Root in between the borders! Added to results. Coefficients: -5.44685185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_004056.]: Samplename: 12.5 + [20250519_004817.]: Samplename: 12.5 Root: 9.458 --> Root in between the borders! Added to results. Coefficients: -16.9301851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_004056.]: Samplename: 25 + [20250519_004817.]: Samplename: 25 Root: 26.35 --> Root in between the borders! Added to results. Coefficients: -23.3501851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_004056.]: Samplename: 37.5 + [20250519_004817.]: Samplename: 37.5 Root: 34.728 --> Root in between the borders! Added to results. Coefficients: -37.6251851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_004056.]: Samplename: 50 + [20250519_004817.]: Samplename: 50 Root: 51.781 --> Root in between the borders! Added to results. Coefficients: -48.8261851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_004056.]: Samplename: 62.5 + [20250519_004817.]: Samplename: 62.5 Root: 64.192 --> Root in between the borders! Added to results. Coefficients: -61.6676851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_004056.]: Samplename: 75 + [20250519_004817.]: Samplename: 75 Root: 77.804 --> Root in between the borders! Added to results. Coefficients: -64.5101851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_004056.]: Samplename: 87.5 + [20250519_004817.]: Samplename: 87.5 Root: 80.758 --> Root in between the borders! Added to results. Coefficients: -86.0601851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_004056.]: Samplename: 100 + [20250519_004817.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.834 --> '100 < root < 110' --> substitute 100 - [20250519_004056.]: Solving cubic regression for CpG#9 + [20250519_004817.]: Solving cubic regression for CpG#9 Coefficients: -0.989865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_004056.]: Samplename: 0 + [20250519_004817.]: Samplename: 0 Root: 1.475 --> Root in between the borders! Added to results. Coefficients: -6.39586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_004056.]: Samplename: 12.5 + [20250519_004817.]: Samplename: 12.5 Root: 10.12 --> Root in between the borders! Added to results. Coefficients: -14.7058653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_004056.]: Samplename: 25 + [20250519_004817.]: Samplename: 25 Root: 24.844 --> Root in between the borders! Added to results. Coefficients: -20.6238653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_004056.]: Samplename: 37.5 + [20250519_004817.]: Samplename: 37.5 Root: 35.327 --> Root in between the borders! Added to results. Coefficients: -31.3958653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_004056.]: Samplename: 50 + [20250519_004817.]: Samplename: 50 Root: 51.855 --> Root in between the borders! Added to results. Coefficients: -42.6858653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_004056.]: Samplename: 62.5 + [20250519_004817.]: Samplename: 62.5 Root: 65.265 --> Root in between the borders! Added to results. Coefficients: -52.9033653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_004056.]: Samplename: 75 + [20250519_004817.]: Samplename: 75 Root: 74.915 --> Root in between the borders! Added to results. Coefficients: -65.492531986532Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_004056.]: Samplename: 87.5 + [20250519_004817.]: Samplename: 87.5 Root: 84.67 --> Root in between the borders! Added to results. Coefficients: -92.9898653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_004056.]: Samplename: 100 + [20250519_004817.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.082 --> '100 < root < 110' --> substitute 100 - [20250519_004056.]: Solving cubic regression for row_means + [20250519_004817.]: Solving cubic regression for row_means Coefficients: -0.771215488215478Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 - [20250519_004056.]: Samplename: 0 + [20250519_004817.]: Samplename: 0 Root: 1.287 --> Root in between the borders! Added to results. Coefficients: -6.47247474747474Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 - [20250519_004056.]: Samplename: 12.5 + [20250519_004817.]: Samplename: 12.5 Root: 10.847 --> Root in between the borders! Added to results. Coefficients: -14.8972154882155Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 - [20250519_004056.]: Samplename: 25 + [20250519_004817.]: Samplename: 25 Root: 24.737 --> Root in between the borders! Added to results. Coefficients: -22.1492154882155Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 - [20250519_004056.]: Samplename: 37.5 + [20250519_004817.]: Samplename: 37.5 Root: 36.02 --> Root in between the borders! Added to results. Coefficients: -32.5259932659933Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 - [20250519_004056.]: Samplename: 50 + [20250519_004817.]: Samplename: 50 Root: 50.639 --> Root in between the borders! Added to results. Coefficients: -44.7218821548821Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 - [20250519_004056.]: Samplename: 62.5 + [20250519_004817.]: Samplename: 62.5 Root: 65.497 --> Root in between the borders! Added to results. Coefficients: -54.4032154882155Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 - [20250519_004056.]: Samplename: 75 + [20250519_004817.]: Samplename: 75 Root: 75.751 --> Root in between the borders! Added to results. Coefficients: -62.4313636363636Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 - [20250519_004056.]: Samplename: 87.5 + [20250519_004817.]: Samplename: 87.5 Root: 83.403 --> Root in between the borders! Added to results. Coefficients: -84.7343265993266Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 - [20250519_004056.]: Samplename: 100 + [20250519_004817.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.573 --> '100 < root < 110' --> substitute 100 - [20250519_004056.]: + [20250519_004817.]: ### Starting with regression calculations ### - [20250519_004056.]: Entered 'regression_type1'-Function - [20250519_004056.]: # CpG-site: CpG#1 + [20250519_004817.]: Entered 'regression_type1'-Function + [20250519_004817.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) - [20250519_004056.]: Logging df_agg: CpG#1 - [20250519_004056.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004056.]: c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)[20250519_004056.]: c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)[20250519_004056.]: c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) - [20250519_004056.]: Entered 'hyperbolic_regression'-Function - [20250519_004056.]: 'hyperbolic_regression': minmax = FALSE - [20250519_004056.]: Entered 'cubic_regression'-Function - [20250519_004056.]: 'cubic_regression': minmax = FALSE - [20250519_004056.]: # CpG-site: CpG#2 + [20250519_004817.]: Logging df_agg: CpG#1 + [20250519_004817.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004817.]: c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)[20250519_004817.]: c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)[20250519_004817.]: c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) + [20250519_004817.]: Entered 'hyperbolic_regression'-Function + [20250519_004817.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004817.]: Entered 'cubic_regression'-Function + [20250519_004817.]: 'cubic_regression': minmax = FALSE + [20250519_004817.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) - [20250519_004056.]: Logging df_agg: CpG#2 - [20250519_004056.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004056.]: c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)[20250519_004056.]: c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)[20250519_004056.]: c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) - [20250519_004056.]: Entered 'hyperbolic_regression'-Function - [20250519_004056.]: 'hyperbolic_regression': minmax = FALSE - [20250519_004056.]: Entered 'cubic_regression'-Function - [20250519_004056.]: 'cubic_regression': minmax = FALSE - [20250519_004056.]: # CpG-site: CpG#3 + [20250519_004817.]: Logging df_agg: CpG#2 + [20250519_004817.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004817.]: c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)[20250519_004817.]: c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)[20250519_004817.]: c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) + [20250519_004817.]: Entered 'hyperbolic_regression'-Function + [20250519_004817.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004817.]: Entered 'cubic_regression'-Function + [20250519_004817.]: 'cubic_regression': minmax = FALSE + [20250519_004817.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) - [20250519_004056.]: Logging df_agg: CpG#3 - [20250519_004056.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004056.]: c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)[20250519_004056.]: c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)[20250519_004056.]: c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) - [20250519_004056.]: Entered 'hyperbolic_regression'-Function - [20250519_004056.]: 'hyperbolic_regression': minmax = FALSE - [20250519_004056.]: Entered 'cubic_regression'-Function - [20250519_004056.]: 'cubic_regression': minmax = FALSE - [20250519_004056.]: # CpG-site: CpG#4 + [20250519_004817.]: Logging df_agg: CpG#3 + [20250519_004817.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004817.]: c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)[20250519_004817.]: c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)[20250519_004817.]: c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) + [20250519_004817.]: Entered 'hyperbolic_regression'-Function + [20250519_004817.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004817.]: Entered 'cubic_regression'-Function + [20250519_004817.]: 'cubic_regression': minmax = FALSE + [20250519_004817.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) - [20250519_004056.]: Logging df_agg: CpG#4 - [20250519_004056.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004056.]: c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)[20250519_004056.]: c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)[20250519_004056.]: c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) - [20250519_004056.]: Entered 'hyperbolic_regression'-Function - [20250519_004056.]: 'hyperbolic_regression': minmax = FALSE - [20250519_004057.]: Entered 'cubic_regression'-Function - [20250519_004057.]: 'cubic_regression': minmax = FALSE - [20250519_004057.]: # CpG-site: CpG#5 + [20250519_004817.]: Logging df_agg: CpG#4 + [20250519_004817.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004817.]: c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)[20250519_004817.]: c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)[20250519_004817.]: c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) + [20250519_004817.]: Entered 'hyperbolic_regression'-Function + [20250519_004817.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004817.]: Entered 'cubic_regression'-Function + [20250519_004817.]: 'cubic_regression': minmax = FALSE + [20250519_004817.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) - [20250519_004057.]: Logging df_agg: CpG#5 - [20250519_004057.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004057.]: c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)[20250519_004057.]: c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)[20250519_004057.]: c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) - [20250519_004057.]: Entered 'hyperbolic_regression'-Function - [20250519_004057.]: 'hyperbolic_regression': minmax = FALSE - [20250519_004057.]: Entered 'cubic_regression'-Function - [20250519_004057.]: 'cubic_regression': minmax = FALSE - [20250519_004057.]: # CpG-site: CpG#6 + [20250519_004817.]: Logging df_agg: CpG#5 + [20250519_004817.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004817.]: c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)[20250519_004817.]: c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)[20250519_004817.]: c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) + [20250519_004817.]: Entered 'hyperbolic_regression'-Function + [20250519_004817.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004817.]: Entered 'cubic_regression'-Function + [20250519_004817.]: 'cubic_regression': minmax = FALSE + [20250519_004817.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) - [20250519_004057.]: Logging df_agg: CpG#6 - [20250519_004057.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004057.]: c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)[20250519_004057.]: c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)[20250519_004057.]: c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) - [20250519_004057.]: Entered 'hyperbolic_regression'-Function - [20250519_004057.]: 'hyperbolic_regression': minmax = FALSE - [20250519_004057.]: Entered 'cubic_regression'-Function - [20250519_004057.]: 'cubic_regression': minmax = FALSE - [20250519_004057.]: # CpG-site: CpG#7 + [20250519_004817.]: Logging df_agg: CpG#6 + [20250519_004817.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004817.]: c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)[20250519_004817.]: c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)[20250519_004817.]: c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) + [20250519_004817.]: Entered 'hyperbolic_regression'-Function + [20250519_004817.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004817.]: Entered 'cubic_regression'-Function + [20250519_004817.]: 'cubic_regression': minmax = FALSE + [20250519_004817.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) - [20250519_004057.]: Logging df_agg: CpG#7 - [20250519_004057.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004057.]: c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)[20250519_004057.]: c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)[20250519_004057.]: c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) - [20250519_004057.]: Entered 'hyperbolic_regression'-Function - [20250519_004057.]: 'hyperbolic_regression': minmax = FALSE - [20250519_004057.]: Entered 'cubic_regression'-Function - [20250519_004057.]: 'cubic_regression': minmax = FALSE - [20250519_004057.]: # CpG-site: CpG#8 + [20250519_004817.]: Logging df_agg: CpG#7 + [20250519_004817.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004817.]: c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)[20250519_004817.]: c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)[20250519_004817.]: c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) + [20250519_004817.]: Entered 'hyperbolic_regression'-Function + [20250519_004818.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004818.]: Entered 'cubic_regression'-Function + [20250519_004818.]: 'cubic_regression': minmax = FALSE + [20250519_004818.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) - [20250519_004057.]: Logging df_agg: CpG#8 - [20250519_004057.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004057.]: c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)[20250519_004057.]: c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)[20250519_004057.]: c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) - [20250519_004057.]: Entered 'hyperbolic_regression'-Function - [20250519_004057.]: 'hyperbolic_regression': minmax = FALSE - [20250519_004057.]: Entered 'cubic_regression'-Function - [20250519_004057.]: 'cubic_regression': minmax = FALSE - [20250519_004057.]: # CpG-site: CpG#9 + [20250519_004818.]: Logging df_agg: CpG#8 + [20250519_004818.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004818.]: c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)[20250519_004818.]: c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)[20250519_004818.]: c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) + [20250519_004818.]: Entered 'hyperbolic_regression'-Function + [20250519_004818.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004818.]: Entered 'cubic_regression'-Function + [20250519_004818.]: 'cubic_regression': minmax = FALSE + [20250519_004818.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) - [20250519_004057.]: Logging df_agg: CpG#9 - [20250519_004057.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004057.]: c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)[20250519_004057.]: c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)[20250519_004057.]: c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) - [20250519_004057.]: Entered 'hyperbolic_regression'-Function - [20250519_004057.]: 'hyperbolic_regression': minmax = FALSE - [20250519_004057.]: Entered 'cubic_regression'-Function - [20250519_004057.]: 'cubic_regression': minmax = FALSE - [20250519_004057.]: # CpG-site: row_means + [20250519_004818.]: Logging df_agg: CpG#9 + [20250519_004818.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004818.]: c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)[20250519_004818.]: c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)[20250519_004818.]: c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) + [20250519_004818.]: Entered 'hyperbolic_regression'-Function + [20250519_004818.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004818.]: Entered 'cubic_regression'-Function + [20250519_004818.]: 'cubic_regression': minmax = FALSE + [20250519_004818.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) - [20250519_004057.]: Logging df_agg: row_means - [20250519_004057.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004057.]: c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)[20250519_004057.]: c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)[20250519_004057.]: c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) - [20250519_004057.]: Entered 'hyperbolic_regression'-Function - [20250519_004057.]: 'hyperbolic_regression': minmax = FALSE - [20250519_004057.]: Entered 'cubic_regression'-Function - [20250519_004057.]: 'cubic_regression': minmax = FALSE - [20250519_004057.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Test_error_CpG1_corrected_h.png - [20250519_004059.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Test_error_CpG2_corrected_h.png - [20250519_004100.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Test_error_CpG3_corrected_h.png - [20250519_004101.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Test_error_CpG4_corrected_h.png - [20250519_004103.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Test_error_CpG5_corrected_h.png - [20250519_004104.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Test_error_CpG6_corrected_h.png - [20250519_004105.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Test_error_CpG7_corrected_h.png - [20250519_004106.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Test_error_CpG8_corrected_h.png - [20250519_004108.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Test_error_CpG9_corrected_h.png - [20250519_004109.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/RtmpHizWK0/plotdir/Test_error_rowmeans_corrected_h.png - [20250519_004110.]: on_start: using future::plan("sequential") - [20250519_004112.]: on_start: using future::plan("sequential") + [20250519_004818.]: Logging df_agg: row_means + [20250519_004818.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_004818.]: c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)[20250519_004818.]: c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)[20250519_004818.]: c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) + [20250519_004818.]: Entered 'hyperbolic_regression'-Function + [20250519_004818.]: 'hyperbolic_regression': minmax = FALSE + [20250519_004818.]: Entered 'cubic_regression'-Function + [20250519_004818.]: 'cubic_regression': minmax = FALSE + [20250519_004818.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Test_error_CpG1_corrected_h.png + [20250519_004819.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Test_error_CpG2_corrected_h.png + [20250519_004821.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Test_error_CpG3_corrected_h.png + [20250519_004822.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Test_error_CpG4_corrected_h.png + [20250519_004823.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Test_error_CpG5_corrected_h.png + [20250519_004825.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Test_error_CpG6_corrected_h.png + [20250519_004826.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Test_error_CpG7_corrected_h.png + [20250519_004827.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Test_error_CpG8_corrected_h.png + [20250519_004828.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Test_error_CpG9_corrected_h.png + [20250519_004830.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/RtmpjxG02N/plotdir/Test_error_rowmeans_corrected_h.png + [20250519_004831.]: on_start: using future::plan("sequential") + [20250519_004833.]: on_start: using future::plan("sequential") [ FAIL 4 | WARN 209 | SKIP 12 | PASS 42 ] == Skipped tests (12) ========================================================== > library(data.table, lib.loc=R.home("library")) > (sig.diff.dt <- myDiff(Rvers)) Key: Empty data.table (0 rows and 3 cols): checking,master,release > > ## If there are significant differences, use git bisect to find when > ## they started. > if(nrow(sig.diff.dt)){ + dt.git <- file.path(task.dir, "data.table.git") + system(paste("cd ~/R/data.table && git fetch --tags")) + system(paste("git clone ~/R/data.table", dt.git)) + release.tag <- gsub(".tar.gz|.*_", "", cargs[["release"]]) + rev.parse.cmd <- paste( + "cd", dt.git, "&& git rev-parse master") + master.sha <- system(rev.parse.cmd, intern=TRUE) + merge.base.cmd <- paste( + "cd", dt.git, "&& git merge-base master", release.tag) + merge.base.sha <- system(merge.base.cmd, intern=TRUE) + old.sha <- merge.base.sha + run_R <- file.path(proj.dir, "install_dt_then_check_dep.R") + sig.diff.dt[, first.bad.commit := NA_character_] + sig.diff.dt[, comments := NA_character_] + for(diff.i in 1:nrow(sig.diff.dt)){ + sig.diff.row <- sig.diff.dt[diff.i] + bisect.cmd <- paste( + "cd", dt.git, "&&", + "git bisect start &&", + "git bisect old", old.sha, "&&", + "git bisect new master &&", + "git bisect run", + R.home('bin/Rscript'), + run_R, + shQuote(sig.diff.row$checking), + sig.diff.row$release, + rev.dep.release.tar.gz, + release.tag) + print(bisect.cmd) + bisect.out <- system(bisect.cmd, intern=TRUE) + cat(bisect.out,sep="\n") + if(is.null(attr(bisect.out,"status"))){ + first.bad.sha <- nc::capture_all_str( + bisect.out, + sha="[0-9a-f]+", + " is the first new commit")$sha + parent.cmd <- paste( + "cd ~/R/data.table && git log --pretty=%P -n 1", + first.bad.sha) + parent.sha <- system(parent.cmd, intern=TRUE) + sig.diff.dt[diff.i, first.bad.commit := first.bad.sha] + parent.msg <- paste0("parent=", parent.sha) + this.comment <- if(parent.sha==old.sha){ + paste(parent.msg, "same as git bisect old") + }else if(first.bad.sha==master.sha){ + paste("same as git bisect new=master,", parent.msg) + }else{ + parent.msg + } + sig.diff.dt[diff.i, comments := this.comment] + } + } + ## add CRAN column. + sig.diff.dt[, CRAN := { + flavor <- get_flavor(Rvers) + details <- data.table(flavor=unique(flavor))[, { + base <- "https://www.r-project.org/nosvn/R.check/" + u <- paste0(base, flavor, "/", rev.dep, "-00check.txt") + check.txt <- tempfile() + tryCatch({ + download.file(u, check.txt, quiet=TRUE) + }, error=function(e){ + NULL + }) + check.lines <- if(file.exists(check.txt)){ + readLines(check.txt,encoding="UTF-8") + }else{ + "" + } + repl.lines <- gsub("[\u2018\u2019]", "'", check.lines) + ##gsub("[‘’]", "'", check.lines) does not work with LC_ALL=C. + myStatus(line.vec=repl.lines) + }, by=flavor] + select.dt <- data.table(flavor, checking) + details[select.dt, msg, on=.(flavor, checking)] + }] + dir.create(file.path(job.dir, Rvers)) + diffs.csv <- file.path(job.dir, Rvers, "significant_differences.csv") + data.table::fwrite(sig.diff.dt, diffs.csv) + print(sig.diff.dt) + } > WARNING: ignoring environment value of R_HOME R version 4.5.0 (2025-04-11) -- "How About a Twenty-Six" Copyright (C) 2025 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > cargs <- commandArgs(trailingOnly=TRUE) > if(length(cargs)==0){ + ## before running interactively, make sure to start emacs/R with + ## environment defined in /scratch/...check_one.sh, particularly + ## R_LIBS_USER=/tmp/... otherwise we get error when installing + ## data.table. + base <- "/scratch/th798/data.table-revdeps/*" + cargs <- c( + Sys.glob(file.path(base,"deps.csv")), + "349", + Sys.glob(file.path(base, "data.table_release_*tar.gz")), + Sys.glob(file.path(base, "data.table_master_*tar.gz")) + ) + } > names(cargs) <- c("deps.csv", "task.str", "release", "master") > dput(cargs) c(deps.csv = "/scratch/th798/data.table-revdeps/2025-05-19/deps.csv", task.str = "1149", release = "/scratch/th798/data.table-revdeps/2025-05-19/data.table_release_1.17.2.tar.gz", master = "/scratch/th798/data.table-revdeps/2025-05-19/data.table_master_1.17.99.c4ea09e099b1833fae0eacb50552bce6f051ed82.tar.gz" ) > (task.dir <- dirname(.libPaths()[1]))#should be /tmp/th798/slurmid/R-vers [1] "/tmp/th798/17369309/R-release/1149" > if(requireNamespace("R.cache"))R.cache::getCachePath() Loading required namespace: R.cache [1] "/tmp/th798/17369309/R-release/1149/R.cache" > task.id <- as.integer(cargs[["task.str"]]) > deps.df <- read.csv(cargs[["deps.csv"]]) > (rev.dep <- deps.df$Package[task.id]) [1] "rBiasCorrection" > job.dir <- file.path(dirname(cargs[["deps.csv"]]), "tasks", task.id) > setwd(task.dir) > .libPaths() [1] "/tmp/th798/17369309/R-release/1149/library" [2] "/projects/genomic-ml/R/R-release/library" > options(repos=c(#this should be in ~/.Rprofile too. + CRAN="http://cloud.r-project.org")) > print(Sys.time()) [1] "2025-05-19 00:48:49 MST" > install.time <- system.time({ + install.packages(rev.dep, dep=TRUE) + }) Installing package into '/tmp/th798/17369309/R-release/1149/library' (as 'lib' is unspecified) also installing the dependencies 'xmlparsedata', 'lintr', 'microbenchmark', 'quarto' trying URL 'http://cloud.r-project.org/src/contrib/xmlparsedata_1.0.5.tar.gz' trying URL 'http://cloud.r-project.org/src/contrib/lintr_3.2.0.tar.gz' trying URL 'http://cloud.r-project.org/src/contrib/microbenchmark_1.5.0.tar.gz' trying URL 'http://cloud.r-project.org/src/contrib/quarto_1.4.4.tar.gz' trying URL 'http://cloud.r-project.org/src/contrib/rBiasCorrection_0.3.5.tar.gz' * installing *source* package 'xmlparsedata' ... ** this is package 'xmlparsedata' version '1.0.5' ** package 'xmlparsedata' successfully unpacked and MD5 sums checked ** using staged installation ** R ** byte-compile and prepare package for lazy loading ** help *** installing help indices ** building package indices ** testing if installed package can be loaded from temporary location ** testing if installed package can be loaded from final location ** testing if installed package keeps a record of temporary installation path * DONE (xmlparsedata) * installing *source* package 'microbenchmark' ... ** this is package 'microbenchmark' version '1.5.0' ** package 'microbenchmark' successfully unpacked and MD5 sums checked ** using staged installation checking for gcc... /packages/gcc/12.2.0-nnbserq/bin/gcc checking whether the C compiler works... yes checking for C compiler default output file name... a.out checking for suffix of executables... checking whether we are cross compiling... no checking for suffix of object files... o checking whether the compiler supports GNU C... yes checking whether /packages/gcc/12.2.0-nnbserq/bin/gcc accepts -g... yes checking for /packages/gcc/12.2.0-nnbserq/bin/gcc option to enable C11 features... none needed checking for stdio.h... yes checking for stdlib.h... yes checking for string.h... yes checking for inttypes.h... yes checking for stdint.h... yes checking for strings.h... yes checking for sys/stat.h... yes checking for sys/types.h... yes checking for unistd.h... yes checking for stdint.h... (cached) yes checking for stdlib.h... (cached) yes checking for sys/time.h... yes checking for uint64_t... yes checking for error_at_line... yes checking for mach_absolute_time... no checking for library containing clock_gettime... none required checking for best clockid_t to use with clock_gettime... CLOCK_MONOTONIC_RAW configure: creating ./config.status config.status: creating src/Makevars config.status: creating src/config.h ** libs using C compiler: 'gcc (Spack GCC) 12.2.0' /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -D_POSIX_C_SOURCE=200112L -DHAVE_CONFIG_H -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c do_nothing.c -o do_nothing.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -D_POSIX_C_SOURCE=200112L -DHAVE_CONFIG_H -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c init.c -o init.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -D_POSIX_C_SOURCE=200112L -DHAVE_CONFIG_H -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c nanotimer.c -o nanotimer.o /packages/gcc/12.2.0-nnbserq/bin/gcc -shared -L/home/th798/.conda/envs/emacs1/lib -Wl,-rpath=/home/th798/.conda/envs/emacs1/lib -L/home/th798/lib -Wl,-rpath=/home/th798/lib -L/home/th798/lib64 -Wl,-rpath=/home/th798/lib64 -o microbenchmark.so do_nothing.o init.o nanotimer.o installing to /tmp/th798/17369309/R-release/1149/library/00LOCK-microbenchmark/00new/microbenchmark/libs ** R ** inst ** byte-compile and prepare package for lazy loading ** help *** installing help indices ** building package indices ** testing if installed package can be loaded from temporary location ** checking absolute paths in shared objects and dynamic libraries ** testing if installed package can be loaded from final location ** testing if installed package keeps a record of temporary installation path * DONE (microbenchmark) * installing *source* package 'quarto' ... ** this is package 'quarto' version '1.4.4' ** package 'quarto' successfully unpacked and MD5 sums checked ** using staged installation ** R ** inst ** byte-compile and prepare package for lazy loading ** help *** installing help indices *** copying figures ** building package indices ** installing vignettes ** testing if installed package can be loaded from temporary location ** testing if installed package can be loaded from final location ** testing if installed package keeps a record of temporary installation path * DONE (quarto) * installing *source* package 'rBiasCorrection' ... ** this is package 'rBiasCorrection' version '0.3.5' ** package 'rBiasCorrection' successfully unpacked and MD5 sums checked ** using staged installation ** R ** data *** moving datasets to lazyload DB ** inst ** byte-compile and prepare package for lazy loading ** help *** installing help indices ** building package indices ** installing vignettes ** testing if installed package can be loaded from temporary location ** testing if installed package can be loaded from final location ** testing if installed package keeps a record of temporary installation path * DONE (rBiasCorrection) * installing *source* package 'lintr' ... ** this is package 'lintr' version '3.2.0' ** package 'lintr' successfully unpacked and MD5 sums checked ** using staged installation ** R ** inst ** byte-compile and prepare package for lazy loading ** help *** installing help indices *** copying figures ** building package indices ** installing vignettes ** testing if installed package can be loaded from temporary location ** testing if installed package can be loaded from final location ** testing if installed package keeps a record of temporary installation path * DONE (lintr) The downloaded source packages are in '/tmp/th798/17369309/Rtmp4wi1q9/downloaded_packages' > cat("Time to install revdep:\n") Time to install revdep: > print(install.time) user system elapsed 27.867 3.544 33.218 > print(Sys.time()) [1] "2025-05-19 00:49:23 MST" > downloaded_packages <- file.path( + tempdir(), + "downloaded_packages") > dl.glob <- file.path( + downloaded_packages, + paste0(rev.dep,"_*.tar.gz")) > rev.dep.dl.row <- cbind(rev.dep, Sys.glob(dl.glob)) > colnames(rev.dep.dl.row) <- c("pkg","path") > rev.dep.release.tar.gz <- normalizePath(rev.dep.dl.row[,"path"], mustWork=TRUE) > pkg.Rcheck <- paste0(rev.dep, ".Rcheck") > > proj.dir <- "~/genomic-ml/data.table-revdeps" > source(file.path(proj.dir, "myStatus.R")) > Rvers <- gsub("[()]", "", gsub(" ", "_", R.version[["version.string"]])) > dir.create(Rvers, showWarnings=FALSE) > Rcheck.list <- list() > for(dt.version.short in c("release", "master")){ + dt.tar.gz <- cargs[[dt.version.short]] + dt.version <- gsub(".tar.gz|/.*?_", "", dt.tar.gz) + print(Sys.time()) + install.packages(dt.tar.gz, repos=NULL) + print(Sys.time()) + check.cmd <- get_check_cmd(rev.dep.release.tar.gz) + system(check.cmd) + print(Sys.time()) + dest.Rcheck <- file.path( + Rvers, + paste0(dt.version, ".Rcheck")) + unlink(dest.Rcheck, recursive=TRUE) + file.rename(pkg.Rcheck, dest.Rcheck) + Rcheck.list[[dt.version]] <- file.path(dest.Rcheck, "00check.log") + } [1] "2025-05-19 00:49:23 MST" Installing package into '/tmp/th798/17369309/R-release/1149/library' (as 'lib' is unspecified) * installing *source* package 'data.table' ... ** this is package 'data.table' version '1.17.2' ** package 'data.table' successfully unpacked and MD5 sums checked ** using staged installation zlib 1.2.13 is available ok * checking if R installation supports OpenMP without any extra hints... yes ** libs using C compiler: 'gcc (Spack GCC) 12.2.0' /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c assign.c -o assign.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c between.c -o between.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c bmerge.c -o bmerge.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c chmatch.c -o chmatch.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c cj.c -o cj.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c coalesce.c -o coalesce.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c dogroups.c -o dogroups.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c fastmean.c -o fastmean.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c fcast.c -o fcast.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c fifelse.c -o fifelse.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c fmelt.c -o fmelt.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c forder.c -o forder.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c frank.c -o frank.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c fread.c -o fread.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c freadR.c -o freadR.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c froll.c -o froll.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c frollR.c -o frollR.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c frolladaptive.c -o frolladaptive.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c fsort.c -o fsort.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c fwrite.c -o fwrite.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c fwriteR.c -o fwriteR.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c gsumm.c -o gsumm.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c idatetime.c -o idatetime.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c ijoin.c -o ijoin.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c init.c -o init.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c inrange.c -o inrange.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c nafill.c -o nafill.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c negate.c -o negate.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c nqrecreateindices.c -o nqrecreateindices.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c openmp-utils.c -o openmp-utils.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c programming.c -o programming.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c quickselect.c -o quickselect.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c rbindlist.c -o rbindlist.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c reorder.c -o reorder.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c shift.c -o shift.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c snprintf.c -o snprintf.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c subset.c -o subset.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c transpose.c -o transpose.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c types.c -o types.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c uniqlist.c -o uniqlist.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c utils.c -o utils.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c vecseq.c -o vecseq.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c wrappers.c -o wrappers.o /packages/gcc/12.2.0-nnbserq/bin/gcc -shared -L/home/th798/.conda/envs/emacs1/lib -Wl,-rpath=/home/th798/.conda/envs/emacs1/lib -L/home/th798/lib -Wl,-rpath=/home/th798/lib -L/home/th798/lib64 -Wl,-rpath=/home/th798/lib64 -o data.table.so assign.o between.o bmerge.o chmatch.o cj.o coalesce.o dogroups.o fastmean.o fcast.o fifelse.o fmelt.o forder.o frank.o fread.o freadR.o froll.o frollR.o frolladaptive.o fsort.o fwrite.o fwriteR.o gsumm.o idatetime.o ijoin.o init.o inrange.o nafill.o negate.o nqrecreateindices.o openmp-utils.o programming.o quickselect.o rbindlist.o reorder.o shift.o snprintf.o subset.o transpose.o types.o uniqlist.o utils.o vecseq.o wrappers.o -fopenmp -L/home/th798/.conda/envs/emacs1/lib -lz PKG_CFLAGS = -fopenmp -I/home/th798/.conda/envs/emacs1/include PKG_LIBS = -fopenmp -L/home/th798/.conda/envs/emacs1/lib -lz if [ "data.table.so" != "data_table.so" ]; then mv data.table.so data_table.so; fi if [ "" != "Windows_NT" ] && [ `uname -s` = 'Darwin' ]; then install_name_tool -id data_table.so data_table.so; fi installing to /tmp/th798/17369309/R-release/1149/library/00LOCK-data.table/00new/data.table/libs ** R ** inst ** byte-compile and prepare package for lazy loading ** help *** installing help indices ** building package indices ** installing vignettes ** testing if installed package can be loaded from temporary location ** checking absolute paths in shared objects and dynamic libraries ** testing if installed package can be loaded from final location ** testing if installed package keeps a record of temporary installation path * DONE (data.table) [1] "2025-05-19 00:49:45 MST" * using log directory '/tmp/th798/17369309/R-release/1149/rBiasCorrection.Rcheck' * using R version 4.5.0 (2025-04-11) * using platform: x86_64-pc-linux-gnu * R was compiled by gcc (Spack GCC) 12.2.0 GNU Fortran (Spack GCC) 12.2.0 * running under: Red Hat Enterprise Linux 8.10 (Ootpa) * using session charset: ASCII * checking for file 'rBiasCorrection/DESCRIPTION' ... OK * this is package 'rBiasCorrection' version '0.3.5' * package encoding: UTF-8 * checking package namespace information ... OK * checking package dependencies ... OK * checking if this is a source package ... OK * checking if there is a namespace ... OK * checking for executable files ... OK * checking for hidden files and directories ... OK * checking for portable file names ... OK * checking for sufficient/correct file permissions ... OK * checking whether package 'rBiasCorrection' can be installed ... OK * checking installed package size ... OK * checking package directory ... OK * checking 'build' directory ... OK * checking DESCRIPTION meta-information ... OK * checking top-level files ... OK * checking for left-over files ... OK * checking index information ... OK * checking package subdirectories ... OK * checking code files for non-ASCII characters ... OK * checking R files for syntax errors ... OK * checking whether the package can be loaded ... OK * checking whether the package can be loaded with stated dependencies ... OK * checking whether the package can be unloaded cleanly ... OK * checking whether the namespace can be loaded with stated dependencies ... OK * checking whether the namespace can be unloaded cleanly ... OK * checking loading without being on the library search path ... OK * checking whether startup messages can be suppressed ... OK * checking dependencies in R code ... OK * checking S3 generic/method consistency ... OK * checking replacement functions ... OK * checking foreign function calls ... OK * checking R code for possible problems ... OK * checking Rd files ... OK * checking Rd metadata ... OK * checking Rd cross-references ... OK * checking for missing documentation entries ... OK * checking for code/documentation mismatches ... OK * checking Rd \usage sections ... OK * checking Rd contents ... OK * checking for unstated dependencies in examples ... OK * checking contents of 'data' directory ... OK * checking data for non-ASCII characters ... OK * checking LazyData ... OK * checking data for ASCII and uncompressed saves ... OK * checking installed files from 'inst/doc' ... OK * checking files in 'vignettes' ... OK * checking examples ... OK * checking for unstated dependencies in 'tests' ... OK * checking tests ... Running 'testthat.R' ERROR Running the tests in 'tests/testthat.R' failed. Complete output: > # https://github.com/Rdatatable/data.table/issues/5658 > Sys.setenv("OMP_THREAD_LIMIT" = 2) > Sys.setenv("Ncpu" = 2) > > library(testthat) > library(rBiasCorrection) > > local_edition(3) > > test_check("rBiasCorrection") [20250519_005047.]: Entered 'clean_dt'-Function [20250519_005047.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_005047.]: got experimental data [20250519_005047.]: Entered 'clean_dt'-Function [20250519_005047.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_005047.]: got calibration data [20250519_005047.]: ### Starting with regression calculations ### [20250519_005047.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_005047.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005047.]: Logging df_agg: CpG#1 [20250519_005047.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005047.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005047.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005047.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005047.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005047.]: Entered 'hyperbolic_regression'-Function [20250519_005047.]: 'hyperbolic_regression': minmax = FALSE [20250519_005048.]: Entered 'cubic_regression'-Function [20250519_005048.]: 'cubic_regression': minmax = FALSE [20250519_005048.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005048.]: Logging df_agg: CpG#2 [20250519_005048.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005048.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005048.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005048.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005048.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005048.]: Entered 'hyperbolic_regression'-Function [20250519_005048.]: 'hyperbolic_regression': minmax = FALSE [20250519_005048.]: Entered 'cubic_regression'-Function [20250519_005048.]: 'cubic_regression': minmax = FALSE [20250519_005048.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005048.]: Logging df_agg: CpG#3 [20250519_005048.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005048.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005048.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005048.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005048.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005048.]: Entered 'hyperbolic_regression'-Function [20250519_005048.]: 'hyperbolic_regression': minmax = FALSE [20250519_005048.]: Entered 'cubic_regression'-Function [20250519_005048.]: 'cubic_regression': minmax = FALSE [20250519_005048.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005048.]: Logging df_agg: CpG#4 [20250519_005048.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005048.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005048.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005048.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005048.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005048.]: Entered 'hyperbolic_regression'-Function [20250519_005048.]: 'hyperbolic_regression': minmax = FALSE [20250519_005048.]: Entered 'cubic_regression'-Function [20250519_005048.]: 'cubic_regression': minmax = FALSE [20250519_005049.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005049.]: Logging df_agg: CpG#5 [20250519_005049.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005049.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005049.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005049.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005049.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005049.]: Entered 'hyperbolic_regression'-Function [20250519_005049.]: 'hyperbolic_regression': minmax = FALSE [20250519_005049.]: Entered 'cubic_regression'-Function [20250519_005049.]: 'cubic_regression': minmax = FALSE [20250519_005049.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005049.]: Logging df_agg: CpG#6 [20250519_005049.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005049.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005049.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005049.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005049.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005049.]: Entered 'hyperbolic_regression'-Function [20250519_005049.]: 'hyperbolic_regression': minmax = FALSE [20250519_005049.]: Entered 'cubic_regression'-Function [20250519_005049.]: 'cubic_regression': minmax = FALSE [20250519_005049.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005049.]: Logging df_agg: CpG#7 [20250519_005049.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005049.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005049.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005049.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005049.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005049.]: Entered 'hyperbolic_regression'-Function [20250519_005049.]: 'hyperbolic_regression': minmax = FALSE [20250519_005049.]: Entered 'cubic_regression'-Function [20250519_005049.]: 'cubic_regression': minmax = FALSE [20250519_005049.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005049.]: Logging df_agg: CpG#8 [20250519_005049.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005049.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005049.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005049.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005049.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005049.]: Entered 'hyperbolic_regression'-Function [20250519_005049.]: 'hyperbolic_regression': minmax = FALSE [20250519_005050.]: Entered 'cubic_regression'-Function [20250519_005050.]: 'cubic_regression': minmax = FALSE [20250519_005050.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005050.]: Logging df_agg: CpG#9 [20250519_005050.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005050.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005050.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005050.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005050.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005050.]: Entered 'hyperbolic_regression'-Function [20250519_005050.]: 'hyperbolic_regression': minmax = FALSE [20250519_005050.]: Entered 'cubic_regression'-Function [20250519_005050.]: 'cubic_regression': minmax = FALSE [20250519_005050.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005050.]: Logging df_agg: row_means [20250519_005050.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005050.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005050.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_005050.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005050.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005050.]: Entered 'hyperbolic_regression'-Function [20250519_005050.]: 'hyperbolic_regression': minmax = FALSE [20250519_005050.]: Entered 'cubic_regression'-Function [20250519_005050.]: 'cubic_regression': minmax = FALSE [20250519_005051.]: ### Starting with regression calculations ### [20250519_005051.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_005051.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005051.]: Logging df_agg: CpG#1 [20250519_005051.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005051.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005051.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005051.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005051.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005051.]: Entered 'hyperbolic_regression'-Function [20250519_005051.]: 'hyperbolic_regression': minmax = FALSE [20250519_005051.]: Entered 'cubic_regression'-Function [20250519_005051.]: 'cubic_regression': minmax = FALSE [20250519_005051.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005051.]: Logging df_agg: CpG#2 [20250519_005051.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005051.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005051.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005051.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005051.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005051.]: Entered 'hyperbolic_regression'-Function [20250519_005051.]: 'hyperbolic_regression': minmax = FALSE [20250519_005051.]: Entered 'cubic_regression'-Function [20250519_005051.]: 'cubic_regression': minmax = FALSE [20250519_005051.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005051.]: Logging df_agg: CpG#3 [20250519_005051.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005051.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005051.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005051.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005051.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005051.]: Entered 'hyperbolic_regression'-Function [20250519_005051.]: 'hyperbolic_regression': minmax = FALSE [20250519_005052.]: Entered 'cubic_regression'-Function [20250519_005052.]: 'cubic_regression': minmax = FALSE [20250519_005052.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005052.]: Logging df_agg: CpG#4 [20250519_005052.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005052.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005052.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005052.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005052.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005052.]: Entered 'hyperbolic_regression'-Function [20250519_005052.]: 'hyperbolic_regression': minmax = FALSE [20250519_005052.]: Entered 'cubic_regression'-Function [20250519_005052.]: 'cubic_regression': minmax = FALSE [20250519_005052.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005052.]: Logging df_agg: CpG#5 [20250519_005052.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005052.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005052.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005052.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005052.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005052.]: Entered 'hyperbolic_regression'-Function [20250519_005052.]: 'hyperbolic_regression': minmax = FALSE [20250519_005052.]: Entered 'cubic_regression'-Function [20250519_005052.]: 'cubic_regression': minmax = FALSE [20250519_005052.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005052.]: Logging df_agg: CpG#6 [20250519_005052.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005052.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005052.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005052.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005052.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005052.]: Entered 'hyperbolic_regression'-Function [20250519_005052.]: 'hyperbolic_regression': minmax = FALSE [20250519_005052.]: Entered 'cubic_regression'-Function [20250519_005052.]: 'cubic_regression': minmax = FALSE [20250519_005052.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005052.]: Logging df_agg: CpG#7 [20250519_005052.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005052.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005052.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005052.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005052.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005052.]: Entered 'hyperbolic_regression'-Function [20250519_005052.]: 'hyperbolic_regression': minmax = FALSE [20250519_005052.]: Entered 'cubic_regression'-Function [20250519_005052.]: 'cubic_regression': minmax = FALSE [20250519_005052.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005052.]: Logging df_agg: CpG#8 [20250519_005052.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005052.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005052.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005052.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005052.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005052.]: Entered 'hyperbolic_regression'-Function [20250519_005052.]: 'hyperbolic_regression': minmax = FALSE [20250519_005053.]: Entered 'cubic_regression'-Function [20250519_005053.]: 'cubic_regression': minmax = FALSE [20250519_005053.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005053.]: Logging df_agg: CpG#9 [20250519_005053.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005053.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005053.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005053.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005053.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005053.]: Entered 'hyperbolic_regression'-Function [20250519_005053.]: 'hyperbolic_regression': minmax = FALSE [20250519_005053.]: Entered 'cubic_regression'-Function [20250519_005053.]: 'cubic_regression': minmax = FALSE [20250519_005053.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005053.]: Logging df_agg: row_means [20250519_005053.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005053.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005053.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_005053.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005053.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005053.]: Entered 'hyperbolic_regression'-Function [20250519_005053.]: 'hyperbolic_regression': minmax = FALSE [20250519_005053.]: Entered 'cubic_regression'-Function [20250519_005053.]: 'cubic_regression': minmax = FALSE [20250519_005055.]: ### Starting with regression calculations ### [20250519_005055.]: Entered 'regression_type1'-Function [20250519_005055.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005055.]: Logging df_agg: CpG#1 [20250519_005055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005055.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005055.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005055.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005055.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005055.]: Entered 'hyperbolic_regression'-Function [20250519_005055.]: 'hyperbolic_regression': minmax = FALSE [20250519_005055.]: Entered 'cubic_regression'-Function [20250519_005055.]: 'cubic_regression': minmax = FALSE [20250519_005055.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005055.]: Logging df_agg: CpG#2 [20250519_005055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005055.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005055.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005055.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005055.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005055.]: Entered 'hyperbolic_regression'-Function [20250519_005055.]: 'hyperbolic_regression': minmax = FALSE [20250519_005055.]: Entered 'cubic_regression'-Function [20250519_005055.]: 'cubic_regression': minmax = FALSE [20250519_005055.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005055.]: Logging df_agg: CpG#3 [20250519_005055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005055.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005055.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005055.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005055.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005055.]: Entered 'hyperbolic_regression'-Function [20250519_005055.]: 'hyperbolic_regression': minmax = FALSE [20250519_005055.]: Entered 'cubic_regression'-Function [20250519_005055.]: 'cubic_regression': minmax = FALSE [20250519_005055.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005055.]: Logging df_agg: CpG#4 [20250519_005055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005055.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005055.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005055.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005055.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005055.]: Entered 'hyperbolic_regression'-Function [20250519_005055.]: 'hyperbolic_regression': minmax = FALSE [20250519_005055.]: Entered 'cubic_regression'-Function [20250519_005055.]: 'cubic_regression': minmax = FALSE [20250519_005055.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005055.]: Logging df_agg: CpG#5 [20250519_005055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005055.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005055.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005055.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005055.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005055.]: Entered 'hyperbolic_regression'-Function [20250519_005055.]: 'hyperbolic_regression': minmax = FALSE [20250519_005055.]: Entered 'cubic_regression'-Function [20250519_005055.]: 'cubic_regression': minmax = FALSE [20250519_005055.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005055.]: Logging df_agg: CpG#6 [20250519_005055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005055.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005055.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005055.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005055.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005055.]: Entered 'hyperbolic_regression'-Function [20250519_005055.]: 'hyperbolic_regression': minmax = FALSE [20250519_005055.]: Entered 'cubic_regression'-Function [20250519_005055.]: 'cubic_regression': minmax = FALSE [20250519_005055.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005055.]: Logging df_agg: CpG#7 [20250519_005055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005055.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005055.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005055.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005055.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005055.]: Entered 'hyperbolic_regression'-Function [20250519_005055.]: 'hyperbolic_regression': minmax = FALSE [20250519_005055.]: Entered 'cubic_regression'-Function [20250519_005055.]: 'cubic_regression': minmax = FALSE [20250519_005055.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005055.]: Logging df_agg: CpG#8 [20250519_005055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005055.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005055.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005055.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005055.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005055.]: Entered 'hyperbolic_regression'-Function [20250519_005055.]: 'hyperbolic_regression': minmax = FALSE [20250519_005055.]: Entered 'cubic_regression'-Function [20250519_005055.]: 'cubic_regression': minmax = FALSE [20250519_005055.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005055.]: Logging df_agg: CpG#9 [20250519_005055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005055.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005055.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005055.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005055.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005055.]: Entered 'hyperbolic_regression'-Function [20250519_005055.]: 'hyperbolic_regression': minmax = FALSE [20250519_005055.]: Entered 'cubic_regression'-Function [20250519_005055.]: 'cubic_regression': minmax = FALSE [20250519_005055.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005055.]: Logging df_agg: row_means [20250519_005055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005055.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005055.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_005055.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005055.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005055.]: Entered 'hyperbolic_regression'-Function [20250519_005055.]: 'hyperbolic_regression': minmax = FALSE [20250519_005055.]: Entered 'cubic_regression'-Function [20250519_005055.]: 'cubic_regression': minmax = FALSE [20250519_005057.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_005057.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005057.]: Logging df_agg: CpG#1 [20250519_005057.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005057.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005057.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005057.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005057.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005057.]: Entered 'hyperbolic_regression'-Function [20250519_005057.]: 'hyperbolic_regression': minmax = FALSE [20250519_005057.]: Entered 'cubic_regression'-Function [20250519_005057.]: 'cubic_regression': minmax = FALSE [20250519_005057.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005057.]: Logging df_agg: CpG#2 [20250519_005057.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005057.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005057.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005057.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005057.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005057.]: Entered 'hyperbolic_regression'-Function [20250519_005057.]: 'hyperbolic_regression': minmax = FALSE [20250519_005058.]: Entered 'cubic_regression'-Function [20250519_005058.]: 'cubic_regression': minmax = FALSE [20250519_005058.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005058.]: Logging df_agg: CpG#3 [20250519_005058.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005058.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005058.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005058.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005058.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005058.]: Entered 'hyperbolic_regression'-Function [20250519_005058.]: 'hyperbolic_regression': minmax = FALSE [20250519_005058.]: Entered 'cubic_regression'-Function [20250519_005058.]: 'cubic_regression': minmax = FALSE [20250519_005058.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005058.]: Logging df_agg: CpG#4 [20250519_005058.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005058.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005058.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005058.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005058.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005058.]: Entered 'hyperbolic_regression'-Function [20250519_005058.]: 'hyperbolic_regression': minmax = FALSE [20250519_005058.]: Entered 'cubic_regression'-Function [20250519_005058.]: 'cubic_regression': minmax = FALSE [20250519_005058.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005058.]: Logging df_agg: CpG#5 [20250519_005058.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005058.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005058.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005058.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005058.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005058.]: Entered 'hyperbolic_regression'-Function [20250519_005058.]: 'hyperbolic_regression': minmax = FALSE [20250519_005058.]: Entered 'cubic_regression'-Function [20250519_005058.]: 'cubic_regression': minmax = FALSE [20250519_005058.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005058.]: Logging df_agg: CpG#6 [20250519_005058.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005058.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005058.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005058.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005058.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005058.]: Entered 'hyperbolic_regression'-Function [20250519_005058.]: 'hyperbolic_regression': minmax = FALSE [20250519_005059.]: Entered 'cubic_regression'-Function [20250519_005059.]: 'cubic_regression': minmax = FALSE [20250519_005059.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005059.]: Logging df_agg: CpG#7 [20250519_005059.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005059.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005059.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005059.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005059.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005059.]: Entered 'hyperbolic_regression'-Function [20250519_005059.]: 'hyperbolic_regression': minmax = FALSE [20250519_005059.]: Entered 'cubic_regression'-Function [20250519_005059.]: 'cubic_regression': minmax = FALSE [20250519_005059.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005059.]: Logging df_agg: CpG#8 [20250519_005059.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005059.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005059.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005059.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005059.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005059.]: Entered 'hyperbolic_regression'-Function [20250519_005059.]: 'hyperbolic_regression': minmax = FALSE [20250519_005059.]: Entered 'cubic_regression'-Function [20250519_005059.]: 'cubic_regression': minmax = FALSE [20250519_005059.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005059.]: Logging df_agg: CpG#9 [20250519_005059.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005059.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005059.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005059.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005059.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005059.]: Entered 'hyperbolic_regression'-Function [20250519_005059.]: 'hyperbolic_regression': minmax = FALSE [20250519_005059.]: Entered 'cubic_regression'-Function [20250519_005059.]: 'cubic_regression': minmax = FALSE [20250519_005059.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005059.]: Logging df_agg: row_means [20250519_005059.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005059.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005059.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_005059.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005059.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005059.]: Entered 'hyperbolic_regression'-Function [20250519_005059.]: 'hyperbolic_regression': minmax = FALSE [20250519_005100.]: Entered 'cubic_regression'-Function [20250519_005100.]: 'cubic_regression': minmax = FALSE [20250519_005101.]: Entered 'clean_dt'-Function [20250519_005101.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_005101.]: got experimental data [20250519_005101.]: Entered 'clean_dt'-Function [20250519_005101.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_005101.]: got calibration data [20250519_005101.]: ### Starting with regression calculations ### [20250519_005101.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_005101.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005101.]: Logging df_agg: CpG#1 [20250519_005101.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005101.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005101.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005101.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005101.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005101.]: Entered 'hyperbolic_regression'-Function [20250519_005101.]: 'hyperbolic_regression': minmax = FALSE [20250519_005101.]: Entered 'cubic_regression'-Function [20250519_005101.]: 'cubic_regression': minmax = FALSE [20250519_005101.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005101.]: Logging df_agg: CpG#2 [20250519_005101.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005101.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005101.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005101.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005101.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005101.]: Entered 'hyperbolic_regression'-Function [20250519_005101.]: 'hyperbolic_regression': minmax = FALSE [20250519_005101.]: Entered 'cubic_regression'-Function [20250519_005101.]: 'cubic_regression': minmax = FALSE [20250519_005101.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005101.]: Logging df_agg: CpG#3 [20250519_005101.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005101.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005101.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005101.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005101.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005101.]: Entered 'hyperbolic_regression'-Function [20250519_005101.]: 'hyperbolic_regression': minmax = FALSE [20250519_005101.]: Entered 'cubic_regression'-Function [20250519_005101.]: 'cubic_regression': minmax = FALSE [20250519_005101.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005101.]: Logging df_agg: CpG#4 [20250519_005101.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005101.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005101.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005101.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005101.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005101.]: Entered 'hyperbolic_regression'-Function [20250519_005101.]: 'hyperbolic_regression': minmax = FALSE [20250519_005102.]: Entered 'cubic_regression'-Function [20250519_005102.]: 'cubic_regression': minmax = FALSE [20250519_005102.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005102.]: Logging df_agg: CpG#5 [20250519_005102.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005102.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005102.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005102.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005102.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005102.]: Entered 'hyperbolic_regression'-Function [20250519_005102.]: 'hyperbolic_regression': minmax = FALSE [20250519_005102.]: Entered 'cubic_regression'-Function [20250519_005102.]: 'cubic_regression': minmax = FALSE [20250519_005102.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005102.]: Logging df_agg: CpG#6 [20250519_005102.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005102.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005102.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005102.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005102.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005102.]: Entered 'hyperbolic_regression'-Function [20250519_005102.]: 'hyperbolic_regression': minmax = FALSE [20250519_005102.]: Entered 'cubic_regression'-Function [20250519_005102.]: 'cubic_regression': minmax = FALSE [20250519_005102.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005102.]: Logging df_agg: CpG#7 [20250519_005102.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005102.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005102.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005102.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005102.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005102.]: Entered 'hyperbolic_regression'-Function [20250519_005102.]: 'hyperbolic_regression': minmax = FALSE [20250519_005102.]: Entered 'cubic_regression'-Function [20250519_005102.]: 'cubic_regression': minmax = FALSE [20250519_005102.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005102.]: Logging df_agg: CpG#8 [20250519_005102.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005102.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005102.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005102.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005102.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005102.]: Entered 'hyperbolic_regression'-Function [20250519_005102.]: 'hyperbolic_regression': minmax = FALSE [20250519_005103.]: Entered 'cubic_regression'-Function [20250519_005103.]: 'cubic_regression': minmax = FALSE [20250519_005103.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005103.]: Logging df_agg: CpG#9 [20250519_005103.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005103.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005103.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005103.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005103.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005103.]: Entered 'hyperbolic_regression'-Function [20250519_005103.]: 'hyperbolic_regression': minmax = FALSE [20250519_005103.]: Entered 'cubic_regression'-Function [20250519_005103.]: 'cubic_regression': minmax = FALSE [20250519_005103.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005103.]: Logging df_agg: row_means [20250519_005103.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005103.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005103.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_005103.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005103.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005103.]: Entered 'hyperbolic_regression'-Function [20250519_005103.]: 'hyperbolic_regression': minmax = FALSE [20250519_005103.]: Entered 'cubic_regression'-Function [20250519_005103.]: 'cubic_regression': minmax = FALSE [20250519_005104.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_005104.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005104.]: Logging df_agg: CpG#1 [20250519_005104.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005104.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005104.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005104.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005104.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005104.]: Entered 'hyperbolic_regression'-Function [20250519_005104.]: 'hyperbolic_regression': minmax = FALSE [20250519_005104.]: Entered 'cubic_regression'-Function [20250519_005104.]: 'cubic_regression': minmax = FALSE [20250519_005104.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005104.]: Logging df_agg: CpG#2 [20250519_005104.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005104.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005104.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005104.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005104.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005104.]: Entered 'hyperbolic_regression'-Function [20250519_005104.]: 'hyperbolic_regression': minmax = FALSE [20250519_005104.]: Entered 'cubic_regression'-Function [20250519_005104.]: 'cubic_regression': minmax = FALSE [20250519_005104.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005104.]: Logging df_agg: CpG#3 [20250519_005104.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005104.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005104.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005104.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005104.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005104.]: Entered 'hyperbolic_regression'-Function [20250519_005104.]: 'hyperbolic_regression': minmax = FALSE [20250519_005104.]: Entered 'cubic_regression'-Function [20250519_005104.]: 'cubic_regression': minmax = FALSE [20250519_005104.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005104.]: Logging df_agg: CpG#4 [20250519_005104.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005104.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005104.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005104.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005104.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005104.]: Entered 'hyperbolic_regression'-Function [20250519_005104.]: 'hyperbolic_regression': minmax = FALSE [20250519_005105.]: Entered 'cubic_regression'-Function [20250519_005105.]: 'cubic_regression': minmax = FALSE [20250519_005105.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005105.]: Logging df_agg: CpG#5 [20250519_005105.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005105.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005105.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005105.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005105.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005105.]: Entered 'hyperbolic_regression'-Function [20250519_005105.]: 'hyperbolic_regression': minmax = FALSE [20250519_005105.]: Entered 'cubic_regression'-Function [20250519_005105.]: 'cubic_regression': minmax = FALSE [20250519_005105.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005105.]: Logging df_agg: CpG#6 [20250519_005105.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005105.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005105.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005105.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005105.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005105.]: Entered 'hyperbolic_regression'-Function [20250519_005105.]: 'hyperbolic_regression': minmax = FALSE [20250519_005105.]: Entered 'cubic_regression'-Function [20250519_005105.]: 'cubic_regression': minmax = FALSE [20250519_005105.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005105.]: Logging df_agg: CpG#7 [20250519_005105.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005105.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005105.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005105.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005105.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005105.]: Entered 'hyperbolic_regression'-Function [20250519_005105.]: 'hyperbolic_regression': minmax = FALSE [20250519_005105.]: Entered 'cubic_regression'-Function [20250519_005105.]: 'cubic_regression': minmax = FALSE [20250519_005105.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005105.]: Logging df_agg: CpG#8 [20250519_005105.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005105.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005105.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005105.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005105.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005105.]: Entered 'hyperbolic_regression'-Function [20250519_005105.]: 'hyperbolic_regression': minmax = FALSE [20250519_005106.]: Entered 'cubic_regression'-Function [20250519_005106.]: 'cubic_regression': minmax = FALSE [20250519_005106.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005106.]: Logging df_agg: CpG#9 [20250519_005106.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005106.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005106.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005106.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005106.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005106.]: Entered 'hyperbolic_regression'-Function [20250519_005106.]: 'hyperbolic_regression': minmax = FALSE [20250519_005106.]: Entered 'cubic_regression'-Function [20250519_005106.]: 'cubic_regression': minmax = FALSE [20250519_005106.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005106.]: Logging df_agg: row_means [20250519_005106.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005106.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005106.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_005106.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005106.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005106.]: Entered 'hyperbolic_regression'-Function [20250519_005106.]: 'hyperbolic_regression': minmax = FALSE [20250519_005106.]: Entered 'cubic_regression'-Function [20250519_005106.]: 'cubic_regression': minmax = FALSE [20250519_005107.]: Entered 'solving_equations'-Function [20250519_005107.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: -2.23222837469517 [20250519_005107.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.232 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.1698391693126 [20250519_005107.]: Samplename: 12.5 Root: 12.17 --> Root in between the borders! Added to results. Hyperbolic solved: 24.4781729791561 [20250519_005107.]: Samplename: 25 Root: 24.478 --> Root in between the borders! Added to results. Hyperbolic solved: 38.1730159074047 [20250519_005107.]: Samplename: 37.5 Root: 38.173 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3348987967717 [20250519_005107.]: Samplename: 50 Root: 52.335 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4582305966058 [20250519_005107.]: Samplename: 62.5 Root: 65.458 --> Root in between the borders! Added to results. Hyperbolic solved: 75.0090269722885 [20250519_005107.]: Samplename: 75 Root: 75.009 --> Root in between the borders! Added to results. Hyperbolic solved: 81.527135738891 [20250519_005107.]: Samplename: 87.5 Root: 81.527 --> Root in between the borders! Added to results. Hyperbolic solved: 102.400825350044 [20250519_005107.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.401 --> '100 < root < 110' --> substitute 100 [20250519_005107.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 1.13663087266051 [20250519_005107.]: Samplename: 0 Root: 1.137 --> Root in between the borders! Added to results. Hyperbolic solved: 11.4130277963576 [20250519_005107.]: Samplename: 12.5 Root: 11.413 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1741025868157 [20250519_005107.]: Samplename: 25 Root: 26.174 --> Root in between the borders! Added to results. Hyperbolic solved: 35.1051721736724 [20250519_005107.]: Samplename: 37.5 Root: 35.105 --> Root in between the borders! Added to results. Hyperbolic solved: 47.6856613240114 [20250519_005107.]: Samplename: 50 Root: 47.686 --> Root in between the borders! Added to results. Hyperbolic solved: 67.1442585354696 [20250519_005107.]: Samplename: 62.5 Root: 67.144 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7646957109439 [20250519_005107.]: Samplename: 75 Root: 75.765 --> Root in between the borders! Added to results. Hyperbolic solved: 84.4056634834815 [20250519_005107.]: Samplename: 87.5 Root: 84.406 --> Root in between the borders! Added to results. Hyperbolic solved: 100.94855337669 [20250519_005107.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.949 --> '100 < root < 110' --> substitute 100 [20250519_005107.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 0.512348257854635 [20250519_005107.]: Samplename: 0 Root: 0.512 --> Root in between the borders! Added to results. Hyperbolic solved: 10.7523476241811 [20250519_005107.]: Samplename: 12.5 Root: 10.752 --> Root in between the borders! Added to results. Hyperbolic solved: 25.5218062516799 [20250519_005107.]: Samplename: 25 Root: 25.522 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5269315729339 [20250519_005107.]: Samplename: 37.5 Root: 36.527 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7907738274884 [20250519_005107.]: Samplename: 50 Root: 50.791 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8684489988589 [20250519_005107.]: Samplename: 62.5 Root: 64.868 --> Root in between the borders! Added to results. Hyperbolic solved: 77.5522101073979 [20250519_005107.]: Samplename: 75 Root: 77.552 --> Root in between the borders! Added to results. Hyperbolic solved: 80.4372474691229 [20250519_005107.]: Samplename: 87.5 Root: 80.437 --> Root in between the borders! Added to results. Hyperbolic solved: 102.703772810881 [20250519_005107.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.704 --> '100 < root < 110' --> substitute 100 [20250519_005107.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: -0.519513573026794 [20250519_005107.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.52 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.4933760051342 [20250519_005107.]: Samplename: 12.5 Root: 12.493 --> Root in between the borders! Added to results. Hyperbolic solved: 24.2684789512555 [20250519_005107.]: Samplename: 25 Root: 24.268 --> Root in between the borders! Added to results. Hyperbolic solved: 38.0816229393195 [20250519_005107.]: Samplename: 37.5 Root: 38.082 --> Root in between the borders! Added to results. Hyperbolic solved: 48.5842089589187 [20250519_005107.]: Samplename: 50 Root: 48.584 --> Root in between the borders! Added to results. Hyperbolic solved: 67.6720983549562 [20250519_005107.]: Samplename: 62.5 Root: 67.672 --> Root in between the borders! Added to results. Hyperbolic solved: 74.1547759694059 [20250519_005107.]: Samplename: 75 Root: 74.155 --> Root in between the borders! Added to results. Hyperbolic solved: 82.8820147905386 [20250519_005107.]: Samplename: 87.5 Root: 82.882 --> Root in between the borders! Added to results. Hyperbolic solved: 102.078935346876 [20250519_005107.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.079 --> '100 < root < 110' --> substitute 100 [20250519_005107.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 2.41558040143479 [20250519_005107.]: Samplename: 0 Root: 2.416 --> Root in between the borders! Added to results. Hyperbolic solved: 10.1649584830834 [20250519_005107.]: Samplename: 12.5 Root: 10.165 --> Root in between the borders! Added to results. Hyperbolic solved: 23.9830670020905 [20250519_005107.]: Samplename: 25 Root: 23.983 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2773406385708 [20250519_005107.]: Samplename: 37.5 Root: 37.277 --> Root in between the borders! Added to results. Hyperbolic solved: 50.8659103346102 [20250519_005107.]: Samplename: 50 Root: 50.866 --> Root in between the borders! Added to results. Hyperbolic solved: 62.4341926937382 [20250519_005107.]: Samplename: 62.5 Root: 62.434 --> Root in between the borders! Added to results. Hyperbolic solved: 76.3914832329149 [20250519_005107.]: Samplename: 75 Root: 76.391 --> Root in between the borders! Added to results. Hyperbolic solved: 86.1597399215782 [20250519_005107.]: Samplename: 87.5 Root: 86.16 --> Root in between the borders! Added to results. Hyperbolic solved: 100.267701841999 [20250519_005107.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.268 --> '100 < root < 110' --> substitute 100 [20250519_005107.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 0.138179963459196 [20250519_005107.]: Samplename: 0 Root: 0.138 --> Root in between the borders! Added to results. Hyperbolic solved: 11.8635989635202 [20250519_005107.]: Samplename: 12.5 Root: 11.864 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5108198460409 [20250519_005107.]: Samplename: 25 Root: 26.511 --> Root in between the borders! Added to results. Hyperbolic solved: 35.3206004021833 [20250519_005107.]: Samplename: 37.5 Root: 35.321 --> Root in between the borders! Added to results. Hyperbolic solved: 50.0571987473308 [20250519_005107.]: Samplename: 50 Root: 50.057 --> Root in between the borders! Added to results. Hyperbolic solved: 64.9604436607063 [20250519_005107.]: Samplename: 62.5 Root: 64.96 --> Root in between the borders! Added to results. Hyperbolic solved: 73.6690699266294 [20250519_005107.]: Samplename: 75 Root: 73.669 --> Root in between the borders! Added to results. Hyperbolic solved: 87.1267946879604 [20250519_005107.]: Samplename: 87.5 Root: 87.127 --> Root in between the borders! Added to results. Hyperbolic solved: 100.261842767865 [20250519_005107.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.262 --> '100 < root < 110' --> substitute 100 [20250519_005107.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: -1.37239249181669 [20250519_005107.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -1.372 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.199366215418 [20250519_005107.]: Samplename: 12.5 Root: 10.199 --> Root in between the borders! Added to results. Hyperbolic solved: 24.595295361327 [20250519_005107.]: Samplename: 25 Root: 24.595 --> Root in between the borders! Added to results. Hyperbolic solved: 37.8312095633391 [20250519_005107.]: Samplename: 37.5 Root: 37.831 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5590896582852 [20250519_005107.]: Samplename: 50 Root: 53.559 --> Root in between the borders! Added to results. Hyperbolic solved: 65.9367389282431 [20250519_005107.]: Samplename: 62.5 Root: 65.937 --> Root in between the borders! Added to results. Hyperbolic solved: 75.736370152841 [20250519_005107.]: Samplename: 75 Root: 75.736 --> Root in between the borders! Added to results. Hyperbolic solved: 79.433089359524 [20250519_005107.]: Samplename: 87.5 Root: 79.433 --> Root in between the borders! Added to results. Hyperbolic solved: 103.004516225662 [20250519_005107.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 103.005 --> '100 < root < 110' --> substitute 100 [20250519_005107.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 2.80067549526643 [20250519_005107.]: Samplename: 0 Root: 2.801 --> Root in between the borders! Added to results. Hyperbolic solved: 9.27534686003087 [20250519_005107.]: Samplename: 12.5 Root: 9.275 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4762624089952 [20250519_005107.]: Samplename: 25 Root: 25.476 --> Root in between the borders! Added to results. Hyperbolic solved: 34.0122098045585 [20250519_005107.]: Samplename: 37.5 Root: 34.012 --> Root in between the borders! Added to results. Hyperbolic solved: 51.784270967861 [20250519_005107.]: Samplename: 50 Root: 51.784 --> Root in between the borders! Added to results. Hyperbolic solved: 64.6732380258789 [20250519_005107.]: Samplename: 62.5 Root: 64.673 --> Root in between the borders! Added to results. Hyperbolic solved: 78.4327054483192 [20250519_005107.]: Samplename: 75 Root: 78.433 --> Root in between the borders! Added to results. Hyperbolic solved: 81.3427308951795 [20250519_005107.]: Samplename: 87.5 Root: 81.343 --> Root in between the borders! Added to results. Hyperbolic solved: 101.964413523995 [20250519_005107.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.964 --> '100 < root < 110' --> substitute 100 [20250519_005107.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: -2.13402763663736 [20250519_005107.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.134 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.5081558897243 [20250519_005107.]: Samplename: 12.5 Root: 10.508 --> Root in between the borders! Added to results. Hyperbolic solved: 26.9163121098627 [20250519_005107.]: Samplename: 25 Root: 26.916 --> Root in between the borders! Added to results. Hyperbolic solved: 36.8332915301825 [20250519_005107.]: Samplename: 37.5 Root: 36.833 --> Root in between the borders! Added to results. Hyperbolic solved: 52.00955 [20250519_005107.]: Samplename: 50 Root: 52.01 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8927781893359 [20250519_005107.]: Samplename: 62.5 Root: 64.893 --> Root in between the borders! Added to results. Hyperbolic solved: 74.566810799042 [20250519_005107.]: Samplename: 75 Root: 74.567 --> Root in between the borders! Added to results. Hyperbolic solved: 84.5291854856128 [20250519_005107.]: Samplename: 87.5 Root: 84.529 --> Root in between the borders! Added to results. Hyperbolic solved: 101.046823491232 [20250519_005107.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.047 --> '100 < root < 110' --> substitute 100 [20250519_005107.]: Solving hyperbolic regression for row_means Hyperbolic solved: 0.290936038655697 [20250519_005107.]: Samplename: 0 Root: 0.291 --> Root in between the borders! Added to results. Hyperbolic solved: 11.0412364555656 [20250519_005107.]: Samplename: 12.5 Root: 11.041 --> Root in between the borders! Added to results. Hyperbolic solved: 25.408146358228 [20250519_005107.]: Samplename: 25 Root: 25.408 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5243683648753 [20250519_005107.]: Samplename: 37.5 Root: 36.524 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7348788337891 [20250519_005107.]: Samplename: 50 Root: 50.735 --> Root in between the borders! Added to results. Hyperbolic solved: 65.3135169308955 [20250519_005107.]: Samplename: 62.5 Root: 65.314 --> Root in between the borders! Added to results. Hyperbolic solved: 75.5342663138126 [20250519_005107.]: Samplename: 75 Root: 75.534 --> Root in between the borders! Added to results. Hyperbolic solved: 83.2411177153911 [20250519_005107.]: Samplename: 87.5 Root: 83.241 --> Root in between the borders! Added to results. Hyperbolic solved: 101.666935931185 [20250519_005107.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.667 --> '100 < root < 110' --> substitute 100 [20250519_005107.]: ### Starting with regression calculations ### [20250519_005107.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_005107.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.1698391693126, 24.4781729791561, 38.1730159074047, 52.3348987967717, 65.4582305966058, 75.0090269722885, 81.527135738891, 100)c(0, 0.330160830687401, 0.521827020843901, 0.673015907404697, 2.3348987967717, 2.9582305966058, 0.00902697228849547, 5.972864261109, 0)c(NA, 2.64128664549921, 2.0873080833756, 1.79470908641252, 4.66979759354339, 4.73316895456928, 0.0120359630513273, 6.82613058412457, 0) [20250519_005107.]: Logging df_agg: CpG#1 [20250519_005107.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005107.]: c(0, 12.1698391693126, 24.4781729791561, 38.1730159074047, 52.3348987967717, 65.4582305966058, 75.0090269722885, 81.527135738891, 100)[20250519_005107.]: c(0, 0.330160830687401, 0.521827020843901, 0.673015907404697, 2.3348987967717, 2.9582305966058, 0.00902697228849547, 5.972864261109, 0)[20250519_005107.]: c(NA, 2.64128664549921, 2.0873080833756, 1.79470908641252, 4.66979759354339, 4.73316895456928, 0.0120359630513273, 6.82613058412457, 0) [20250519_005107.]: Entered 'hyperbolic_regression'-Function [20250519_005107.]: 'hyperbolic_regression': minmax = FALSE [20250519_005107.]: Entered 'cubic_regression'-Function [20250519_005107.]: 'cubic_regression': minmax = FALSE [20250519_005107.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.13663087266051, 11.4130277963576, 26.1741025868157, 35.1051721736724, 47.6856613240114, 67.1442585354696, 75.7646957109439, 84.4056634834815, 100)c(1.13663087266051, 1.0869722036424, 1.1741025868157, 2.3948278263276, 2.3143386759886, 4.6442585354696, 0.764695710943897, 3.0943365165185, 0)c(NA, 8.6957776291392, 4.6964103472628, 6.3862075368736, 4.6286773519772, 7.43081365675137, 1.01959428125853, 3.53638459030685, 0) [20250519_005107.]: Logging df_agg: CpG#2 [20250519_005107.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005107.]: c(1.13663087266051, 11.4130277963576, 26.1741025868157, 35.1051721736724, 47.6856613240114, 67.1442585354696, 75.7646957109439, 84.4056634834815, 100)[20250519_005107.]: c(1.13663087266051, 1.0869722036424, 1.1741025868157, 2.3948278263276, 2.3143386759886, 4.6442585354696, 0.764695710943897, 3.0943365165185, 0)[20250519_005107.]: c(NA, 8.6957776291392, 4.6964103472628, 6.3862075368736, 4.6286773519772, 7.43081365675137, 1.01959428125853, 3.53638459030685, 0) [20250519_005107.]: Entered 'hyperbolic_regression'-Function [20250519_005107.]: 'hyperbolic_regression': minmax = FALSE [20250519_005107.]: Entered 'cubic_regression'-Function [20250519_005107.]: 'cubic_regression': minmax = FALSE [20250519_005107.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.512348257854635, 10.7523476241811, 25.5218062516799, 36.5269315729339, 50.7907738274884, 64.8684489988589, 77.5522101073979, 80.4372474691229, 100)c(0.512348257854635, 1.7476523758189, 0.521806251679902, 0.973068427066103, 0.790773827488401, 2.3684489988589, 2.5522101073979, 7.06275253087711, 0)c(NA, 13.9812190065512, 2.08722500671961, 2.59484913884294, 1.5815476549768, 3.78951839817423, 3.40294680986386, 8.07171717814526, 0) [20250519_005108.]: Logging df_agg: CpG#3 [20250519_005108.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005108.]: c(0.512348257854635, 10.7523476241811, 25.5218062516799, 36.5269315729339, 50.7907738274884, 64.8684489988589, 77.5522101073979, 80.4372474691229, 100)[20250519_005108.]: c(0.512348257854635, 1.7476523758189, 0.521806251679902, 0.973068427066103, 0.790773827488401, 2.3684489988589, 2.5522101073979, 7.06275253087711, 0)[20250519_005108.]: c(NA, 13.9812190065512, 2.08722500671961, 2.59484913884294, 1.5815476549768, 3.78951839817423, 3.40294680986386, 8.07171717814526, 0) [20250519_005108.]: Entered 'hyperbolic_regression'-Function [20250519_005108.]: 'hyperbolic_regression': minmax = FALSE [20250519_005108.]: Entered 'cubic_regression'-Function [20250519_005108.]: 'cubic_regression': minmax = FALSE [20250519_005108.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.4933760051342, 24.2684789512555, 38.0816229393195, 48.5842089589187, 67.6720983549562, 74.1547759694059, 82.8820147905386, 100)c(0, 0.00662399486579979, 0.731521048744501, 0.581622939319502, 1.4157910410813, 5.1720983549562, 0.845224030594096, 4.61798520946139, 0)c(NA, 0.0529919589263983, 2.926084194978, 1.550994504852, 2.8315820821626, 8.27535736792993, 1.12696537412546, 5.27769738224159, 0) [20250519_005108.]: Logging df_agg: CpG#4 [20250519_005108.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005108.]: c(0, 12.4933760051342, 24.2684789512555, 38.0816229393195, 48.5842089589187, 67.6720983549562, 74.1547759694059, 82.8820147905386, 100)[20250519_005108.]: c(0, 0.00662399486579979, 0.731521048744501, 0.581622939319502, 1.4157910410813, 5.1720983549562, 0.845224030594096, 4.61798520946139, 0)[20250519_005108.]: c(NA, 0.0529919589263983, 2.926084194978, 1.550994504852, 2.8315820821626, 8.27535736792993, 1.12696537412546, 5.27769738224159, 0) [20250519_005108.]: Entered 'hyperbolic_regression'-Function [20250519_005108.]: 'hyperbolic_regression': minmax = FALSE [20250519_005108.]: Entered 'cubic_regression'-Function [20250519_005108.]: 'cubic_regression': minmax = FALSE [20250519_005108.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.41558040143479, 10.1649584830834, 23.9830670020905, 37.2773406385708, 50.8659103346102, 62.4341926937382, 76.3914832329149, 86.1597399215782, 100)c(2.41558040143479, 2.3350415169166, 1.0169329979095, 0.222659361429201, 0.865910334610199, 0.065807306261803, 1.39148323291489, 1.34026007842181, 0)c(NA, 18.6803321353328, 4.067731991638, 0.593758297144537, 1.7318206692204, 0.105291690018885, 1.85531097721986, 1.53172580391064, 0) [20250519_005108.]: Logging df_agg: CpG#5 [20250519_005108.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005108.]: c(2.41558040143479, 10.1649584830834, 23.9830670020905, 37.2773406385708, 50.8659103346102, 62.4341926937382, 76.3914832329149, 86.1597399215782, 100)[20250519_005108.]: c(2.41558040143479, 2.3350415169166, 1.0169329979095, 0.222659361429201, 0.865910334610199, 0.065807306261803, 1.39148323291489, 1.34026007842181, 0)[20250519_005108.]: c(NA, 18.6803321353328, 4.067731991638, 0.593758297144537, 1.7318206692204, 0.105291690018885, 1.85531097721986, 1.53172580391064, 0) [20250519_005108.]: Entered 'hyperbolic_regression'-Function [20250519_005108.]: 'hyperbolic_regression': minmax = FALSE [20250519_005108.]: Entered 'cubic_regression'-Function [20250519_005108.]: 'cubic_regression': minmax = FALSE [20250519_005108.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.138179963459196, 11.8635989635202, 26.5108198460409, 35.3206004021833, 50.0571987473308, 64.9604436607063, 73.6690699266294, 87.1267946879604, 100)c(0.138179963459196, 0.6364010364798, 1.5108198460409, 2.1793995978167, 0.0571987473308013, 2.4604436607063, 1.3309300733706, 0.373205312039602, 0)c(NA, 5.0912082918384, 6.0432793841636, 5.81173226084453, 0.114397494661603, 3.93670985713009, 1.77457343116079, 0.426520356616688, 0) [20250519_005108.]: Logging df_agg: CpG#6 [20250519_005108.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005108.]: c(0.138179963459196, 11.8635989635202, 26.5108198460409, 35.3206004021833, 50.0571987473308, 64.9604436607063, 73.6690699266294, 87.1267946879604, 100)[20250519_005108.]: c(0.138179963459196, 0.6364010364798, 1.5108198460409, 2.1793995978167, 0.0571987473308013, 2.4604436607063, 1.3309300733706, 0.373205312039602, 0)[20250519_005108.]: c(NA, 5.0912082918384, 6.0432793841636, 5.81173226084453, 0.114397494661603, 3.93670985713009, 1.77457343116079, 0.426520356616688, 0) [20250519_005108.]: Entered 'hyperbolic_regression'-Function [20250519_005108.]: 'hyperbolic_regression': minmax = FALSE [20250519_005108.]: Entered 'cubic_regression'-Function [20250519_005108.]: 'cubic_regression': minmax = FALSE [20250519_005108.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.199366215418, 24.595295361327, 37.8312095633391, 53.5590896582852, 65.9367389282431, 75.736370152841, 79.433089359524, 100)c(0, 2.300633784582, 0.404704638673, 0.331209563339101, 3.5590896582852, 3.4367389282431, 0.736370152841005, 8.066910640476, 0)c(NA, 18.405070276656, 1.618818554692, 0.883225502237603, 7.11817931657041, 5.49878228518896, 0.981826870454673, 9.21932644625828, 0) [20250519_005108.]: Logging df_agg: CpG#7 [20250519_005108.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005108.]: c(0, 10.199366215418, 24.595295361327, 37.8312095633391, 53.5590896582852, 65.9367389282431, 75.736370152841, 79.433089359524, 100)[20250519_005108.]: c(0, 2.300633784582, 0.404704638673, 0.331209563339101, 3.5590896582852, 3.4367389282431, 0.736370152841005, 8.066910640476, 0)[20250519_005108.]: c(NA, 18.405070276656, 1.618818554692, 0.883225502237603, 7.11817931657041, 5.49878228518896, 0.981826870454673, 9.21932644625828, 0) [20250519_005108.]: Entered 'hyperbolic_regression'-Function [20250519_005108.]: 'hyperbolic_regression': minmax = FALSE [20250519_005109.]: Entered 'cubic_regression'-Function [20250519_005109.]: 'cubic_regression': minmax = FALSE [20250519_005109.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.80067549526643, 9.27534686003087, 25.4762624089952, 34.0122098045585, 51.784270967861, 64.6732380258789, 78.4327054483192, 81.3427308951795, 100)c(2.80067549526643, 3.22465313996913, 0.476262408995201, 3.4877901954415, 1.784270967861, 2.1732380258789, 3.4327054483192, 6.1572691048205, 0)c(NA, 25.797225119753, 1.9050496359808, 9.30077385451066, 3.568541935722, 3.47718084140624, 4.57694059775893, 7.03687897693771, 0) [20250519_005109.]: Logging df_agg: CpG#8 [20250519_005109.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005109.]: c(2.80067549526643, 9.27534686003087, 25.4762624089952, 34.0122098045585, 51.784270967861, 64.6732380258789, 78.4327054483192, 81.3427308951795, 100)[20250519_005109.]: c(2.80067549526643, 3.22465313996913, 0.476262408995201, 3.4877901954415, 1.784270967861, 2.1732380258789, 3.4327054483192, 6.1572691048205, 0)[20250519_005109.]: c(NA, 25.797225119753, 1.9050496359808, 9.30077385451066, 3.568541935722, 3.47718084140624, 4.57694059775893, 7.03687897693771, 0) [20250519_005109.]: Entered 'hyperbolic_regression'-Function [20250519_005109.]: 'hyperbolic_regression': minmax = FALSE [20250519_005109.]: Entered 'cubic_regression'-Function [20250519_005109.]: 'cubic_regression': minmax = FALSE [20250519_005109.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.5081558897243, 26.9163121098627, 36.8332915301825, 52.00955, 64.8927781893359, 74.566810799042, 84.5291854856128, 100)c(0, 1.9918441102757, 1.9163121098627, 0.6667084698175, 2.00955, 2.3927781893359, 0.433189200957997, 2.9708145143872, 0)c(NA, 15.9347528822056, 7.66524843945081, 1.77788925284667, 4.01909999999999, 3.82844510293744, 0.57758560127733, 3.39521658787109, 0) [20250519_005109.]: Logging df_agg: CpG#9 [20250519_005109.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005109.]: c(0, 10.5081558897243, 26.9163121098627, 36.8332915301825, 52.00955, 64.8927781893359, 74.566810799042, 84.5291854856128, 100)[20250519_005109.]: c(0, 1.9918441102757, 1.9163121098627, 0.6667084698175, 2.00955, 2.3927781893359, 0.433189200957997, 2.9708145143872, 0)[20250519_005109.]: c(NA, 15.9347528822056, 7.66524843945081, 1.77788925284667, 4.01909999999999, 3.82844510293744, 0.57758560127733, 3.39521658787109, 0) [20250519_005109.]: Entered 'hyperbolic_regression'-Function [20250519_005109.]: 'hyperbolic_regression': minmax = FALSE [20250519_005109.]: Entered 'cubic_regression'-Function [20250519_005109.]: 'cubic_regression': minmax = FALSE [20250519_005109.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.290936038655697, 11.0412364555656, 25.408146358228, 36.5243683648753, 50.7348788337891, 65.3135169308955, 75.5342663138126, 83.2411177153911, 100)c(0.290936038655697, 1.4587635444344, 0.408146358227999, 0.975631635124699, 0.734878833789097, 2.8135169308955, 0.534266313812594, 4.2588822846089, 0)c(NA, 11.6701083554752, 1.63258543291199, 2.60168436033253, 1.46975766757819, 4.5016270894328, 0.712355085083459, 4.86729403955303, 0) [20250519_005109.]: Logging df_agg: row_means [20250519_005109.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005109.]: c(0.290936038655697, 11.0412364555656, 25.408146358228, 36.5243683648753, 50.7348788337891, 65.3135169308955, 75.5342663138126, 83.2411177153911, 100)[20250519_005109.]: c(0.290936038655697, 1.4587635444344, 0.408146358227999, 0.975631635124699, 0.734878833789097, 2.8135169308955, 0.534266313812594, 4.2588822846089, 0)[20250519_005109.]: c(NA, 11.6701083554752, 1.63258543291199, 2.60168436033253, 1.46975766757819, 4.5016270894328, 0.712355085083459, 4.86729403955303, 0) [20250519_005109.]: Entered 'hyperbolic_regression'-Function [20250519_005109.]: 'hyperbolic_regression': minmax = FALSE [20250519_005109.]: Entered 'cubic_regression'-Function [20250519_005109.]: 'cubic_regression': minmax = FALSE [20250519_005110.]: Entered 'solving_equations'-Function [20250519_005110.]: Solving cubic regression for CpG#1 Coefficients: -1.036Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20250519_005110.]: Samplename: 0 Root: 1.335 --> Root in between the borders! Added to results. Coefficients: -8.34133333333333Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20250519_005110.]: Samplename: 12.5 Root: 11.684 --> Root in between the borders! Added to results. Coefficients: -15.388Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20250519_005110.]: Samplename: 25 Root: 24.056 --> Root in between the borders! Added to results. Coefficients: -24.28Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20250519_005110.]: Samplename: 37.5 Root: 50.441 --> Root in between the borders! Added to results. Coefficients: -34.9005Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20250519_005110.]: Samplename: 50 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -46.354Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20250519_005110.]: Samplename: 62.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -55.893Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20250519_005110.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -63.098Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20250519_005110.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -91.046Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20250519_005110.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20250519_005110.]: Solving cubic regression for CpG#2 Coefficients: -0.283000000000001Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20250519_005110.]: Samplename: 0 Root: 0.548 --> Root in between the borders! Added to results. Coefficients: -6.33966666666667Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20250519_005110.]: Samplename: 12.5 Root: 11.535 --> Root in between the borders! Added to results. Coefficients: -15.939Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20250519_005110.]: Samplename: 25 Root: 26.774 --> Root in between the borders! Added to results. Coefficients: -22.337Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20250519_005110.]: Samplename: 37.5 Root: 35.875 --> Root in between the borders! Added to results. Coefficients: -32.228Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20250519_005110.]: Samplename: 50 Root: 48.74 --> Root in between the borders! Added to results. Coefficients: -49.963Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20250519_005110.]: Samplename: 62.5 Root: 69.156 --> Root in between the borders! Added to results. Coefficients: -58.9655Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20250519_005110.]: Samplename: 75 Root: 78.552 --> Root in between the borders! Added to results. Coefficients: -68.8363333333333Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20250519_005110.]: Samplename: 87.5 Root: 88.272 --> Root in between the borders! Added to results. Coefficients: -90.577Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20250519_005110.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 107.97 --> '100 < root < 110' --> substitute 100 [20250519_005110.]: Solving cubic regression for CpG#3 Coefficients: -0.903Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20250519_005110.]: Samplename: 0 Root: 1.441 --> Root in between the borders! Added to results. Coefficients: -6.573Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20250519_005110.]: Samplename: 12.5 Root: 10.647 --> Root in between the borders! Added to results. Coefficients: -15.429Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20250519_005110.]: Samplename: 25 Root: 25.613 --> Root in between the borders! Added to results. Coefficients: -22.613Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20250519_005110.]: Samplename: 37.5 Root: 38.35 --> Root in between the borders! Added to results. Coefficients: -32.7755Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20250519_005110.]: Samplename: 50 Root: 57.445 --> Root in between the borders! Added to results. Coefficients: -43.889Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20250519_005110.]: Samplename: 62.5 Root: 80.105 --> Root in between the borders! Added to results. Coefficients: -54.9755Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20250519_005110.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 105.145 --> '100 < root < 110' --> substitute 100 Coefficients: -57.6563333333333Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20250519_005110.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -80.665Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20250519_005110.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20250519_005110.]: Solving cubic regression for CpG#4 Coefficients: -0.597Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20250519_005110.]: Samplename: 0 Root: 0.859 --> Root in between the borders! Added to results. Coefficients: -8.25233333333333Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20250519_005110.]: Samplename: 12.5 Root: 12.272 --> Root in between the borders! Added to results. Coefficients: -15.803Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20250519_005110.]: Samplename: 25 Root: 24.378 --> Root in between the borders! Added to results. Coefficients: -25.527Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20250519_005110.]: Samplename: 37.5 Root: 41.587 --> Root in between the borders! Added to results. Coefficients: -33.6365Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20250519_005110.]: Samplename: 50 Root: 57.868 --> Root in between the borders! Added to results. Coefficients: -50.255Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20250519_005110.]: Samplename: 62.5 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.893 --> '100 < root < 110' --> substitute 100 Coefficients: -56.539Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20250519_005110.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -65.5923333333333Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20250519_005110.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -88.321Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20250519_005110.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20250519_005110.]: Solving cubic regression for CpG#5 Coefficients: -0.624000000000001Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20250519_005110.]: Samplename: 0 Root: 1.459 --> Root in between the borders! Added to results. Coefficients: -4.768Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20250519_005110.]: Samplename: 12.5 Root: 10.294 --> Root in between the borders! Added to results. Coefficients: -12.726Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20250519_005110.]: Samplename: 25 Root: 24.477 --> Root in between the borders! Added to results. Coefficients: -21.16Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20250519_005110.]: Samplename: 37.5 Root: 37.097 --> Root in between the borders! Added to results. Coefficients: -30.6955Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20250519_005110.]: Samplename: 50 Root: 49.507 --> Root in between the borders! Added to results. Coefficients: -39.658Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20250519_005110.]: Samplename: 62.5 Root: 59.931 --> Root in between the borders! Added to results. Coefficients: -51.683Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20250519_005110.]: Samplename: 75 Root: 72.563 --> Root in between the borders! Added to results. Coefficients: -61.0146666666667Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20250519_005110.]: Samplename: 87.5 Root: 81.549 --> Root in between the borders! Added to results. Coefficients: -76.07Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20250519_005110.]: Samplename: 100 Root: 94.898 --> Root in between the borders! Added to results. [20250519_005110.]: Solving cubic regression for CpG#6 Coefficients: -0.196000000000001Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20250519_005110.]: Samplename: 0 Root: 0.349 --> Root in between the borders! Added to results. Coefficients: -6.73866666666667Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20250519_005110.]: Samplename: 12.5 Root: 11.765 --> Root in between the borders! Added to results. Coefficients: -15.888Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20250519_005110.]: Samplename: 25 Root: 27.02 --> Root in between the borders! Added to results. Coefficients: -22Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20250519_005110.]: Samplename: 37.5 Root: 36.802 --> Root in between the borders! Added to results. Coefficients: -33.4445Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20250519_005110.]: Samplename: 50 Root: 54.35 --> Root in between the borders! Added to results. Coefficients: -46.9Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20250519_005110.]: Samplename: 62.5 Root: 73.873 --> Root in between the borders! Added to results. Coefficients: -55.832Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20250519_005110.]: Samplename: 75 Root: 86.259 --> Root in between the borders! Added to results. Coefficients: -71.5453333333333Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20250519_005110.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 107.089 --> '100 < root < 110' --> substitute 100 Coefficients: -89.656Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20250519_005110.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20250519_005110.]: Solving cubic regression for CpG#7 Coefficients: -1.215Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20250519_005110.]: Samplename: 0 Root: 2.126 --> Root in between the borders! Added to results. Coefficients: -5.39566666666667Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20250519_005110.]: Samplename: 12.5 Root: 9.991 --> Root in between the borders! Added to results. Coefficients: -11.265Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20250519_005110.]: Samplename: 25 Root: 23.104 --> Root in between the borders! Added to results. Coefficients: -17.451Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20250519_005110.]: Samplename: 37.5 Root: 42.609 --> Root in between the borders! Added to results. Coefficients: -26.0315Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20250519_005110.]: Samplename: 50 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -33.965Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20250519_005110.]: Samplename: 62.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -41.169Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20250519_005110.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -44.1356666666667Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20250519_005110.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -67.223Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20250519_005110.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20250519_005110.]: Solving cubic regression for CpG#8 Coefficients: -1.096Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20250519_005110.]: Samplename: 0 Root: 2.018 --> Root in between the borders! Added to results. Coefficients: -5.44666666666667Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20250519_005110.]: Samplename: 12.5 Root: 9.505 --> Root in between the borders! Added to results. Coefficients: -16.93Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20250519_005110.]: Samplename: 25 Root: 26.424 --> Root in between the borders! Added to results. Coefficients: -23.35Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20250519_005110.]: Samplename: 37.5 Root: 34.662 --> Root in between the borders! Added to results. Coefficients: -37.625Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20250519_005110.]: Samplename: 50 Root: 50.932 --> Root in between the borders! Added to results. Coefficients: -48.826Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20250519_005110.]: Samplename: 62.5 Root: 62.271 --> Root in between the borders! Added to results. Coefficients: -61.6675Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20250519_005110.]: Samplename: 75 Root: 74.154 --> Root in between the borders! Added to results. Coefficients: -64.51Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20250519_005110.]: Samplename: 87.5 Root: 76.651 --> Root in between the borders! Added to results. Coefficients: -86.06Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20250519_005110.]: Samplename: 100 Root: 94.331 --> Root in between the borders! Added to results. [20250519_005110.]: Solving cubic regression for CpG#9 Coefficients: -0.99Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20250519_005110.]: Samplename: 0 Root: 1.474 --> Root in between the borders! Added to results. Coefficients: -6.396Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20250519_005110.]: Samplename: 12.5 Root: 10.183 --> Root in between the borders! Added to results. Coefficients: -14.706Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20250519_005110.]: Samplename: 25 Root: 27.044 --> Root in between the borders! Added to results. Coefficients: -20.624Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20250519_005110.]: Samplename: 37.5 Root: 45.863 --> Root in between the borders! Added to results. Coefficients: -31.396Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20250519_005110.]: Samplename: 50 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -42.686Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20250519_005110.]: Samplename: 62.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -52.9035Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20250519_005110.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -65.4926666666667Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20250519_005110.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -92.99Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20250519_005110.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20250519_005110.]: Solving cubic regression for row_means Coefficients: -0.771111111111111Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20250519_005110.]: Samplename: 0 Root: 1.288 --> Root in between the borders! Added to results. Coefficients: -6.47237037037037Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20250519_005110.]: Samplename: 12.5 Root: 10.989 --> Root in between the borders! Added to results. Coefficients: -14.8971111111111Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20250519_005110.]: Samplename: 25 Root: 25.951 --> Root in between the borders! Added to results. Coefficients: -22.1491111111111Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20250519_005110.]: Samplename: 37.5 Root: 39.518 --> Root in between the borders! Added to results. Coefficients: -32.5258888888889Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20250519_005110.]: Samplename: 50 Root: 60.262 --> Root in between the borders! Added to results. Coefficients: -44.7217777777778Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20250519_005110.]: Samplename: 62.5 Root: 87.213 --> Root in between the borders! Added to results. Coefficients: -54.4031111111111Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20250519_005110.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -62.4312592592593Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20250519_005110.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -84.7342222222222Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20250519_005110.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20250519_005110.]: ### Starting with regression calculations ### [20250519_005110.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_005110.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5)c(1.3350694679926, 11.6842678967181, 24.056490215457, 50.4411280642076)c(1.3350694679926, 0.815732103281899, 0.943509784543, 12.9411280642076)c(NA, 6.52585682625519, 3.774039138172, 34.5096748378869) [20250519_005110.]: Logging df_agg: CpG#1 [20250519_005110.]: c(0, 12.5, 25, 37.5)[20250519_005110.]: c(1.3350694679926, 11.6842678967181, 24.056490215457, 50.4411280642076)[20250519_005110.]: c(1.3350694679926, 0.815732103281899, 0.943509784543, 12.9411280642076)[20250519_005110.]: c(NA, 6.52585682625519, 3.774039138172, 34.5096748378869) [20250519_005110.]: Entered 'hyperbolic_regression'-Function [20250519_005110.]: 'hyperbolic_regression': minmax = FALSE [20250519_005111.]: Entered 'cubic_regression'-Function [20250519_005111.]: 'cubic_regression': minmax = FALSE [20250519_005111.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.547766708541827, 11.5349537694669, 26.7737761718445, 35.875449331591, 48.7401699196391, 69.1560032174257, 78.551969470104, 88.2723976336729, 100)c(0.547766708541827, 0.965046230533099, 1.7737761718445, 1.624550668409, 1.2598300803609, 6.65600321742571, 3.551969470104, 0.772397633672895, 0)c(NA, 7.72036984426479, 7.095104687378, 4.33213511575733, 2.5196601607218, 10.6496051478811, 4.735959293472, 0.882740152769023, 0) [20250519_005111.]: Logging df_agg: CpG#2 [20250519_005111.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005111.]: c(0.547766708541827, 11.5349537694669, 26.7737761718445, 35.875449331591, 48.7401699196391, 69.1560032174257, 78.551969470104, 88.2723976336729, 100)[20250519_005111.]: c(0.547766708541827, 0.965046230533099, 1.7737761718445, 1.624550668409, 1.2598300803609, 6.65600321742571, 3.551969470104, 0.772397633672895, 0)[20250519_005111.]: c(NA, 7.72036984426479, 7.095104687378, 4.33213511575733, 2.5196601607218, 10.6496051478811, 4.735959293472, 0.882740152769023, 0) [20250519_005111.]: Entered 'hyperbolic_regression'-Function [20250519_005111.]: 'hyperbolic_regression': minmax = FALSE [20250519_005111.]: Entered 'cubic_regression'-Function [20250519_005111.]: 'cubic_regression': minmax = FALSE [20250519_005111.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75)c(1.44120553086336, 10.6470702300701, 25.6131070939596, 38.3498594232174, 57.444937683935, 80.104724288839, 100)c(1.44120553086336, 1.8529297699299, 0.613107093959599, 0.849859423217403, 7.444937683935, 17.604724288839, 25)c(NA, 14.8234381594392, 2.45242837583839, 2.26629179524641, 14.88987536787, 28.1675588621424, 33.3333333333333) [20250519_005111.]: Logging df_agg: CpG#3 [20250519_005111.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75)[20250519_005111.]: c(1.44120553086336, 10.6470702300701, 25.6131070939596, 38.3498594232174, 57.444937683935, 80.104724288839, 100)[20250519_005111.]: c(1.44120553086336, 1.8529297699299, 0.613107093959599, 0.849859423217403, 7.444937683935, 17.604724288839, 25)[20250519_005111.]: c(NA, 14.8234381594392, 2.45242837583839, 2.26629179524641, 14.88987536787, 28.1675588621424, 33.3333333333333) [20250519_005111.]: Entered 'hyperbolic_regression'-Function [20250519_005111.]: 'hyperbolic_regression': minmax = FALSE [20250519_005111.]: Entered 'cubic_regression'-Function [20250519_005111.]: 'cubic_regression': minmax = FALSE [20250519_005111.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5)c(0.858643525693588, 12.2719279243844, 24.3781797668421, 41.5866572861968, 57.86781708526, 100)c(0.858643525693588, 0.2280720756156, 0.621820233157901, 4.0866572861968, 7.86781708526, 37.5)c(NA, 1.8245766049248, 2.48728093263161, 10.8977527631915, 15.73563417052, 60) [20250519_005111.]: Logging df_agg: CpG#4 [20250519_005111.]: c(0, 12.5, 25, 37.5, 50, 62.5)[20250519_005111.]: c(0.858643525693588, 12.2719279243844, 24.3781797668421, 41.5866572861968, 57.86781708526, 100)[20250519_005111.]: c(0.858643525693588, 0.2280720756156, 0.621820233157901, 4.0866572861968, 7.86781708526, 37.5)[20250519_005111.]: c(NA, 1.8245766049248, 2.48728093263161, 10.8977527631915, 15.73563417052, 60) [20250519_005111.]: Entered 'hyperbolic_regression'-Function [20250519_005111.]: 'hyperbolic_regression': minmax = FALSE [20250519_005111.]: Entered 'cubic_regression'-Function [20250519_005111.]: 'cubic_regression': minmax = FALSE [20250519_005111.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.45850947960108, 10.2941313684311, 24.4773429557174, 37.0974401416089, 49.5067234953282, 59.9312428933937, 72.5628584782902, 81.5490289118528, 94.898442253889)c(1.45850947960108, 2.2058686315689, 0.5226570442826, 0.402559858391101, 0.493276504671798, 2.5687571066063, 2.4371415217098, 5.9509710881472, 5.101557746111)c(NA, 17.6469490525512, 2.0906281771304, 1.0734929557096, 0.986553009343595, 4.11001137057008, 3.24952202894641, 6.80110981502537, 5.101557746111) [20250519_005111.]: Logging df_agg: CpG#5 [20250519_005111.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005111.]: c(1.45850947960108, 10.2941313684311, 24.4773429557174, 37.0974401416089, 49.5067234953282, 59.9312428933937, 72.5628584782902, 81.5490289118528, 94.898442253889)[20250519_005111.]: c(1.45850947960108, 2.2058686315689, 0.5226570442826, 0.402559858391101, 0.493276504671798, 2.5687571066063, 2.4371415217098, 5.9509710881472, 5.101557746111)[20250519_005111.]: c(NA, 17.6469490525512, 2.0906281771304, 1.0734929557096, 0.986553009343595, 4.11001137057008, 3.24952202894641, 6.80110981502537, 5.101557746111) [20250519_005111.]: Entered 'hyperbolic_regression'-Function [20250519_005111.]: 'hyperbolic_regression': minmax = FALSE [20250519_005112.]: Entered 'cubic_regression'-Function [20250519_005112.]: 'cubic_regression': minmax = FALSE [20250519_005112.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)c(0.349158802372062, 11.7651478823068, 27.019511576095, 36.8015127603397, 54.3503397041729, 73.8730379134395, 86.2591171327579, 100)c(0.349158802372062, 0.734852117693199, 2.019511576095, 0.698487239660302, 4.3503397041729, 11.3730379134395, 11.2591171327579, 12.5)c(NA, 5.87881694154559, 8.07804630438, 1.86263263909414, 8.70067940834581, 18.1968606615032, 15.0121561770105, 14.2857142857143) [20250519_005112.]: Logging df_agg: CpG#6 [20250519_005112.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)[20250519_005112.]: c(0.349158802372062, 11.7651478823068, 27.019511576095, 36.8015127603397, 54.3503397041729, 73.8730379134395, 86.2591171327579, 100)[20250519_005112.]: c(0.349158802372062, 0.734852117693199, 2.019511576095, 0.698487239660302, 4.3503397041729, 11.3730379134395, 11.2591171327579, 12.5)[20250519_005112.]: c(NA, 5.87881694154559, 8.07804630438, 1.86263263909414, 8.70067940834581, 18.1968606615032, 15.0121561770105, 14.2857142857143) [20250519_005112.]: Entered 'hyperbolic_regression'-Function [20250519_005112.]: 'hyperbolic_regression': minmax = FALSE [20250519_005112.]: Entered 'cubic_regression'-Function [20250519_005112.]: 'cubic_regression': minmax = FALSE [20250519_005112.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5)c(2.12599911899281, 9.99133393413891, 23.1036438590861, 42.6085296447297)c(2.12599911899281, 2.50866606586109, 1.8963561409139, 5.1085296447297)c(NA, 20.0693285268887, 7.58542456365559, 13.6227457192792) [20250519_005112.]: Logging df_agg: CpG#7 [20250519_005112.]: c(0, 12.5, 25, 37.5)[20250519_005112.]: c(2.12599911899281, 9.99133393413891, 23.1036438590861, 42.6085296447297)[20250519_005112.]: c(2.12599911899281, 2.50866606586109, 1.8963561409139, 5.1085296447297)[20250519_005112.]: c(NA, 20.0693285268887, 7.58542456365559, 13.6227457192792) [20250519_005112.]: Entered 'hyperbolic_regression'-Function [20250519_005112.]: 'hyperbolic_regression': minmax = FALSE [20250519_005112.]: Entered 'cubic_regression'-Function [20250519_005112.]: 'cubic_regression': minmax = FALSE [20250519_005112.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.0181464298155, 9.50518258466437, 26.4243334649289, 34.6620160335629, 50.9321119457565, 62.2712954366094, 74.1538645100711, 76.6511844577497, 94.3306625091067)c(2.0181464298155, 2.99481741533563, 1.4243334649289, 2.8379839664371, 0.932111945756503, 0.2287045633906, 0.846135489928898, 10.8488155422503, 5.6693374908933)c(NA, 23.958539322685, 5.6973338597156, 7.56795724383227, 1.86422389151301, 0.365927301424961, 1.12818065323853, 12.3986463340004, 5.6693374908933) [20250519_005112.]: Logging df_agg: CpG#8 [20250519_005112.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005112.]: c(2.0181464298155, 9.50518258466437, 26.4243334649289, 34.6620160335629, 50.9321119457565, 62.2712954366094, 74.1538645100711, 76.6511844577497, 94.3306625091067)[20250519_005112.]: c(2.0181464298155, 2.99481741533563, 1.4243334649289, 2.8379839664371, 0.932111945756503, 0.2287045633906, 0.846135489928898, 10.8488155422503, 5.6693374908933)[20250519_005112.]: c(NA, 23.958539322685, 5.6973338597156, 7.56795724383227, 1.86422389151301, 0.365927301424961, 1.12818065323853, 12.3986463340004, 5.6693374908933) [20250519_005112.]: Entered 'hyperbolic_regression'-Function [20250519_005112.]: 'hyperbolic_regression': minmax = FALSE [20250519_005112.]: Entered 'cubic_regression'-Function [20250519_005112.]: 'cubic_regression': minmax = FALSE [20250519_005112.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5)c(1.47402616445883, 10.1833646164297, 27.0440334834678, 45.8634394698265)c(1.47402616445883, 2.3166353835703, 2.0440334834678, 8.3634394698265)c(NA, 18.5330830685624, 8.1761339338712, 22.3025052528707) [20250519_005112.]: Logging df_agg: CpG#9 [20250519_005112.]: c(0, 12.5, 25, 37.5)[20250519_005112.]: c(1.47402616445883, 10.1833646164297, 27.0440334834678, 45.8634394698265)[20250519_005112.]: c(1.47402616445883, 2.3166353835703, 2.0440334834678, 8.3634394698265)[20250519_005112.]: c(NA, 18.5330830685624, 8.1761339338712, 22.3025052528707) [20250519_005112.]: Entered 'hyperbolic_regression'-Function [20250519_005112.]: 'hyperbolic_regression': minmax = FALSE [20250519_005112.]: Entered 'cubic_regression'-Function [20250519_005112.]: 'cubic_regression': minmax = FALSE [20250519_005112.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5)c(1.28794987665983, 10.9885302801468, 25.950937077156, 39.5179682033924, 60.2624119769469, 87.2132000752344)c(1.28794987665983, 1.5114697198532, 0.950937077155999, 2.0179682033924, 10.2624119769469, 24.7132000752344)c(NA, 12.0917577588256, 3.80374830862399, 5.38124854237973, 20.5248239538938, 39.541120120375) [20250519_005112.]: Logging df_agg: row_means [20250519_005112.]: c(0, 12.5, 25, 37.5, 50, 62.5)[20250519_005112.]: c(1.28794987665983, 10.9885302801468, 25.950937077156, 39.5179682033924, 60.2624119769469, 87.2132000752344)[20250519_005112.]: c(1.28794987665983, 1.5114697198532, 0.950937077155999, 2.0179682033924, 10.2624119769469, 24.7132000752344)[20250519_005112.]: c(NA, 12.0917577588256, 3.80374830862399, 5.38124854237973, 20.5248239538938, 39.541120120375) [20250519_005112.]: Entered 'hyperbolic_regression'-Function [20250519_005112.]: 'hyperbolic_regression': minmax = FALSE [20250519_005113.]: Entered 'cubic_regression'-Function [20250519_005113.]: 'cubic_regression': minmax = FALSE [20250519_005113.]: Entered 'solving_equations'-Function [20250519_005113.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 79.8672902891399 [20250519_005113.]: Samplename: Sample#1 Root: 79.867 --> Root in between the borders! Added to results. Hyperbolic solved: 29.789995526108 [20250519_005113.]: Samplename: Sample#10 Root: 29.79 --> Root in between the borders! Added to results. Hyperbolic solved: 41.6525103283956 [20250519_005113.]: Samplename: Sample#2 Root: 41.653 --> Root in between the borders! Added to results. Hyperbolic solved: 57.4651672980431 [20250519_005113.]: Samplename: Sample#3 Root: 57.465 --> Root in between the borders! Added to results. Hyperbolic solved: 9.20070553821487 [20250519_005113.]: Samplename: Sample#4 Root: 9.201 --> Root in between the borders! Added to results. Hyperbolic solved: 21.8059429711637 [20250519_005113.]: Samplename: Sample#5 Root: 21.806 --> Root in between the borders! Added to results. Hyperbolic solved: 23.0837787089758 [20250519_005113.]: Samplename: Sample#6 Root: 23.084 --> Root in between the borders! Added to results. Hyperbolic solved: 45.5033907021902 [20250519_005113.]: Samplename: Sample#7 Root: 45.503 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6987316769908 [20250519_005113.]: Samplename: Sample#8 Root: 85.699 --> Root in between the borders! Added to results. Hyperbolic solved: -3.66512538562846 [20250519_005113.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -3.665 --> '-10 < root < 0' --> substitute 0 [20250519_005113.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 76.7230240612756 [20250519_005113.]: Samplename: Sample#1 Root: 76.723 --> Root in between the borders! Added to results. Hyperbolic solved: 30.992543297941 [20250519_005113.]: Samplename: Sample#10 Root: 30.993 --> Root in between the borders! Added to results. Hyperbolic solved: 42.6812361010669 [20250519_005113.]: Samplename: Sample#2 Root: 42.681 --> Root in between the borders! Added to results. Hyperbolic solved: 58.933980239834 [20250519_005113.]: Samplename: Sample#3 Root: 58.934 --> Root in between the borders! Added to results. Hyperbolic solved: 4.98024265777963 [20250519_005113.]: Samplename: Sample#4 Root: 4.98 --> Root in between the borders! Added to results. Hyperbolic solved: 20.2481111308688 [20250519_005113.]: Samplename: Sample#5 Root: 20.248 --> Root in between the borders! Added to results. Hyperbolic solved: 17.813386088098 [20250519_005113.]: Samplename: Sample#6 Root: 17.813 --> Root in between the borders! Added to results. Hyperbolic solved: 40.9178176381316 [20250519_005113.]: Samplename: Sample#7 Root: 40.918 --> Root in between the borders! Added to results. Hyperbolic solved: 86.0453226524415 [20250519_005113.]: Samplename: Sample#8 Root: 86.045 --> Root in between the borders! Added to results. Hyperbolic solved: 3.11582988095862 [20250519_005113.]: Samplename: Sample#9 Root: 3.116 --> Root in between the borders! Added to results. [20250519_005113.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 74.934721803959 [20250519_005113.]: Samplename: Sample#1 Root: 74.935 --> Root in between the borders! Added to results. Hyperbolic solved: 27.6843475257834 [20250519_005113.]: Samplename: Sample#10 Root: 27.684 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8518905782652 [20250519_005113.]: Samplename: Sample#2 Root: 41.852 --> Root in between the borders! Added to results. Hyperbolic solved: 55.8323554649537 [20250519_005113.]: Samplename: Sample#3 Root: 55.832 --> Root in between the borders! Added to results. Hyperbolic solved: 8.03516016616019 [20250519_005113.]: Samplename: Sample#4 Root: 8.035 --> Root in between the borders! Added to results. Hyperbolic solved: 24.1065510576898 [20250519_005113.]: Samplename: Sample#5 Root: 24.107 --> Root in between the borders! Added to results. Hyperbolic solved: 26.2418954231184 [20250519_005113.]: Samplename: Sample#6 Root: 26.242 --> Root in between the borders! Added to results. Hyperbolic solved: 44.0943580511304 [20250519_005113.]: Samplename: Sample#7 Root: 44.094 --> Root in between the borders! Added to results. Hyperbolic solved: 85.8277140531902 [20250519_005113.]: Samplename: Sample#8 Root: 85.828 --> Root in between the borders! Added to results. Hyperbolic solved: -0.666486811581751 [20250519_005113.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.666 --> '-10 < root < 0' --> substitute 0 [20250519_005113.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 76.349372671628 [20250519_005113.]: Samplename: Sample#1 Root: 76.349 --> Root in between the borders! Added to results. Hyperbolic solved: 28.2567843724913 [20250519_005113.]: Samplename: Sample#10 Root: 28.257 --> Root in between the borders! Added to results. Hyperbolic solved: 43.4088841408467 [20250519_005113.]: Samplename: Sample#2 Root: 43.409 --> Root in between the borders! Added to results. Hyperbolic solved: 58.5433972042602 [20250519_005113.]: Samplename: Sample#3 Root: 58.543 --> Root in between the borders! Added to results. Hyperbolic solved: 10.3086704325003 [20250519_005113.]: Samplename: Sample#4 Root: 10.309 --> Root in between the borders! Added to results. Hyperbolic solved: 22.182986320858 [20250519_005113.]: Samplename: Sample#5 Root: 22.183 --> Root in between the borders! Added to results. Hyperbolic solved: 27.1337081900258 [20250519_005113.]: Samplename: Sample#6 Root: 27.134 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8320127104856 [20250519_005113.]: Samplename: Sample#7 Root: 41.832 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6888498156134 [20250519_005113.]: Samplename: Sample#8 Root: 85.689 --> Root in between the borders! Added to results. Hyperbolic solved: 2.42230396811441 [20250519_005113.]: Samplename: Sample#9 Root: 2.422 --> Root in between the borders! Added to results. [20250519_005113.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 72.8126507590781 [20250519_005113.]: Samplename: Sample#1 Root: 72.813 --> Root in between the borders! Added to results. Hyperbolic solved: 26.4206774223762 [20250519_005113.]: Samplename: Sample#10 Root: 26.421 --> Root in between the borders! Added to results. Hyperbolic solved: 44.2738516315831 [20250519_005113.]: Samplename: Sample#2 Root: 44.274 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5324088748019 [20250519_005113.]: Samplename: Sample#3 Root: 53.532 --> Root in between the borders! Added to results. Hyperbolic solved: 10.0373628417333 [20250519_005113.]: Samplename: Sample#4 Root: 10.037 --> Root in between the borders! Added to results. Hyperbolic solved: 22.8732969704156 [20250519_005113.]: Samplename: Sample#5 Root: 22.873 --> Root in between the borders! Added to results. Hyperbolic solved: 25.9417756406497 [20250519_005113.]: Samplename: Sample#6 Root: 25.942 --> Root in between the borders! Added to results. Hyperbolic solved: 42.7944833740993 [20250519_005113.]: Samplename: Sample#7 Root: 42.794 --> Root in between the borders! Added to results. Hyperbolic solved: 88.8740708807914 [20250519_005113.]: Samplename: Sample#8 Root: 88.874 --> Root in between the borders! Added to results. Hyperbolic solved: 2.34646946564885 [20250519_005113.]: Samplename: Sample#9 Root: 2.346 --> Root in between the borders! Added to results. [20250519_005113.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 79.2782328649447 [20250519_005113.]: Samplename: Sample#1 Root: 79.278 --> Root in between the borders! Added to results. Hyperbolic solved: 30.2013285011234 [20250519_005113.]: Samplename: Sample#10 Root: 30.201 --> Root in between the borders! Added to results. Hyperbolic solved: 41.847545497608 [20250519_005113.]: Samplename: Sample#2 Root: 41.848 --> Root in between the borders! Added to results. Hyperbolic solved: 56.8424863688491 [20250519_005113.]: Samplename: Sample#3 Root: 56.842 --> Root in between the borders! Added to results. Hyperbolic solved: 8.87859681588532 [20250519_005113.]: Samplename: Sample#4 Root: 8.879 --> Root in between the borders! Added to results. Hyperbolic solved: 18.6902176438696 [20250519_005113.]: Samplename: Sample#5 Root: 18.69 --> Root in between the borders! Added to results. Hyperbolic solved: 29.9310083954849 [20250519_005113.]: Samplename: Sample#6 Root: 29.931 --> Root in between the borders! Added to results. Hyperbolic solved: 42.814964037839 [20250519_005113.]: Samplename: Sample#7 Root: 42.815 --> Root in between the borders! Added to results. Hyperbolic solved: 86.7503685829287 [20250519_005113.]: Samplename: Sample#8 Root: 86.75 --> Root in between the borders! Added to results. Hyperbolic solved: 1.51518138323643 [20250519_005113.]: Samplename: Sample#9 Root: 1.515 --> Root in between the borders! Added to results. [20250519_005113.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 78.2568233815803 [20250519_005113.]: Samplename: Sample#1 Root: 78.257 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4888594923858 [20250519_005113.]: Samplename: Sample#10 Root: 25.489 --> Root in between the borders! Added to results. Hyperbolic solved: 47.3714242177043 [20250519_005113.]: Samplename: Sample#2 Root: 47.371 --> Root in between the borders! Added to results. Hyperbolic solved: 58.3144949237501 [20250519_005113.]: Samplename: Sample#3 Root: 58.314 --> Root in between the borders! Added to results. Hyperbolic solved: 11.7212806729981 [20250519_005113.]: Samplename: Sample#4 Root: 11.721 --> Root in between the borders! Added to results. Hyperbolic solved: 25.3798682873976 [20250519_005113.]: Samplename: Sample#5 Root: 25.38 --> Root in between the borders! Added to results. Hyperbolic solved: 29.4096493828253 [20250519_005113.]: Samplename: Sample#6 Root: 29.41 --> Root in between the borders! Added to results. Hyperbolic solved: 44.5756969430888 [20250519_005113.]: Samplename: Sample#7 Root: 44.576 --> Root in between the borders! Added to results. Hyperbolic solved: 85.9631455147929 [20250519_005113.]: Samplename: Sample#8 Root: 85.963 --> Root in between the borders! Added to results. Hyperbolic solved: -4.16459229634232 [20250519_005113.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -4.165 --> '-10 < root < 0' --> substitute 0 [20250519_005113.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 72.9696678780879 [20250519_005113.]: Samplename: Sample#1 Root: 72.97 --> Root in between the borders! Added to results. Hyperbolic solved: 27.825096438612 [20250519_005113.]: Samplename: Sample#10 Root: 27.825 --> Root in between the borders! Added to results. Hyperbolic solved: 34.9068412412515 [20250519_005113.]: Samplename: Sample#2 Root: 34.907 --> Root in between the borders! Added to results. Hyperbolic solved: 59.1637501357335 [20250519_005113.]: Samplename: Sample#3 Root: 59.164 --> Root in between the borders! Added to results. Hyperbolic solved: 9.6464397671905 [20250519_005113.]: Samplename: Sample#4 Root: 9.646 --> Root in between the borders! Added to results. Hyperbolic solved: 19.3522593577541 [20250519_005113.]: Samplename: Sample#5 Root: 19.352 --> Root in between the borders! Added to results. Hyperbolic solved: 38.5571468548369 [20250519_005113.]: Samplename: Sample#6 Root: 38.557 --> Root in between the borders! Added to results. Hyperbolic solved: 44.775966890921 [20250519_005113.]: Samplename: Sample#7 Root: 44.776 --> Root in between the borders! Added to results. Hyperbolic solved: 84.8599747802978 [20250519_005113.]: Samplename: Sample#8 Root: 84.86 --> Root in between the borders! Added to results. Hyperbolic solved: -2.05045739684141 [20250519_005113.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.05 --> '-10 < root < 0' --> substitute 0 [20250519_005113.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 81.0413625074508 [20250519_005113.]: Samplename: Sample#1 Root: 81.041 --> Root in between the borders! Added to results. Hyperbolic solved: 26.6440232383638 [20250519_005113.]: Samplename: Sample#10 Root: 26.644 --> Root in between the borders! Added to results. Hyperbolic solved: 45.704400902509 [20250519_005113.]: Samplename: Sample#2 Root: 45.704 --> Root in between the borders! Added to results. Hyperbolic solved: 56.9765444860285 [20250519_005113.]: Samplename: Sample#3 Root: 56.977 --> Root in between the borders! Added to results. Hyperbolic solved: 6.81226926030202 [20250519_005113.]: Samplename: Sample#4 Root: 6.812 --> Root in between the borders! Added to results. Hyperbolic solved: 22.9283517775063 [20250519_005113.]: Samplename: Sample#5 Root: 22.928 --> Root in between the borders! Added to results. Hyperbolic solved: 38.8651260750996 [20250519_005113.]: Samplename: Sample#6 Root: 38.865 --> Root in between the borders! Added to results. Hyperbolic solved: 43.6853511825078 [20250519_005113.]: Samplename: Sample#7 Root: 43.685 --> Root in between the borders! Added to results. Hyperbolic solved: 88.0152224353095 [20250519_005113.]: Samplename: Sample#8 Root: 88.015 --> Root in between the borders! Added to results. Hyperbolic solved: -3.35608361520768 [20250519_005113.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -3.356 --> '-10 < root < 0' --> substitute 0 [20250519_005113.]: Solving hyperbolic regression for row_means Hyperbolic solved: 77.069275047056 [20250519_005113.]: Samplename: Sample#1 Root: 77.069 --> Root in between the borders! Added to results. Hyperbolic solved: 28.3620003757916 [20250519_005113.]: Samplename: Sample#10 Root: 28.362 --> Root in between the borders! Added to results. Hyperbolic solved: 42.5026135474906 [20250519_005113.]: Samplename: Sample#2 Root: 42.503 --> Root in between the borders! Added to results. Hyperbolic solved: 57.2972007822813 [20250519_005113.]: Samplename: Sample#3 Root: 57.297 --> Root in between the borders! Added to results. Hyperbolic solved: 8.82703592503651 [20250519_005113.]: Samplename: Sample#4 Root: 8.827 --> Root in between the borders! Added to results. Hyperbolic solved: 21.8102552608375 [20250519_005113.]: Samplename: Sample#5 Root: 21.81 --> Root in between the borders! Added to results. Hyperbolic solved: 28.7228620570755 [20250519_005113.]: Samplename: Sample#6 Root: 28.723 --> Root in between the borders! Added to results. Hyperbolic solved: 43.4105062814289 [20250519_005113.]: Samplename: Sample#7 Root: 43.411 --> Root in between the borders! Added to results. Hyperbolic solved: 86.4143497902066 [20250519_005113.]: Samplename: Sample#8 Root: 86.414 --> Root in between the borders! Added to results. Hyperbolic solved: -0.237025016559672 [20250519_005113.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.237 --> '-10 < root < 0' --> substitute 0 [20250519_005114.]: Entered 'solving_equations'-Function [20250519_005114.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: -2.23222837469517 [20250519_005114.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.232 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.1698391693126 [20250519_005114.]: Samplename: 12.5 Root: 12.17 --> Root in between the borders! Added to results. Hyperbolic solved: 24.4781729791561 [20250519_005114.]: Samplename: 25 Root: 24.478 --> Root in between the borders! Added to results. Hyperbolic solved: 38.1730159074047 [20250519_005114.]: Samplename: 37.5 Root: 38.173 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3348987967717 [20250519_005114.]: Samplename: 50 Root: 52.335 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4582305966058 [20250519_005114.]: Samplename: 62.5 Root: 65.458 --> Root in between the borders! Added to results. Hyperbolic solved: 75.0090269722885 [20250519_005114.]: Samplename: 75 Root: 75.009 --> Root in between the borders! Added to results. Hyperbolic solved: 81.527135738891 [20250519_005114.]: Samplename: 87.5 Root: 81.527 --> Root in between the borders! Added to results. Hyperbolic solved: 102.400825350044 [20250519_005114.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.401 --> '100 < root < 110' --> substitute 100 [20250519_005114.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 1.13663087266051 [20250519_005114.]: Samplename: 0 Root: 1.137 --> Root in between the borders! Added to results. Hyperbolic solved: 11.4130277963576 [20250519_005114.]: Samplename: 12.5 Root: 11.413 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1741025868157 [20250519_005114.]: Samplename: 25 Root: 26.174 --> Root in between the borders! Added to results. Hyperbolic solved: 35.1051721736724 [20250519_005114.]: Samplename: 37.5 Root: 35.105 --> Root in between the borders! Added to results. Hyperbolic solved: 47.6856613240114 [20250519_005114.]: Samplename: 50 Root: 47.686 --> Root in between the borders! Added to results. Hyperbolic solved: 67.1442585354696 [20250519_005114.]: Samplename: 62.5 Root: 67.144 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7646957109439 [20250519_005114.]: Samplename: 75 Root: 75.765 --> Root in between the borders! Added to results. Hyperbolic solved: 84.4056634834815 [20250519_005114.]: Samplename: 87.5 Root: 84.406 --> Root in between the borders! Added to results. Hyperbolic solved: 100.94855337669 [20250519_005114.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.949 --> '100 < root < 110' --> substitute 100 [20250519_005114.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 0.512348257854635 [20250519_005114.]: Samplename: 0 Root: 0.512 --> Root in between the borders! Added to results. Hyperbolic solved: 10.7523476241811 [20250519_005114.]: Samplename: 12.5 Root: 10.752 --> Root in between the borders! Added to results. Hyperbolic solved: 25.5218062516799 [20250519_005114.]: Samplename: 25 Root: 25.522 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5269315729339 [20250519_005114.]: Samplename: 37.5 Root: 36.527 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7907738274884 [20250519_005114.]: Samplename: 50 Root: 50.791 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8684489988589 [20250519_005114.]: Samplename: 62.5 Root: 64.868 --> Root in between the borders! Added to results. Hyperbolic solved: 77.5522101073979 [20250519_005114.]: Samplename: 75 Root: 77.552 --> Root in between the borders! Added to results. Hyperbolic solved: 80.4372474691229 [20250519_005114.]: Samplename: 87.5 Root: 80.437 --> Root in between the borders! Added to results. Hyperbolic solved: 102.703772810881 [20250519_005114.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.704 --> '100 < root < 110' --> substitute 100 [20250519_005114.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: -0.519513573026794 [20250519_005114.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.52 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.4933760051342 [20250519_005114.]: Samplename: 12.5 Root: 12.493 --> Root in between the borders! Added to results. Hyperbolic solved: 24.2684789512555 [20250519_005114.]: Samplename: 25 Root: 24.268 --> Root in between the borders! Added to results. Hyperbolic solved: 38.0816229393195 [20250519_005114.]: Samplename: 37.5 Root: 38.082 --> Root in between the borders! Added to results. Hyperbolic solved: 48.5842089589187 [20250519_005114.]: Samplename: 50 Root: 48.584 --> Root in between the borders! Added to results. Hyperbolic solved: 67.6720983549562 [20250519_005114.]: Samplename: 62.5 Root: 67.672 --> Root in between the borders! Added to results. Hyperbolic solved: 74.1547759694059 [20250519_005114.]: Samplename: 75 Root: 74.155 --> Root in between the borders! Added to results. Hyperbolic solved: 82.8820147905386 [20250519_005114.]: Samplename: 87.5 Root: 82.882 --> Root in between the borders! Added to results. Hyperbolic solved: 102.078935346876 [20250519_005114.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.079 --> '100 < root < 110' --> substitute 100 [20250519_005114.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 2.41558040143479 [20250519_005114.]: Samplename: 0 Root: 2.416 --> Root in between the borders! Added to results. Hyperbolic solved: 10.1649584830834 [20250519_005114.]: Samplename: 12.5 Root: 10.165 --> Root in between the borders! Added to results. Hyperbolic solved: 23.9830670020905 [20250519_005114.]: Samplename: 25 Root: 23.983 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2773406385708 [20250519_005114.]: Samplename: 37.5 Root: 37.277 --> Root in between the borders! Added to results. Hyperbolic solved: 50.8659103346102 [20250519_005114.]: Samplename: 50 Root: 50.866 --> Root in between the borders! Added to results. Hyperbolic solved: 62.4341926937382 [20250519_005114.]: Samplename: 62.5 Root: 62.434 --> Root in between the borders! Added to results. Hyperbolic solved: 76.3914832329149 [20250519_005114.]: Samplename: 75 Root: 76.391 --> Root in between the borders! Added to results. Hyperbolic solved: 86.1597399215782 [20250519_005114.]: Samplename: 87.5 Root: 86.16 --> Root in between the borders! Added to results. Hyperbolic solved: 100.267701841999 [20250519_005114.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.268 --> '100 < root < 110' --> substitute 100 [20250519_005114.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 0.138179963459196 [20250519_005114.]: Samplename: 0 Root: 0.138 --> Root in between the borders! Added to results. Hyperbolic solved: 11.8635989635202 [20250519_005114.]: Samplename: 12.5 Root: 11.864 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5108198460409 [20250519_005114.]: Samplename: 25 Root: 26.511 --> Root in between the borders! Added to results. Hyperbolic solved: 35.3206004021833 [20250519_005114.]: Samplename: 37.5 Root: 35.321 --> Root in between the borders! Added to results. Hyperbolic solved: 50.0571987473308 [20250519_005114.]: Samplename: 50 Root: 50.057 --> Root in between the borders! Added to results. Hyperbolic solved: 64.9604436607063 [20250519_005114.]: Samplename: 62.5 Root: 64.96 --> Root in between the borders! Added to results. Hyperbolic solved: 73.6690699266294 [20250519_005114.]: Samplename: 75 Root: 73.669 --> Root in between the borders! Added to results. Hyperbolic solved: 87.1267946879604 [20250519_005114.]: Samplename: 87.5 Root: 87.127 --> Root in between the borders! Added to results. Hyperbolic solved: 100.261842767865 [20250519_005114.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.262 --> '100 < root < 110' --> substitute 100 [20250519_005114.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: -1.37239249181669 [20250519_005114.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -1.372 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.199366215418 [20250519_005114.]: Samplename: 12.5 Root: 10.199 --> Root in between the borders! Added to results. Hyperbolic solved: 24.595295361327 [20250519_005114.]: Samplename: 25 Root: 24.595 --> Root in between the borders! Added to results. Hyperbolic solved: 37.8312095633391 [20250519_005114.]: Samplename: 37.5 Root: 37.831 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5590896582852 [20250519_005114.]: Samplename: 50 Root: 53.559 --> Root in between the borders! Added to results. Hyperbolic solved: 65.9367389282431 [20250519_005114.]: Samplename: 62.5 Root: 65.937 --> Root in between the borders! Added to results. Hyperbolic solved: 75.736370152841 [20250519_005114.]: Samplename: 75 Root: 75.736 --> Root in between the borders! Added to results. Hyperbolic solved: 79.433089359524 [20250519_005114.]: Samplename: 87.5 Root: 79.433 --> Root in between the borders! Added to results. Hyperbolic solved: 103.004516225662 [20250519_005114.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 103.005 --> '100 < root < 110' --> substitute 100 [20250519_005114.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 2.80067549526643 [20250519_005114.]: Samplename: 0 Root: 2.801 --> Root in between the borders! Added to results. Hyperbolic solved: 9.27534686003087 [20250519_005114.]: Samplename: 12.5 Root: 9.275 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4762624089952 [20250519_005114.]: Samplename: 25 Root: 25.476 --> Root in between the borders! Added to results. Hyperbolic solved: 34.0122098045585 [20250519_005114.]: Samplename: 37.5 Root: 34.012 --> Root in between the borders! Added to results. Hyperbolic solved: 51.784270967861 [20250519_005114.]: Samplename: 50 Root: 51.784 --> Root in between the borders! Added to results. Hyperbolic solved: 64.6732380258789 [20250519_005114.]: Samplename: 62.5 Root: 64.673 --> Root in between the borders! Added to results. Hyperbolic solved: 78.4327054483192 [20250519_005114.]: Samplename: 75 Root: 78.433 --> Root in between the borders! Added to results. Hyperbolic solved: 81.3427308951795 [20250519_005114.]: Samplename: 87.5 Root: 81.343 --> Root in between the borders! Added to results. Hyperbolic solved: 101.964413523995 [20250519_005114.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.964 --> '100 < root < 110' --> substitute 100 [20250519_005114.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: -2.13402763663736 [20250519_005114.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.134 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.5081558897243 [20250519_005114.]: Samplename: 12.5 Root: 10.508 --> Root in between the borders! Added to results. Hyperbolic solved: 26.9163121098627 [20250519_005114.]: Samplename: 25 Root: 26.916 --> Root in between the borders! Added to results. Hyperbolic solved: 36.8332915301825 [20250519_005114.]: Samplename: 37.5 Root: 36.833 --> Root in between the borders! Added to results. Hyperbolic solved: 52.00955 [20250519_005114.]: Samplename: 50 Root: 52.01 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8927781893359 [20250519_005114.]: Samplename: 62.5 Root: 64.893 --> Root in between the borders! Added to results. Hyperbolic solved: 74.566810799042 [20250519_005114.]: Samplename: 75 Root: 74.567 --> Root in between the borders! Added to results. Hyperbolic solved: 84.5291854856128 [20250519_005114.]: Samplename: 87.5 Root: 84.529 --> Root in between the borders! Added to results. Hyperbolic solved: 101.046823491232 [20250519_005114.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.047 --> '100 < root < 110' --> substitute 100 [20250519_005114.]: Solving hyperbolic regression for row_means Hyperbolic solved: 0.290936038655697 [20250519_005114.]: Samplename: 0 Root: 0.291 --> Root in between the borders! Added to results. Hyperbolic solved: 11.0412364555656 [20250519_005114.]: Samplename: 12.5 Root: 11.041 --> Root in between the borders! Added to results. Hyperbolic solved: 25.408146358228 [20250519_005114.]: Samplename: 25 Root: 25.408 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5243683648753 [20250519_005114.]: Samplename: 37.5 Root: 36.524 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7348788337891 [20250519_005114.]: Samplename: 50 Root: 50.735 --> Root in between the borders! Added to results. Hyperbolic solved: 65.3135169308955 [20250519_005114.]: Samplename: 62.5 Root: 65.314 --> Root in between the borders! Added to results. Hyperbolic solved: 75.5342663138126 [20250519_005114.]: Samplename: 75 Root: 75.534 --> Root in between the borders! Added to results. Hyperbolic solved: 83.2411177153911 [20250519_005114.]: Samplename: 87.5 Root: 83.241 --> Root in between the borders! Added to results. Hyperbolic solved: 101.666935931185 [20250519_005114.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.667 --> '100 < root < 110' --> substitute 100 [20250519_005114.]: Entered 'clean_dt'-Function [20250519_005114.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_005114.]: got experimental data [20250519_005114.]: Entered 'clean_dt'-Function [20250519_005114.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_005114.]: got calibration data [20250519_005114.]: ### Starting with regression calculations ### [20250519_005114.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_005114.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005114.]: Logging df_agg: CpG#1 [20250519_005114.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005114.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005114.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005114.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005114.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005114.]: Entered 'hyperbolic_regression'-Function [20250519_005114.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005114.]: Entered 'cubic_regression'-Function [20250519_005114.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005114.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005114.]: Logging df_agg: CpG#2 [20250519_005114.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005114.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005114.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005114.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005114.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005114.]: Entered 'hyperbolic_regression'-Function [20250519_005115.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005115.]: Entered 'cubic_regression'-Function [20250519_005115.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005115.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005115.]: Logging df_agg: CpG#3 [20250519_005115.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005115.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005115.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005115.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005115.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005115.]: Entered 'hyperbolic_regression'-Function [20250519_005115.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005115.]: Entered 'cubic_regression'-Function [20250519_005115.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005115.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005115.]: Logging df_agg: CpG#4 [20250519_005115.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005115.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005115.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005115.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005115.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005115.]: Entered 'hyperbolic_regression'-Function [20250519_005115.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005116.]: Entered 'cubic_regression'-Function [20250519_005116.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005116.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005116.]: Logging df_agg: CpG#5 [20250519_005116.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005116.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005116.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005116.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005116.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005116.]: Entered 'hyperbolic_regression'-Function [20250519_005116.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005116.]: Entered 'cubic_regression'-Function [20250519_005116.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005116.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005116.]: Logging df_agg: CpG#6 [20250519_005116.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005116.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005116.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005116.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005116.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005116.]: Entered 'hyperbolic_regression'-Function [20250519_005116.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005116.]: Entered 'cubic_regression'-Function [20250519_005116.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005116.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005116.]: Logging df_agg: CpG#7 [20250519_005116.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005116.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005116.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005116.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005116.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005116.]: Entered 'hyperbolic_regression'-Function [20250519_005116.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005117.]: Entered 'cubic_regression'-Function [20250519_005117.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005117.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005117.]: Logging df_agg: CpG#8 [20250519_005117.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005117.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005117.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005117.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005117.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005117.]: Entered 'hyperbolic_regression'-Function [20250519_005117.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005117.]: Entered 'cubic_regression'-Function [20250519_005117.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005117.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005117.]: Logging df_agg: CpG#9 [20250519_005117.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005117.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005117.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005117.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005117.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005117.]: Entered 'hyperbolic_regression'-Function [20250519_005117.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005117.]: Entered 'cubic_regression'-Function [20250519_005117.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005118.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005118.]: Logging df_agg: row_means [20250519_005118.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005118.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005118.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_005118.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005118.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005118.]: Entered 'hyperbolic_regression'-Function [20250519_005118.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005118.]: Entered 'cubic_regression'-Function [20250519_005118.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005119.]: ### Starting with regression calculations ### [20250519_005119.]: Entered 'regression_type1'-Function [20250519_005119.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005119.]: Logging df_agg: CpG#1 [20250519_005119.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005119.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005119.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005119.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005119.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005119.]: Entered 'hyperbolic_regression'-Function [20250519_005119.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005119.]: Entered 'cubic_regression'-Function [20250519_005119.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005119.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005119.]: Logging df_agg: CpG#2 [20250519_005119.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005119.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005119.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005119.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005119.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005119.]: Entered 'hyperbolic_regression'-Function [20250519_005119.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005119.]: Entered 'cubic_regression'-Function [20250519_005119.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005119.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005119.]: Logging df_agg: CpG#3 [20250519_005119.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005119.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005119.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005119.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005119.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005119.]: Entered 'hyperbolic_regression'-Function [20250519_005119.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005120.]: Entered 'cubic_regression'-Function [20250519_005120.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005120.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005120.]: Logging df_agg: CpG#4 [20250519_005120.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005120.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005120.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005120.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005120.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005120.]: Entered 'hyperbolic_regression'-Function [20250519_005120.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005120.]: Entered 'cubic_regression'-Function [20250519_005120.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005120.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005120.]: Logging df_agg: CpG#5 [20250519_005120.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005120.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005120.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005120.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005120.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005120.]: Entered 'hyperbolic_regression'-Function [20250519_005120.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005120.]: Entered 'cubic_regression'-Function [20250519_005120.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005121.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005121.]: Logging df_agg: CpG#6 [20250519_005121.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005121.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005121.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005121.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005121.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005121.]: Entered 'hyperbolic_regression'-Function [20250519_005121.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005121.]: Entered 'cubic_regression'-Function [20250519_005121.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005121.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005121.]: Logging df_agg: CpG#7 [20250519_005121.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005121.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005121.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005121.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005121.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005121.]: Entered 'hyperbolic_regression'-Function [20250519_005121.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005121.]: Entered 'cubic_regression'-Function [20250519_005121.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005121.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005121.]: Logging df_agg: CpG#8 [20250519_005121.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005121.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005121.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005121.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005121.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005121.]: Entered 'hyperbolic_regression'-Function [20250519_005121.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005122.]: Entered 'cubic_regression'-Function [20250519_005122.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005122.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005122.]: Logging df_agg: CpG#9 [20250519_005122.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005122.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005122.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005122.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005122.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005122.]: Entered 'hyperbolic_regression'-Function [20250519_005122.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005122.]: Entered 'cubic_regression'-Function [20250519_005122.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005122.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005122.]: Logging df_agg: row_means [20250519_005122.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005122.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005122.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_005122.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005122.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005122.]: Entered 'hyperbolic_regression'-Function [20250519_005122.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005122.]: Entered 'cubic_regression'-Function [20250519_005122.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005123.]: ### Starting with regression calculations ### [20250519_005123.]: Entered 'regression_type1'-Function [20250519_005124.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005124.]: Logging df_agg: CpG#1 [20250519_005124.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005124.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005124.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005124.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005124.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005124.]: Entered 'hyperbolic_regression'-Function [20250519_005124.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005124.]: Entered 'cubic_regression'-Function [20250519_005124.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005124.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005124.]: Logging df_agg: CpG#2 [20250519_005124.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005124.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005124.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005124.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005124.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005124.]: Entered 'hyperbolic_regression'-Function [20250519_005124.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005124.]: Entered 'cubic_regression'-Function [20250519_005124.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005124.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005124.]: Logging df_agg: CpG#3 [20250519_005124.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005124.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005124.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005124.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005124.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005124.]: Entered 'hyperbolic_regression'-Function [20250519_005124.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005124.]: Entered 'cubic_regression'-Function [20250519_005124.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005124.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005124.]: Logging df_agg: CpG#4 [20250519_005124.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005124.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005124.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005124.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005124.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005124.]: Entered 'hyperbolic_regression'-Function [20250519_005124.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005124.]: Entered 'cubic_regression'-Function [20250519_005124.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005124.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005124.]: Logging df_agg: CpG#5 [20250519_005124.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005124.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005124.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005124.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005124.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005124.]: Entered 'hyperbolic_regression'-Function [20250519_005124.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005124.]: Entered 'cubic_regression'-Function [20250519_005124.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005124.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005124.]: Logging df_agg: CpG#6 [20250519_005124.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005124.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005124.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005124.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005124.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005124.]: Entered 'hyperbolic_regression'-Function [20250519_005124.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005124.]: Entered 'cubic_regression'-Function [20250519_005124.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005124.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005124.]: Logging df_agg: CpG#7 [20250519_005124.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005124.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005124.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005124.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005124.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005124.]: Entered 'hyperbolic_regression'-Function [20250519_005124.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005124.]: Entered 'cubic_regression'-Function [20250519_005124.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005124.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005124.]: Logging df_agg: CpG#8 [20250519_005124.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005124.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005124.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005124.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005124.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005124.]: Entered 'hyperbolic_regression'-Function [20250519_005124.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005124.]: Entered 'cubic_regression'-Function [20250519_005124.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005124.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005124.]: Logging df_agg: CpG#9 [20250519_005124.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005124.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005124.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005124.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005124.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005124.]: Entered 'hyperbolic_regression'-Function [20250519_005124.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005124.]: Entered 'cubic_regression'-Function [20250519_005124.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005124.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005124.]: Logging df_agg: row_means [20250519_005124.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005124.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005124.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_005124.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005124.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005124.]: Entered 'hyperbolic_regression'-Function [20250519_005124.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005124.]: Entered 'cubic_regression'-Function [20250519_005124.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005125.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_005125.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005125.]: Logging df_agg: CpG#1 [20250519_005125.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005125.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005125.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005125.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005125.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005125.]: Entered 'hyperbolic_regression'-Function [20250519_005125.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005126.]: Entered 'cubic_regression'-Function [20250519_005126.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005126.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005126.]: Logging df_agg: CpG#2 [20250519_005126.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005126.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005126.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005126.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005126.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005126.]: Entered 'hyperbolic_regression'-Function [20250519_005126.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005126.]: Entered 'cubic_regression'-Function [20250519_005126.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005126.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005126.]: Logging df_agg: CpG#3 [20250519_005126.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005126.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005126.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005126.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005126.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005126.]: Entered 'hyperbolic_regression'-Function [20250519_005126.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005127.]: Entered 'cubic_regression'-Function [20250519_005127.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005127.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005127.]: Logging df_agg: CpG#4 [20250519_005127.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005127.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005127.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005127.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005127.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005127.]: Entered 'hyperbolic_regression'-Function [20250519_005127.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005127.]: Entered 'cubic_regression'-Function [20250519_005127.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005127.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005127.]: Logging df_agg: CpG#5 [20250519_005127.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005127.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005127.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005127.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005127.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005127.]: Entered 'hyperbolic_regression'-Function [20250519_005127.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005127.]: Entered 'cubic_regression'-Function [20250519_005127.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005127.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005127.]: Logging df_agg: CpG#6 [20250519_005127.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005127.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005127.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005127.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005127.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005127.]: Entered 'hyperbolic_regression'-Function [20250519_005127.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005128.]: Entered 'cubic_regression'-Function [20250519_005128.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005128.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005128.]: Logging df_agg: CpG#7 [20250519_005128.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005128.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005128.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005128.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005128.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005128.]: Entered 'hyperbolic_regression'-Function [20250519_005128.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005128.]: Entered 'cubic_regression'-Function [20250519_005128.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005128.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005128.]: Logging df_agg: CpG#8 [20250519_005128.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005128.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005128.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005128.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005128.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005128.]: Entered 'hyperbolic_regression'-Function [20250519_005128.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005128.]: Entered 'cubic_regression'-Function [20250519_005128.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005128.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005129.]: Logging df_agg: CpG#9 [20250519_005129.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005129.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005129.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005129.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005129.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005129.]: Entered 'hyperbolic_regression'-Function [20250519_005129.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005129.]: Entered 'cubic_regression'-Function [20250519_005129.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005129.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005129.]: Logging df_agg: row_means [20250519_005129.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005129.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005129.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_005129.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005129.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005129.]: Entered 'hyperbolic_regression'-Function [20250519_005129.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005129.]: Entered 'cubic_regression'-Function [20250519_005129.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005130.]: Entered 'clean_dt'-Function [20250519_005130.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_005130.]: got experimental data [20250519_005130.]: Entered 'clean_dt'-Function [20250519_005130.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_005130.]: got calibration data [20250519_005130.]: ### Starting with regression calculations ### [20250519_005130.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_005130.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005130.]: Logging df_agg: CpG#1 [20250519_005130.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005130.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005130.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005130.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005130.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005130.]: Entered 'hyperbolic_regression'-Function [20250519_005130.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005130.]: Entered 'cubic_regression'-Function [20250519_005130.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005130.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005130.]: Logging df_agg: CpG#2 [20250519_005130.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005130.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005130.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005130.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005130.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005130.]: Entered 'hyperbolic_regression'-Function [20250519_005130.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005131.]: Entered 'cubic_regression'-Function [20250519_005131.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005131.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005131.]: Logging df_agg: CpG#3 [20250519_005131.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005131.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005131.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005131.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005131.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005131.]: Entered 'hyperbolic_regression'-Function [20250519_005131.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005131.]: Entered 'cubic_regression'-Function [20250519_005131.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005131.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005131.]: Logging df_agg: CpG#4 [20250519_005131.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005131.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005131.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005131.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005131.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005131.]: Entered 'hyperbolic_regression'-Function [20250519_005131.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005131.]: Entered 'cubic_regression'-Function [20250519_005131.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005131.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005131.]: Logging df_agg: CpG#5 [20250519_005131.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005131.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005131.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005131.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005131.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005131.]: Entered 'hyperbolic_regression'-Function [20250519_005131.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005132.]: Entered 'cubic_regression'-Function [20250519_005132.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005132.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005132.]: Logging df_agg: CpG#6 [20250519_005132.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005132.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005132.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005132.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005132.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005132.]: Entered 'hyperbolic_regression'-Function [20250519_005132.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005132.]: Entered 'cubic_regression'-Function [20250519_005132.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005132.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005132.]: Logging df_agg: CpG#7 [20250519_005132.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005132.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005132.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005132.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005132.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005132.]: Entered 'hyperbolic_regression'-Function [20250519_005132.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005132.]: Entered 'cubic_regression'-Function [20250519_005132.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005133.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005133.]: Logging df_agg: CpG#8 [20250519_005133.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005133.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005133.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005133.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005133.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005133.]: Entered 'hyperbolic_regression'-Function [20250519_005133.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005133.]: Entered 'cubic_regression'-Function [20250519_005133.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005133.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005133.]: Logging df_agg: CpG#9 [20250519_005133.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005133.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005133.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005133.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005133.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005133.]: Entered 'hyperbolic_regression'-Function [20250519_005133.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005133.]: Entered 'cubic_regression'-Function [20250519_005133.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005133.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005133.]: Logging df_agg: row_means [20250519_005133.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005133.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005133.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_005133.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005133.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005133.]: Entered 'hyperbolic_regression'-Function [20250519_005133.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005133.]: Entered 'cubic_regression'-Function [20250519_005133.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005134.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_005134.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005134.]: Logging df_agg: CpG#1 [20250519_005134.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005134.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005134.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005134.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005134.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005134.]: Entered 'hyperbolic_regression'-Function [20250519_005134.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005134.]: Entered 'cubic_regression'-Function [20250519_005134.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005135.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005135.]: Logging df_agg: CpG#2 [20250519_005135.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005135.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005135.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005135.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005135.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005135.]: Entered 'hyperbolic_regression'-Function [20250519_005135.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005135.]: Entered 'cubic_regression'-Function [20250519_005135.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005135.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005135.]: Logging df_agg: CpG#3 [20250519_005135.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005135.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005135.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005135.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005135.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005135.]: Entered 'hyperbolic_regression'-Function [20250519_005135.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005135.]: Entered 'cubic_regression'-Function [20250519_005135.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005135.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005135.]: Logging df_agg: CpG#4 [20250519_005135.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005135.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005135.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005135.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005135.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005135.]: Entered 'hyperbolic_regression'-Function [20250519_005135.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005136.]: Entered 'cubic_regression'-Function [20250519_005136.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005136.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005136.]: Logging df_agg: CpG#5 [20250519_005136.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005136.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005136.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005136.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005136.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005136.]: Entered 'hyperbolic_regression'-Function [20250519_005136.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005136.]: Entered 'cubic_regression'-Function [20250519_005136.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005136.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005136.]: Logging df_agg: CpG#6 [20250519_005136.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005136.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005136.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005136.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005136.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005136.]: Entered 'hyperbolic_regression'-Function [20250519_005136.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005136.]: Entered 'cubic_regression'-Function [20250519_005136.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005136.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005136.]: Logging df_agg: CpG#7 [20250519_005136.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005136.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005136.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005136.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005136.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005136.]: Entered 'hyperbolic_regression'-Function [20250519_005136.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005137.]: Entered 'cubic_regression'-Function [20250519_005137.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005137.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005137.]: Logging df_agg: CpG#8 [20250519_005137.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005137.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005137.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005137.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005137.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005137.]: Entered 'hyperbolic_regression'-Function [20250519_005137.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005137.]: Entered 'cubic_regression'-Function [20250519_005137.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005137.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005137.]: Logging df_agg: CpG#9 [20250519_005137.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005137.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005137.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005137.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005137.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005137.]: Entered 'hyperbolic_regression'-Function [20250519_005137.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005137.]: Entered 'cubic_regression'-Function [20250519_005137.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005138.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005138.]: Logging df_agg: row_means [20250519_005138.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005138.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005138.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_005138.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005138.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005138.]: Entered 'hyperbolic_regression'-Function [20250519_005138.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005138.]: Entered 'cubic_regression'-Function [20250519_005138.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005138.]: Entered 'solving_equations'-Function [20250519_005138.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 0 [20250519_005138.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 14.1480167109277 [20250519_005138.]: Samplename: 12.5 Root: 14.148 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1398443790446 [20250519_005138.]: Samplename: 25 Root: 26.14 --> Root in between the borders! Added to results. Hyperbolic solved: 39.3762039523382 [20250519_005139.]: Samplename: 37.5 Root: 39.376 --> Root in between the borders! Added to results. Hyperbolic solved: 52.9476244028783 [20250519_005139.]: Samplename: 50 Root: 52.948 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4195105873419 [20250519_005139.]: Samplename: 62.5 Root: 65.42 --> Root in between the borders! Added to results. Hyperbolic solved: 74.433837698331 [20250519_005139.]: Samplename: 75 Root: 74.434 --> Root in between the borders! Added to results. Hyperbolic solved: 80.5559264925808 [20250519_005139.]: Samplename: 87.5 Root: 80.556 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_005139.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_005139.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 0 [20250519_005139.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 10.7812303822548 [20250519_005139.]: Samplename: 12.5 Root: 10.781 --> Root in between the borders! Added to results. Hyperbolic solved: 26.0648314401535 [20250519_005139.]: Samplename: 25 Root: 26.065 --> Root in between the borders! Added to results. Hyperbolic solved: 35.1980950192158 [20250519_005139.]: Samplename: 37.5 Root: 35.198 --> Root in between the borders! Added to results. Hyperbolic solved: 47.9203836090837 [20250519_005139.]: Samplename: 50 Root: 47.92 --> Root in between the borders! Added to results. Hyperbolic solved: 67.275750545092 [20250519_005139.]: Samplename: 62.5 Root: 67.276 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7278143684244 [20250519_005139.]: Samplename: 75 Root: 75.728 --> Root in between the borders! Added to results. Hyperbolic solved: 84.1258428690749 [20250519_005139.]: Samplename: 87.5 Root: 84.126 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_005139.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_005139.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 0 [20250519_005139.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 10.8467198180169 [20250519_005139.]: Samplename: 12.5 Root: 10.847 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1449700612555 [20250519_005139.]: Samplename: 25 Root: 26.145 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2865760013773 [20250519_005139.]: Samplename: 37.5 Root: 37.287 --> Root in between the borders! Added to results. Hyperbolic solved: 51.4114078772697 [20250519_005139.]: Samplename: 50 Root: 51.411 --> Root in between the borders! Added to results. Hyperbolic solved: 65.0139634978039 [20250519_005139.]: Samplename: 62.5 Root: 65.014 --> Root in between the borders! Added to results. Hyperbolic solved: 76.9921394830877 [20250519_005139.]: Samplename: 75 Root: 76.992 --> Root in between the borders! Added to results. Hyperbolic solved: 79.6808818589171 [20250519_005139.]: Samplename: 87.5 Root: 79.681 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_005139.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_005139.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 0 [20250519_005139.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 13.2494913489925 [20250519_005139.]: Samplename: 12.5 Root: 13.249 --> Root in between the borders! Added to results. Hyperbolic solved: 25.0914701740206 [20250519_005139.]: Samplename: 25 Root: 25.091 --> Root in between the borders! Added to results. Hyperbolic solved: 38.8081740444332 [20250519_005139.]: Samplename: 37.5 Root: 38.808 --> Root in between the borders! Added to results. Hyperbolic solved: 49.1133034150896 [20250519_005139.]: Samplename: 50 Root: 49.113 --> Root in between the borders! Added to results. Hyperbolic solved: 67.573566028728 [20250519_005139.]: Samplename: 62.5 Root: 67.574 --> Root in between the borders! Added to results. Hyperbolic solved: 73.7655861480302 [20250519_005139.]: Samplename: 75 Root: 73.766 --> Root in between the borders! Added to results. Hyperbolic solved: 82.0404940898918 [20250519_005139.]: Samplename: 87.5 Root: 82.04 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_005139.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_005139.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 0 [20250519_005139.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 8.36101271071658 [20250519_005139.]: Samplename: 12.5 Root: 8.361 --> Root in between the borders! Added to results. Hyperbolic solved: 23.0724671456609 [20250519_005139.]: Samplename: 25 Root: 23.072 --> Root in between the borders! Added to results. Hyperbolic solved: 36.9926902689013 [20250519_005139.]: Samplename: 37.5 Root: 36.993 --> Root in between the borders! Added to results. Hyperbolic solved: 50.9902012556783 [20250519_005139.]: Samplename: 50 Root: 50.99 --> Root in between the borders! Added to results. Hyperbolic solved: 62.7269420395599 [20250519_005139.]: Samplename: 62.5 Root: 62.727 --> Root in between the borders! Added to results. Hyperbolic solved: 76.6726263557269 [20250519_005139.]: Samplename: 75 Root: 76.673 --> Root in between the borders! Added to results. Hyperbolic solved: 86.2959101486824 [20250519_005139.]: Samplename: 87.5 Root: 86.296 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_005139.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_005139.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 0 [20250519_005139.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 11.8139244542506 [20250519_005139.]: Samplename: 12.5 Root: 11.814 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5330423964606 [20250519_005139.]: Samplename: 25 Root: 26.533 --> Root in between the borders! Added to results. Hyperbolic solved: 35.365455365759 [20250519_005139.]: Samplename: 37.5 Root: 35.365 --> Root in between the borders! Added to results. Hyperbolic solved: 50.1054345020203 [20250519_005139.]: Samplename: 50 Root: 50.105 --> Root in between the borders! Added to results. Hyperbolic solved: 64.9683746571925 [20250519_005139.]: Samplename: 62.5 Root: 64.968 --> Root in between the borders! Added to results. Hyperbolic solved: 73.6331726521876 [20250519_005139.]: Samplename: 75 Root: 73.633 --> Root in between the borders! Added to results. Hyperbolic solved: 86.9938602772804 [20250519_005139.]: Samplename: 87.5 Root: 86.994 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_005139.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_005139.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 0 [20250519_005139.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 11.7880491100164 [20250519_005139.]: Samplename: 12.5 Root: 11.788 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1959234034328 [20250519_005139.]: Samplename: 25 Root: 26.196 --> Root in between the borders! Added to results. Hyperbolic solved: 39.1978567589215 [20250519_005139.]: Samplename: 37.5 Root: 39.198 --> Root in between the borders! Added to results. Hyperbolic solved: 54.3513505291649 [20250519_005139.]: Samplename: 50 Root: 54.351 --> Root in between the borders! Added to results. Hyperbolic solved: 66.0567953704493 [20250519_005139.]: Samplename: 62.5 Root: 66.057 --> Root in between the borders! Added to results. Hyperbolic solved: 75.1900867269869 [20250519_005139.]: Samplename: 75 Root: 75.19 --> Root in between the borders! Added to results. Hyperbolic solved: 78.6051660349008 [20250519_005139.]: Samplename: 87.5 Root: 78.605 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_005139.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_005139.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 0 [20250519_005139.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 7.27382484148278 [20250519_005139.]: Samplename: 12.5 Root: 7.274 --> Root in between the borders! Added to results. Hyperbolic solved: 24.9765598282779 [20250519_005139.]: Samplename: 25 Root: 24.977 --> Root in between the borders! Added to results. Hyperbolic solved: 34.028313807905 [20250519_005139.]: Samplename: 37.5 Root: 34.028 --> Root in between the borders! Added to results. Hyperbolic solved: 52.2946423509726 [20250519_005139.]: Samplename: 50 Root: 52.295 --> Root in between the borders! Added to results. Hyperbolic solved: 65.0742432627639 [20250519_005139.]: Samplename: 62.5 Root: 65.074 --> Root in between the borders! Added to results. Hyperbolic solved: 78.3047550247862 [20250519_005139.]: Samplename: 75 Root: 78.305 --> Root in between the borders! Added to results. Hyperbolic solved: 81.0501991298613 [20250519_005139.]: Samplename: 87.5 Root: 81.05 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_005139.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_005139.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 0 [20250519_005139.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 12.2068234201026 [20250519_005139.]: Samplename: 12.5 Root: 12.207 --> Root in between the borders! Added to results. Hyperbolic solved: 28.0688737636578 [20250519_005139.]: Samplename: 25 Root: 28.069 --> Root in between the borders! Added to results. Hyperbolic solved: 37.6661823177513 [20250519_005139.]: Samplename: 37.5 Root: 37.666 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3684233460174 [20250519_005139.]: Samplename: 50 Root: 52.368 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8636916649907 [20250519_005139.]: Samplename: 62.5 Root: 64.864 --> Root in between the borders! Added to results. Hyperbolic solved: 74.2551330907062 [20250519_005139.]: Samplename: 75 Root: 74.255 --> Root in between the borders! Added to results. Hyperbolic solved: 83.9343288872042 [20250519_005139.]: Samplename: 87.5 Root: 83.934 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_005139.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_005139.]: Solving hyperbolic regression for row_means Hyperbolic solved: 0.000228160853766634 [20250519_005139.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 11.1587839347378 [20250519_005139.]: Samplename: 12.5 Root: 11.159 --> Root in between the borders! Added to results. Hyperbolic solved: 25.8570776700364 [20250519_005139.]: Samplename: 25 Root: 25.857 --> Root in between the borders! Added to results. Hyperbolic solved: 37.065004454281 [20250519_005139.]: Samplename: 37.5 Root: 37.065 --> Root in between the borders! Added to results. Hyperbolic solved: 51.1881763075298 [20250519_005139.]: Samplename: 50 Root: 51.188 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4439754555105 [20250519_005139.]: Samplename: 62.5 Root: 65.444 --> Root in between the borders! Added to results. Hyperbolic solved: 75.3005883283082 [20250519_005139.]: Samplename: 75 Root: 75.301 --> Root in between the borders! Added to results. Hyperbolic solved: 82.6591025877361 [20250519_005139.]: Samplename: 87.5 Root: 82.659 --> Root in between the borders! Added to results. Hyperbolic solved: 100.000153506599 [20250519_005139.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 [20250519_005139.]: ### Starting with regression calculations ### [20250519_005139.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_005139.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 14.1480167109277, 26.1398443790446, 39.3762039523382, 52.9476244028783, 65.4195105873419, 74.433837698331, 80.5559264925808, 100)c(0, 1.6480167109277, 1.1398443790446, 1.8762039523382, 2.9476244028783, 2.9195105873419, 0.566162301668996, 6.9440735074192, 0)c(NA, 13.1841336874216, 4.5593775161784, 5.00321053956854, 5.89524880575659, 4.67121693974705, 0.754883068891995, 7.93608400847909, 0) [20250519_005139.]: Logging df_agg: CpG#1 [20250519_005139.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005139.]: c(0, 14.1480167109277, 26.1398443790446, 39.3762039523382, 52.9476244028783, 65.4195105873419, 74.433837698331, 80.5559264925808, 100)[20250519_005139.]: c(0, 1.6480167109277, 1.1398443790446, 1.8762039523382, 2.9476244028783, 2.9195105873419, 0.566162301668996, 6.9440735074192, 0)[20250519_005139.]: c(NA, 13.1841336874216, 4.5593775161784, 5.00321053956854, 5.89524880575659, 4.67121693974705, 0.754883068891995, 7.93608400847909, 0) [20250519_005139.]: Entered 'hyperbolic_regression'-Function [20250519_005139.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005139.]: Entered 'cubic_regression'-Function [20250519_005139.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005139.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.7812303822548, 26.0648314401535, 35.1980950192158, 47.9203836090837, 67.275750545092, 75.7278143684244, 84.1258428690749, 100)c(0, 1.7187696177452, 1.0648314401535, 2.3019049807842, 2.0796163909163, 4.775750545092, 0.727814368424404, 3.3741571309251, 0)c(NA, 13.7501569419616, 4.259325760614, 6.1384132820912, 4.1592327818326, 7.64120087214719, 0.970419157899206, 3.85617957820011, 0) [20250519_005139.]: Logging df_agg: CpG#2 [20250519_005139.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005139.]: c(0, 10.7812303822548, 26.0648314401535, 35.1980950192158, 47.9203836090837, 67.275750545092, 75.7278143684244, 84.1258428690749, 100)[20250519_005139.]: c(0, 1.7187696177452, 1.0648314401535, 2.3019049807842, 2.0796163909163, 4.775750545092, 0.727814368424404, 3.3741571309251, 0)[20250519_005139.]: c(NA, 13.7501569419616, 4.259325760614, 6.1384132820912, 4.1592327818326, 7.64120087214719, 0.970419157899206, 3.85617957820011, 0) [20250519_005139.]: Entered 'hyperbolic_regression'-Function [20250519_005139.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005140.]: Entered 'cubic_regression'-Function [20250519_005140.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005140.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.8467198180169, 26.1449700612555, 37.2865760013773, 51.4114078772697, 65.0139634978039, 76.9921394830877, 79.6808818589171, 100)c(0, 1.6532801819831, 1.1449700612555, 0.213423998622702, 1.4114078772697, 2.5139634978039, 1.9921394830877, 7.8191181410829, 0)c(NA, 13.2262414558648, 4.579880245022, 0.569130662993871, 2.82281575453941, 4.02234159648624, 2.65618597745026, 8.93613501838045, 0) [20250519_005140.]: Logging df_agg: CpG#3 [20250519_005140.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005140.]: c(0, 10.8467198180169, 26.1449700612555, 37.2865760013773, 51.4114078772697, 65.0139634978039, 76.9921394830877, 79.6808818589171, 100)[20250519_005140.]: c(0, 1.6532801819831, 1.1449700612555, 0.213423998622702, 1.4114078772697, 2.5139634978039, 1.9921394830877, 7.8191181410829, 0)[20250519_005140.]: c(NA, 13.2262414558648, 4.579880245022, 0.569130662993871, 2.82281575453941, 4.02234159648624, 2.65618597745026, 8.93613501838045, 0) [20250519_005140.]: Entered 'hyperbolic_regression'-Function [20250519_005140.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005140.]: Entered 'cubic_regression'-Function [20250519_005140.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005140.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 13.2494913489925, 25.0914701740206, 38.8081740444332, 49.1133034150896, 67.573566028728, 73.7655861480302, 82.0404940898918, 100)c(0, 0.7494913489925, 0.0914701740205999, 1.3081740444332, 0.886696584910403, 5.073566028728, 1.2344138519698, 5.45950591010821, 0)c(NA, 5.99593079194, 0.365880696082399, 3.48846411848854, 1.77339316982081, 8.11770564596479, 1.64588513595974, 6.23943532583795, 0) [20250519_005140.]: Logging df_agg: CpG#4 [20250519_005140.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005140.]: c(0, 13.2494913489925, 25.0914701740206, 38.8081740444332, 49.1133034150896, 67.573566028728, 73.7655861480302, 82.0404940898918, 100)[20250519_005140.]: c(0, 0.7494913489925, 0.0914701740205999, 1.3081740444332, 0.886696584910403, 5.073566028728, 1.2344138519698, 5.45950591010821, 0)[20250519_005140.]: c(NA, 5.99593079194, 0.365880696082399, 3.48846411848854, 1.77339316982081, 8.11770564596479, 1.64588513595974, 6.23943532583795, 0) [20250519_005140.]: Entered 'hyperbolic_regression'-Function [20250519_005140.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005140.]: Entered 'cubic_regression'-Function [20250519_005140.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005140.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.36101271071658, 23.0724671456609, 36.9926902689013, 50.9902012556783, 62.7269420395599, 76.6726263557269, 86.2959101486824, 100)c(0, 4.13898728928342, 1.9275328543391, 0.507309731098701, 0.990201255678301, 0.226942039559901, 1.6726263557269, 1.2040898513176, 0)c(NA, 33.1118983142674, 7.71013141735639, 1.35282594959654, 1.9804025113566, 0.363107263295842, 2.23016847430254, 1.37610268722012, 0) [20250519_005141.]: Logging df_agg: CpG#5 [20250519_005141.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005141.]: c(0, 8.36101271071658, 23.0724671456609, 36.9926902689013, 50.9902012556783, 62.7269420395599, 76.6726263557269, 86.2959101486824, 100)[20250519_005141.]: c(0, 4.13898728928342, 1.9275328543391, 0.507309731098701, 0.990201255678301, 0.226942039559901, 1.6726263557269, 1.2040898513176, 0)[20250519_005141.]: c(NA, 33.1118983142674, 7.71013141735639, 1.35282594959654, 1.9804025113566, 0.363107263295842, 2.23016847430254, 1.37610268722012, 0) [20250519_005141.]: Entered 'hyperbolic_regression'-Function [20250519_005141.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005141.]: Entered 'cubic_regression'-Function [20250519_005141.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005141.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.8139244542506, 26.5330423964606, 35.365455365759, 50.1054345020203, 64.9683746571925, 73.6331726521876, 86.9938602772804, 100)c(0, 0.6860755457494, 1.5330423964606, 2.134544634241, 0.105434502020302, 2.4683746571925, 1.3668273478124, 0.506139722719595, 0)c(NA, 5.4886043659952, 6.13216958584241, 5.69211902464267, 0.210869004040603, 3.949399451508, 1.82243646374987, 0.578445397393823, 0) [20250519_005141.]: Logging df_agg: CpG#6 [20250519_005141.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005141.]: c(0, 11.8139244542506, 26.5330423964606, 35.365455365759, 50.1054345020203, 64.9683746571925, 73.6331726521876, 86.9938602772804, 100)[20250519_005141.]: c(0, 0.6860755457494, 1.5330423964606, 2.134544634241, 0.105434502020302, 2.4683746571925, 1.3668273478124, 0.506139722719595, 0)[20250519_005141.]: c(NA, 5.4886043659952, 6.13216958584241, 5.69211902464267, 0.210869004040603, 3.949399451508, 1.82243646374987, 0.578445397393823, 0) [20250519_005141.]: Entered 'hyperbolic_regression'-Function [20250519_005141.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005141.]: Entered 'cubic_regression'-Function [20250519_005141.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005141.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.7880491100164, 26.1959234034328, 39.1978567589215, 54.3513505291649, 66.0567953704493, 75.1900867269869, 78.6051660349008, 100)c(0, 0.7119508899836, 1.1959234034328, 1.6978567589215, 4.3513505291649, 3.55679537044929, 0.190086726986905, 8.8948339650992, 0)c(NA, 5.6956071198688, 4.7836936137312, 4.52761802379068, 8.70270105832979, 5.69087259271887, 0.253448969315874, 10.1655245315419, 0) [20250519_005141.]: Logging df_agg: CpG#7 [20250519_005141.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005141.]: c(0, 11.7880491100164, 26.1959234034328, 39.1978567589215, 54.3513505291649, 66.0567953704493, 75.1900867269869, 78.6051660349008, 100)[20250519_005141.]: c(0, 0.7119508899836, 1.1959234034328, 1.6978567589215, 4.3513505291649, 3.55679537044929, 0.190086726986905, 8.8948339650992, 0)[20250519_005141.]: c(NA, 5.6956071198688, 4.7836936137312, 4.52761802379068, 8.70270105832979, 5.69087259271887, 0.253448969315874, 10.1655245315419, 0) [20250519_005141.]: Entered 'hyperbolic_regression'-Function [20250519_005141.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005142.]: Entered 'cubic_regression'-Function [20250519_005142.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005142.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 7.27382484148278, 24.9765598282779, 34.028313807905, 52.2946423509726, 65.0742432627639, 78.3047550247862, 81.0501991298613, 100)c(0, 5.22617515851722, 0.0234401717221004, 3.471686192095, 2.2946423509726, 2.5742432627639, 3.3047550247862, 6.4498008701387, 0)c(NA, 41.8094012681378, 0.0937606868884018, 9.25782984558666, 4.5892847019452, 4.11878922042224, 4.40634003304827, 7.37120099444423, 0) [20250519_005142.]: Logging df_agg: CpG#8 [20250519_005142.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005142.]: c(0, 7.27382484148278, 24.9765598282779, 34.028313807905, 52.2946423509726, 65.0742432627639, 78.3047550247862, 81.0501991298613, 100)[20250519_005142.]: c(0, 5.22617515851722, 0.0234401717221004, 3.471686192095, 2.2946423509726, 2.5742432627639, 3.3047550247862, 6.4498008701387, 0)[20250519_005142.]: c(NA, 41.8094012681378, 0.0937606868884018, 9.25782984558666, 4.5892847019452, 4.11878922042224, 4.40634003304827, 7.37120099444423, 0) [20250519_005142.]: Entered 'hyperbolic_regression'-Function [20250519_005142.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005142.]: Entered 'cubic_regression'-Function [20250519_005142.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005142.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.2068234201026, 28.0688737636578, 37.6661823177513, 52.3684233460174, 64.8636916649907, 74.2551330907062, 83.9343288872042, 100)c(0, 0.2931765798974, 3.0688737636578, 0.1661823177513, 2.3684233460174, 2.3636916649907, 0.744866909293805, 3.5656711127958, 0)c(NA, 2.3454126391792, 12.2754950546312, 0.4431528473368, 4.7368466920348, 3.78190666398511, 0.993155879058406, 4.07505270033805, 0) [20250519_005142.]: Logging df_agg: CpG#9 [20250519_005142.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005142.]: c(0, 12.2068234201026, 28.0688737636578, 37.6661823177513, 52.3684233460174, 64.8636916649907, 74.2551330907062, 83.9343288872042, 100)[20250519_005142.]: c(0, 0.2931765798974, 3.0688737636578, 0.1661823177513, 2.3684233460174, 2.3636916649907, 0.744866909293805, 3.5656711127958, 0)[20250519_005142.]: c(NA, 2.3454126391792, 12.2754950546312, 0.4431528473368, 4.7368466920348, 3.78190666398511, 0.993155879058406, 4.07505270033805, 0) [20250519_005142.]: Entered 'hyperbolic_regression'-Function [20250519_005142.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005142.]: Entered 'cubic_regression'-Function [20250519_005142.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005142.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.000228160853766634, 11.1587839347378, 25.8570776700364, 37.065004454281, 51.1881763075298, 65.4439754555105, 75.3005883283082, 82.6591025877361, 100)c(0.000228160853766634, 1.3412160652622, 0.8570776700364, 0.434995545718998, 1.1881763075298, 2.9439754555105, 0.300588328308194, 4.8408974122639, 0)c(NA, 10.7297285220976, 3.4283106801456, 1.15998812191733, 2.3763526150596, 4.7103607288168, 0.400784437744259, 5.53245418544446, 0) [20250519_005142.]: Logging df_agg: row_means [20250519_005142.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005142.]: c(0.000228160853766634, 11.1587839347378, 25.8570776700364, 37.065004454281, 51.1881763075298, 65.4439754555105, 75.3005883283082, 82.6591025877361, 100)[20250519_005142.]: c(0.000228160853766634, 1.3412160652622, 0.8570776700364, 0.434995545718998, 1.1881763075298, 2.9439754555105, 0.300588328308194, 4.8408974122639, 0)[20250519_005142.]: c(NA, 10.7297285220976, 3.4283106801456, 1.15998812191733, 2.3763526150596, 4.7103607288168, 0.400784437744259, 5.53245418544446, 0) [20250519_005142.]: Entered 'hyperbolic_regression'-Function [20250519_005142.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005143.]: Entered 'cubic_regression'-Function [20250519_005143.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005143.]: Entered 'solving_equations'-Function [20250519_005143.]: Solving cubic regression for CpG#1 Coefficients: 0Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20250519_005143.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -7.30533333333333Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20250519_005143.]: Samplename: 12.5 Root: 4.661 --> Root in between the borders! Added to results. Coefficients: -14.352Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20250519_005143.]: Samplename: 25 Root: 9.352 --> Root in between the borders! Added to results. Coefficients: -23.244Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20250519_005143.]: Samplename: 37.5 Root: 15.59 --> Root in between the borders! Added to results. Coefficients: -33.8645Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20250519_005143.]: Samplename: 50 Root: 23.601 --> Root in between the borders! Added to results. Coefficients: -45.318Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20250519_005143.]: Samplename: 62.5 Root: 33.121 --> Root in between the borders! Added to results. Coefficients: -54.857Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20250519_005143.]: Samplename: 75 Root: 42.001 --> Root in between the borders! Added to results. Coefficients: -62.062Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20250519_005143.]: Samplename: 87.5 Root: 49.51 --> Root in between the borders! Added to results. Coefficients: -90.01Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20250519_005143.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 [20250519_005143.]: Solving cubic regression for CpG#2 Coefficients: 0Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20250519_005143.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -6.05666666666666Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20250519_005143.]: Samplename: 12.5 Root: 8.415 --> Root in between the borders! Added to results. Coefficients: -15.656Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20250519_005143.]: Samplename: 25 Root: 21.016 --> Root in between the borders! Added to results. Coefficients: -22.054Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20250519_005143.]: Samplename: 37.5 Root: 28.984 --> Root in between the borders! Added to results. Coefficients: -31.945Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20250519_005143.]: Samplename: 50 Root: 40.726 --> Root in between the borders! Added to results. Coefficients: -49.68Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20250519_005143.]: Samplename: 62.5 Root: 60.322 --> Root in between the borders! Added to results. Coefficients: -58.6825Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20250519_005143.]: Samplename: 75 Root: 69.671 --> Root in between the borders! Added to results. Coefficients: -68.5533333333333Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20250519_005143.]: Samplename: 87.5 Root: 79.529 --> Root in between the borders! Added to results. Coefficients: -90.294Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20250519_005143.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 [20250519_005143.]: Solving cubic regression for CpG#3 Coefficients: 0Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20250519_005143.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -5.67Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20250519_005143.]: Samplename: 12.5 Root: 5.75 --> Root in between the borders! Added to results. Coefficients: -14.526Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20250519_005143.]: Samplename: 25 Root: 15.012 --> Root in between the borders! Added to results. Coefficients: -21.71Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20250519_005143.]: Samplename: 37.5 Root: 22.804 --> Root in between the borders! Added to results. Coefficients: -31.8725Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20250519_005143.]: Samplename: 50 Root: 34.308 --> Root in between the borders! Added to results. Coefficients: -42.986Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20250519_005143.]: Samplename: 62.5 Root: 47.638 --> Root in between the borders! Added to results. Coefficients: -54.0725Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20250519_005143.]: Samplename: 75 Root: 61.877 --> Root in between the borders! Added to results. Coefficients: -56.7533333333333Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20250519_005143.]: Samplename: 87.5 Root: 65.486 --> Root in between the borders! Added to results. Coefficients: -79.762Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20250519_005143.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 [20250519_005143.]: Solving cubic regression for CpG#4 Coefficients: 0Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20250519_005143.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -7.65533333333333Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20250519_005143.]: Samplename: 12.5 Root: 6.614 --> Root in between the borders! Added to results. Coefficients: -15.206Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20250519_005143.]: Samplename: 25 Root: 13.372 --> Root in between the borders! Added to results. Coefficients: -24.93Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20250519_005143.]: Samplename: 37.5 Root: 22.462 --> Root in between the borders! Added to results. Coefficients: -33.0395Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20250519_005143.]: Samplename: 50 Root: 30.424 --> Root in between the borders! Added to results. Coefficients: -49.658Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20250519_005143.]: Samplename: 62.5 Root: 48.07 --> Root in between the borders! Added to results. Coefficients: -55.942Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20250519_005143.]: Samplename: 75 Root: 55.318 --> Root in between the borders! Added to results. Coefficients: -64.9953333333333Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20250519_005143.]: Samplename: 87.5 Root: 66.469 --> Root in between the borders! Added to results. Coefficients: -87.724Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20250519_005143.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_005143.]: Solving cubic regression for CpG#5 Coefficients: 0Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_005143.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -4.144Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_005143.]: Samplename: 12.5 Root: 10.457 --> Root in between the borders! Added to results. Coefficients: -12.102Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_005143.]: Samplename: 25 Root: 26.323 --> Root in between the borders! Added to results. Coefficients: -20.536Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_005143.]: Samplename: 37.5 Root: 39.937 --> Root in between the borders! Added to results. Coefficients: -30.0715Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_005143.]: Samplename: 50 Root: 53.063 --> Root in between the borders! Added to results. Coefficients: -39.034Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_005143.]: Samplename: 62.5 Root: 63.959 --> Root in between the borders! Added to results. Coefficients: -51.059Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_005143.]: Samplename: 75 Root: 77.051 --> Root in between the borders! Added to results. Coefficients: -60.3906666666667Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_005143.]: Samplename: 87.5 Root: 86.31 --> Root in between the borders! Added to results. Coefficients: -75.446Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_005143.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_005143.]: Solving cubic regression for CpG#6 Coefficients: 0Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20250519_005143.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -6.54266666666667Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20250519_005143.]: Samplename: 12.5 Root: 8.15 --> Root in between the borders! Added to results. Coefficients: -15.692Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20250519_005143.]: Samplename: 25 Root: 19.28 --> Root in between the borders! Added to results. Coefficients: -21.804Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20250519_005143.]: Samplename: 37.5 Root: 26.553 --> Root in between the borders! Added to results. Coefficients: -33.2485Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20250519_005143.]: Samplename: 50 Root: 39.845 --> Root in between the borders! Added to results. Coefficients: -46.704Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20250519_005143.]: Samplename: 62.5 Root: 54.973 --> Root in between the borders! Added to results. Coefficients: -55.636Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20250519_005143.]: Samplename: 75 Root: 64.743 --> Root in between the borders! Added to results. Coefficients: -71.3493333333333Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20250519_005143.]: Samplename: 87.5 Root: 81.445 --> Root in between the borders! Added to results. Coefficients: -89.46Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20250519_005144.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_005144.]: Solving cubic regression for CpG#7 Coefficients: 0Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20250519_005144.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -4.18066666666667Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20250519_005144.]: Samplename: 12.5 Root: 3.662 --> Root in between the borders! Added to results. Coefficients: -10.05Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20250519_005144.]: Samplename: 25 Root: 9.013 --> Root in between the borders! Added to results. Coefficients: -16.236Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20250519_005144.]: Samplename: 37.5 Root: 14.96 --> Root in between the borders! Added to results. Coefficients: -24.8165Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20250519_005144.]: Samplename: 50 Root: 23.842 --> Root in between the borders! Added to results. Coefficients: -32.75Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20250519_005144.]: Samplename: 62.5 Root: 32.894 --> Root in between the borders! Added to results. Coefficients: -39.954Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20250519_005144.]: Samplename: 75 Root: 42.068 --> Root in between the borders! Added to results. Coefficients: -42.9206666666667Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20250519_005144.]: Samplename: 87.5 Root: 46.196 --> Root in between the borders! Added to results. Coefficients: -66.008Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20250519_005144.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_005144.]: Solving cubic regression for CpG#8 Coefficients: 0Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_005144.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -4.35066666666667Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_005144.]: Samplename: 12.5 Root: 8.961 --> Root in between the borders! Added to results. Coefficients: -15.834Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_005144.]: Samplename: 25 Root: 28.16 --> Root in between the borders! Added to results. Coefficients: -22.254Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_005144.]: Samplename: 37.5 Root: 37.189 --> Root in between the borders! Added to results. Coefficients: -36.529Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_005144.]: Samplename: 50 Root: 54.661 --> Root in between the borders! Added to results. Coefficients: -47.73Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_005144.]: Samplename: 62.5 Root: 66.642 --> Root in between the borders! Added to results. Coefficients: -60.5715Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_005144.]: Samplename: 75 Root: 79.079 --> Root in between the borders! Added to results. Coefficients: -63.414Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_005144.]: Samplename: 87.5 Root: 81.681 --> Root in between the borders! Added to results. Coefficients: -84.964Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_005144.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_005144.]: Solving cubic regression for CpG#9 Coefficients: 0Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20250519_005144.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -5.406Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20250519_005144.]: Samplename: 12.5 Root: 3.608 --> Root in between the borders! Added to results. Coefficients: -13.716Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20250519_005144.]: Samplename: 25 Root: 9.37 --> Root in between the borders! Added to results. Coefficients: -19.634Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20250519_005144.]: Samplename: 37.5 Root: 13.653 --> Root in between the borders! Added to results. Coefficients: -30.406Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20250519_005144.]: Samplename: 50 Root: 21.897 --> Root in between the borders! Added to results. Coefficients: -41.696Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20250519_005144.]: Samplename: 62.5 Root: 31.298 --> Root in between the borders! Added to results. Coefficients: -51.9135Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20250519_005144.]: Samplename: 75 Root: 40.689 --> Root in between the borders! Added to results. Coefficients: -64.5026666666667Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20250519_005144.]: Samplename: 87.5 Root: 53.907 --> Root in between the borders! Added to results. Coefficients: -92Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20250519_005144.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_005144.]: Solving cubic regression for row_means Coefficients: -0.000111111111110951Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20250519_005144.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -5.70137037037037Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20250519_005144.]: Samplename: 12.5 Root: 6.107 --> Root in between the borders! Added to results. Coefficients: -14.1261111111111Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20250519_005144.]: Samplename: 25 Root: 15.282 --> Root in between the borders! Added to results. Coefficients: -21.3781111111111Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20250519_005144.]: Samplename: 37.5 Root: 23.331 --> Root in between the borders! Added to results. Coefficients: -31.7548888888889Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20250519_005144.]: Samplename: 50 Root: 35.107 --> Root in between the borders! Added to results. Coefficients: -43.9507777777778Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20250519_005144.]: Samplename: 62.5 Root: 49.368 --> Root in between the borders! Added to results. Coefficients: -53.6321111111111Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20250519_005144.]: Samplename: 75 Root: 61.044 --> Root in between the borders! Added to results. Coefficients: -61.6602592592593Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20250519_005144.]: Samplename: 87.5 Root: 70.984 --> Root in between the borders! Added to results. Coefficients: -83.9632222222222Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20250519_005144.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 [20250519_005144.]: ### Starting with regression calculations ### [20250519_005144.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 [20250519_005144.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 4.66057108506072, 9.3520572622014, 15.5898391276712, 23.6006773278817, 33.1210812975699, 42.000790440049, 49.5097019721065, 100)c(0, 7.83942891493928, 15.6479427377986, 21.9101608723288, 26.3993226721183, 29.3789187024301, 32.999209559951, 37.9902980278935, 0)c(NA, 62.7154313195142, 62.5917709511944, 58.4270956595435, 52.7986453442366, 47.0062699238882, 43.9989460799347, 43.4174834604497, 0) [20250519_005144.]: Logging df_agg: CpG#1 [20250519_005144.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005144.]: c(0, 4.66057108506072, 9.3520572622014, 15.5898391276712, 23.6006773278817, 33.1210812975699, 42.000790440049, 49.5097019721065, 100)[20250519_005144.]: c(0, 7.83942891493928, 15.6479427377986, 21.9101608723288, 26.3993226721183, 29.3789187024301, 32.999209559951, 37.9902980278935, 0)[20250519_005144.]: c(NA, 62.7154313195142, 62.5917709511944, 58.4270956595435, 52.7986453442366, 47.0062699238882, 43.9989460799347, 43.4174834604497, 0) [20250519_005144.]: Entered 'hyperbolic_regression'-Function [20250519_005144.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005144.]: Entered 'cubic_regression'-Function [20250519_005144.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005144.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.41473135558285, 21.0155787157944, 28.98380769729, 40.7258283847219, 60.321752127534, 69.670868358929, 79.5285048174076, 100)c(0, 4.08526864441715, 3.9844212842056, 8.51619230271, 9.2741716152781, 2.178247872466, 5.329131641071, 7.97149518259241, 0)c(NA, 32.6821491553372, 15.9376851368224, 22.70984614056, 18.5483432305562, 3.4851965959456, 7.10550885476133, 9.11028020867704, 0) [20250519_005144.]: Logging df_agg: CpG#2 [20250519_005144.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005144.]: c(0, 8.41473135558285, 21.0155787157944, 28.98380769729, 40.7258283847219, 60.321752127534, 69.670868358929, 79.5285048174076, 100)[20250519_005144.]: c(0, 4.08526864441715, 3.9844212842056, 8.51619230271, 9.2741716152781, 2.178247872466, 5.329131641071, 7.97149518259241, 0)[20250519_005144.]: c(NA, 32.6821491553372, 15.9376851368224, 22.70984614056, 18.5483432305562, 3.4851965959456, 7.10550885476133, 9.11028020867704, 0) [20250519_005144.]: Entered 'hyperbolic_regression'-Function [20250519_005144.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005144.]: Entered 'cubic_regression'-Function [20250519_005144.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005145.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 5.74980505230347, 15.0124787349221, 22.8043512255536, 34.3082682140428, 47.6381691654944, 61.8773912993076, 65.4860304554385, 100)c(0, 6.75019494769653, 9.9875212650779, 14.6956487744464, 15.6917317859572, 14.8618308345056, 13.1226087006924, 22.0139695445615, 0)c(NA, 54.0015595815722, 39.9500850603116, 39.1883967318571, 31.3834635719144, 23.778929335209, 17.4968116009232, 25.1588223366417, 0) [20250519_005145.]: Logging df_agg: CpG#3 [20250519_005145.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005145.]: c(0, 5.74980505230347, 15.0124787349221, 22.8043512255536, 34.3082682140428, 47.6381691654944, 61.8773912993076, 65.4860304554385, 100)[20250519_005145.]: c(0, 6.75019494769653, 9.9875212650779, 14.6956487744464, 15.6917317859572, 14.8618308345056, 13.1226087006924, 22.0139695445615, 0)[20250519_005145.]: c(NA, 54.0015595815722, 39.9500850603116, 39.1883967318571, 31.3834635719144, 23.778929335209, 17.4968116009232, 25.1588223366417, 0) [20250519_005145.]: Entered 'hyperbolic_regression'-Function [20250519_005145.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005145.]: Entered 'cubic_regression'-Function [20250519_005145.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005145.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 6.6142663267093, 13.3723444572473, 22.4624407354305, 30.4240096190196, 48.070278909378, 55.3176226954937, 66.4686809672836, 100)c(0, 5.8857336732907, 11.6276555427527, 15.0375592645695, 19.5759903809804, 14.429721090622, 19.6823773045063, 21.0313190327164, 0)c(NA, 47.0858693863256, 46.5106221710108, 40.100158038852, 39.1519807619608, 23.0875537449952, 26.2431697393417, 24.0357931802473, 0) [20250519_005145.]: Logging df_agg: CpG#4 [20250519_005145.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005145.]: c(0, 6.6142663267093, 13.3723444572473, 22.4624407354305, 30.4240096190196, 48.070278909378, 55.3176226954937, 66.4686809672836, 100)[20250519_005145.]: c(0, 5.8857336732907, 11.6276555427527, 15.0375592645695, 19.5759903809804, 14.429721090622, 19.6823773045063, 21.0313190327164, 0)[20250519_005145.]: c(NA, 47.0858693863256, 46.5106221710108, 40.100158038852, 39.1519807619608, 23.0875537449952, 26.2431697393417, 24.0357931802473, 0) [20250519_005145.]: Entered 'hyperbolic_regression'-Function [20250519_005145.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005145.]: Entered 'cubic_regression'-Function [20250519_005145.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005145.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.4570373964758, 26.3229081557653, 39.9371114374682, 53.0630784939681, 63.9590902125397, 77.051078420522, 86.3095786035144, 100)c(0, 2.0429626035242, 1.3229081557653, 2.4371114374682, 3.0630784939681, 1.4590902125397, 2.051078420522, 1.1904213964856, 0)c(NA, 16.3437008281936, 5.2916326230612, 6.49896383324852, 6.1261569879362, 2.33454434006352, 2.73477122736267, 1.36048159598355, 0) [20250519_005145.]: Logging df_agg: CpG#5 [20250519_005145.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005145.]: c(0, 10.4570373964758, 26.3229081557653, 39.9371114374682, 53.0630784939681, 63.9590902125397, 77.051078420522, 86.3095786035144, 100)[20250519_005145.]: c(0, 2.0429626035242, 1.3229081557653, 2.4371114374682, 3.0630784939681, 1.4590902125397, 2.051078420522, 1.1904213964856, 0)[20250519_005145.]: c(NA, 16.3437008281936, 5.2916326230612, 6.49896383324852, 6.1261569879362, 2.33454434006352, 2.73477122736267, 1.36048159598355, 0) [20250519_005145.]: Entered 'hyperbolic_regression'-Function [20250519_005145.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005146.]: Entered 'cubic_regression'-Function [20250519_005146.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005146.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.15031343762292, 19.2804724179951, 26.5529108075111, 39.8450445790276, 54.9734681583699, 64.742519688394, 81.444841573602, 100)c(0, 4.34968656237708, 5.7195275820049, 10.9470891924889, 10.1549554209724, 7.5265318416301, 10.257480311606, 6.05515842639799, 0)c(NA, 34.7974924990166, 22.8781103280196, 29.1922378466371, 20.3099108419448, 12.0424509466082, 13.6766404154747, 6.92018105874057, 0) [20250519_005146.]: Logging df_agg: CpG#6 [20250519_005146.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005146.]: c(0, 8.15031343762292, 19.2804724179951, 26.5529108075111, 39.8450445790276, 54.9734681583699, 64.742519688394, 81.444841573602, 100)[20250519_005146.]: c(0, 4.34968656237708, 5.7195275820049, 10.9470891924889, 10.1549554209724, 7.5265318416301, 10.257480311606, 6.05515842639799, 0)[20250519_005146.]: c(NA, 34.7974924990166, 22.8781103280196, 29.1922378466371, 20.3099108419448, 12.0424509466082, 13.6766404154747, 6.92018105874057, 0) [20250519_005146.]: Entered 'hyperbolic_regression'-Function [20250519_005146.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005146.]: Entered 'cubic_regression'-Function [20250519_005146.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005146.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 3.66155933245296, 9.01334562885863, 14.9602090097958, 23.8420840440459, 32.8944868748769, 42.0684566960277, 46.1959470960303, 99.9999999999999)c(0, 8.83844066754704, 15.9866543711414, 22.5397909902042, 26.1579159559541, 29.6055131251231, 32.9315433039723, 41.3040529039697, 9.9475983006414e-14)c(NA, 70.7075253403763, 63.9466174845655, 60.1061093072112, 52.3158319119082, 47.368821000197, 43.9087244052964, 47.2046318902511, 9.9475983006414e-14) [20250519_005146.]: Logging df_agg: CpG#7 [20250519_005146.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005146.]: c(0, 3.66155933245296, 9.01334562885863, 14.9602090097958, 23.8420840440459, 32.8944868748769, 42.0684566960277, 46.1959470960303, 99.9999999999999)[20250519_005146.]: c(0, 8.83844066754704, 15.9866543711414, 22.5397909902042, 26.1579159559541, 29.6055131251231, 32.9315433039723, 41.3040529039697, 9.9475983006414e-14)[20250519_005146.]: c(NA, 70.7075253403763, 63.9466174845655, 60.1061093072112, 52.3158319119082, 47.368821000197, 43.9087244052964, 47.2046318902511, 9.9475983006414e-14) [20250519_005146.]: Entered 'hyperbolic_regression'-Function [20250519_005146.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005146.]: Entered 'cubic_regression'-Function [20250519_005146.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005147.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.96146907472175, 28.1602892189673, 37.1893361505397, 54.6609191320759, 66.6424746382684, 79.0794300908275, 81.6808296744085, 100)c(0, 3.53853092527825, 3.1602892189673, 0.310663849460298, 4.6609191320759, 4.1424746382684, 4.0794300908275, 5.8191703255915, 0)c(NA, 28.308247402226, 12.6411568758692, 0.828436931894127, 9.32183826415179, 6.62795942122943, 5.43924012110333, 6.65048037210457, 0) [20250519_005147.]: Logging df_agg: CpG#8 [20250519_005147.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005147.]: c(0, 8.96146907472175, 28.1602892189673, 37.1893361505397, 54.6609191320759, 66.6424746382684, 79.0794300908275, 81.6808296744085, 100)[20250519_005147.]: c(0, 3.53853092527825, 3.1602892189673, 0.310663849460298, 4.6609191320759, 4.1424746382684, 4.0794300908275, 5.8191703255915, 0)[20250519_005147.]: c(NA, 28.308247402226, 12.6411568758692, 0.828436931894127, 9.32183826415179, 6.62795942122943, 5.43924012110333, 6.65048037210457, 0) [20250519_005147.]: Entered 'hyperbolic_regression'-Function [20250519_005147.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005147.]: Entered 'cubic_regression'-Function [20250519_005147.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005147.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 3.60796342124625, 9.37027096567147, 13.6529015267019, 21.8965479410597, 31.2983747080124, 40.6887948365007, 53.9067876123417, 100)c(0, 8.89203657875375, 15.6297290343285, 23.8470984732981, 28.1034520589403, 31.2016252919876, 34.3112051634993, 33.5932123876583, 0)c(NA, 71.13629263003, 62.5189161373141, 63.5922625954616, 56.2069041178806, 49.9226004671802, 45.7482735513324, 38.3922427287523, 0) [20250519_005147.]: Logging df_agg: CpG#9 [20250519_005147.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005147.]: c(0, 3.60796342124625, 9.37027096567147, 13.6529015267019, 21.8965479410597, 31.2983747080124, 40.6887948365007, 53.9067876123417, 100)[20250519_005147.]: c(0, 8.89203657875375, 15.6297290343285, 23.8470984732981, 28.1034520589403, 31.2016252919876, 34.3112051634993, 33.5932123876583, 0)[20250519_005147.]: c(NA, 71.13629263003, 62.5189161373141, 63.5922625954616, 56.2069041178806, 49.9226004671802, 45.7482735513324, 38.3922427287523, 0) [20250519_005147.]: Entered 'hyperbolic_regression'-Function [20250519_005147.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005147.]: Entered 'cubic_regression'-Function [20250519_005147.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005147.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.000118249869729556, 6.10737244290489, 15.2822474794705, 23.3309318476314, 35.1067698776441, 49.3683845454072, 61.0436420088989, 70.9843655625978, 100)c(0.000118249869729556, 6.39262755709511, 9.7177525205295, 14.1690681523686, 14.8932301223559, 13.1316154545928, 13.9563579911011, 16.5156344374022, 0)c(NA, 51.1410204567609, 38.871010082118, 37.7841817396496, 29.7864602447118, 21.0105847273485, 18.6084773214681, 18.8750107856025, 0) [20250519_005147.]: Logging df_agg: row_means [20250519_005147.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005147.]: c(0.000118249869729556, 6.10737244290489, 15.2822474794705, 23.3309318476314, 35.1067698776441, 49.3683845454072, 61.0436420088989, 70.9843655625978, 100)[20250519_005147.]: c(0.000118249869729556, 6.39262755709511, 9.7177525205295, 14.1690681523686, 14.8932301223559, 13.1316154545928, 13.9563579911011, 16.5156344374022, 0)[20250519_005147.]: c(NA, 51.1410204567609, 38.871010082118, 37.7841817396496, 29.7864602447118, 21.0105847273485, 18.6084773214681, 18.8750107856025, 0) [20250519_005147.]: Entered 'hyperbolic_regression'-Function [20250519_005147.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005148.]: Entered 'cubic_regression'-Function [20250519_005148.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005148.]: Entered 'solving_equations'-Function [20250519_005148.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 78.9992198550223 [20250519_005148.]: Samplename: Sample#1 Root: 78.999 --> Root in between the borders! Added to results. Hyperbolic solved: 31.2870579236875 [20250519_005148.]: Samplename: Sample#10 Root: 31.287 --> Root in between the borders! Added to results. Hyperbolic solved: 42.7215290847412 [20250519_005148.]: Samplename: Sample#2 Root: 42.722 --> Root in between the borders! Added to results. Hyperbolic solved: 57.8350974981729 [20250519_005148.]: Samplename: Sample#3 Root: 57.835 --> Root in between the borders! Added to results. Hyperbolic solved: 11.2415690196975 [20250519_005148.]: Samplename: Sample#4 Root: 11.242 --> Root in between the borders! Added to results. Hyperbolic solved: 23.5440570752846 [20250519_005148.]: Samplename: Sample#5 Root: 23.544 --> Root in between the borders! Added to results. Hyperbolic solved: 24.7858714956021 [20250519_005148.]: Samplename: Sample#6 Root: 24.786 --> Root in between the borders! Added to results. Hyperbolic solved: 46.4156211496502 [20250519_005148.]: Samplename: Sample#7 Root: 46.416 --> Root in between the borders! Added to results. Hyperbolic solved: 84.4614182359021 [20250519_005148.]: Samplename: Sample#8 Root: 84.461 --> Root in between the borders! Added to results. Hyperbolic solved: -1.41454023853738 [20250519_005148.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -1.415 --> '-10 < root < 0' --> substitute 0 [20250519_005148.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 76.6628425084681 [20250519_005148.]: Samplename: Sample#1 Root: 76.663 --> Root in between the borders! Added to results. Hyperbolic solved: 31.002922067723 [20250519_005148.]: Samplename: Sample#10 Root: 31.003 --> Root in between the borders! Added to results. Hyperbolic solved: 42.8794162472613 [20250519_005148.]: Samplename: Sample#2 Root: 42.879 --> Root in between the borders! Added to results. Hyperbolic solved: 59.1562251576939 [20250519_005148.]: Samplename: Sample#3 Root: 59.156 --> Root in between the borders! Added to results. Hyperbolic solved: 4.04613842773285 [20250519_005148.]: Samplename: Sample#4 Root: 4.046 --> Root in between the borders! Added to results. Hyperbolic solved: 19.9574827771543 [20250519_005148.]: Samplename: Sample#5 Root: 19.957 --> Root in between the borders! Added to results. Hyperbolic solved: 17.4372350357645 [20250519_005148.]: Samplename: Sample#6 Root: 17.437 --> Root in between the borders! Added to results. Hyperbolic solved: 41.0968872148931 [20250519_005148.]: Samplename: Sample#7 Root: 41.097 --> Root in between the borders! Added to results. Hyperbolic solved: 85.7111018674234 [20250519_005148.]: Samplename: Sample#8 Root: 85.711 --> Root in between the borders! Added to results. Hyperbolic solved: 2.08554114625032 [20250519_005148.]: Samplename: Sample#9 Root: 2.086 --> Root in between the borders! Added to results. [20250519_005148.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 74.5413598656994 [20250519_005148.]: Samplename: Sample#1 Root: 74.541 --> Root in between the borders! Added to results. Hyperbolic solved: 28.3514323412579 [20250519_005148.]: Samplename: Sample#10 Root: 28.351 --> Root in between the borders! Added to results. Hyperbolic solved: 42.6007641487945 [20250519_005148.]: Samplename: Sample#2 Root: 42.601 --> Root in between the borders! Added to results. Hyperbolic solved: 56.3207791919885 [20250519_005148.]: Samplename: Sample#3 Root: 56.321 --> Root in between the borders! Added to results. Hyperbolic solved: 7.98800399083 [20250519_005148.]: Samplename: Sample#4 Root: 7.988 --> Root in between the borders! Added to results. Hyperbolic solved: 24.6963927948962 [20250519_005148.]: Samplename: Sample#5 Root: 24.696 --> Root in between the borders! Added to results. Hyperbolic solved: 26.8806215804592 [20250519_005148.]: Samplename: Sample#6 Root: 26.881 --> Root in between the borders! Added to results. Hyperbolic solved: 44.8239489304102 [20250519_005148.]: Samplename: Sample#7 Root: 44.824 --> Root in between the borders! Added to results. Hyperbolic solved: 84.669654917361 [20250519_005148.]: Samplename: Sample#8 Root: 84.67 --> Root in between the borders! Added to results. Hyperbolic solved: -1.26160051687973 [20250519_005148.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -1.262 --> '-10 < root < 0' --> substitute 0 [20250519_005148.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 75.8530019120299 [20250519_005148.]: Samplename: Sample#1 Root: 75.853 --> Root in between the borders! Added to results. Hyperbolic solved: 29.0711677507654 [20250519_005148.]: Samplename: Sample#10 Root: 29.071 --> Root in between the borders! Added to results. Hyperbolic solved: 44.0485537238583 [20250519_005148.]: Samplename: Sample#2 Root: 44.049 --> Root in between the borders! Added to results. Hyperbolic solved: 58.7878536128746 [20250519_005148.]: Samplename: Sample#3 Root: 58.788 --> Root in between the borders! Added to results. Hyperbolic solved: 11.0370782280591 [20250519_005148.]: Samplename: Sample#4 Root: 11.037 --> Root in between the borders! Added to results. Hyperbolic solved: 23.0042107924666 [20250519_005148.]: Samplename: Sample#5 Root: 23.004 --> Root in between the borders! Added to results. Hyperbolic solved: 27.9521038368967 [20250519_005148.]: Samplename: Sample#6 Root: 27.952 --> Root in between the borders! Added to results. Hyperbolic solved: 42.50025622677 [20250519_005148.]: Samplename: Sample#7 Root: 42.5 --> Root in between the borders! Added to results. Hyperbolic solved: 84.6870942263369 [20250519_005148.]: Samplename: Sample#8 Root: 84.687 --> Root in between the borders! Added to results. Hyperbolic solved: 3.01041300417756 [20250519_005148.]: Samplename: Sample#9 Root: 3.01 --> Root in between the borders! Added to results. [20250519_005148.]: Solving cubic regression for CpG#5 Coefficients: -47.8373333333333Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_005148.]: Samplename: Sample#1 Root: 73.686 --> Root in between the borders! Added to results. Coefficients: -13.588Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_005148.]: Samplename: Sample#10 Root: 28.906 --> Root in between the borders! Added to results. Coefficients: -25.3211428571429Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_005148.]: Samplename: Sample#2 Root: 46.761 --> Root in between the borders! Added to results. Coefficients: -32.064Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_005148.]: Samplename: Sample#3 Root: 55.588 --> Root in between the borders! Added to results. Coefficients: -4.074Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_005148.]: Samplename: Sample#4 Root: 10.297 --> Root in between the borders! Added to results. Coefficients: -11.434Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_005148.]: Samplename: Sample#5 Root: 25.131 --> Root in between the borders! Added to results. Coefficients: -13.294Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_005148.]: Samplename: Sample#6 Root: 28.402 --> Root in between the borders! Added to results. Coefficients: -24.288Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_005148.]: Samplename: Sample#7 Root: 45.332 --> Root in between the borders! Added to results. Coefficients: -63.134Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_005148.]: Samplename: Sample#8 Root: 88.909 --> Root in between the borders! Added to results. Coefficients: 0.0360000000000005Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_005148.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.102 --> '-10 < root < 0' --> substitute 0 [20250519_005148.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 79.2062097176894 [20250519_005148.]: Samplename: Sample#1 Root: 79.206 --> Root in between the borders! Added to results. Hyperbolic solved: 30.234912908094 [20250519_005148.]: Samplename: Sample#10 Root: 30.235 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8992139020538 [20250519_005148.]: Samplename: Sample#2 Root: 41.899 --> Root in between the borders! Added to results. Hyperbolic solved: 56.8778124304882 [20250519_005148.]: Samplename: Sample#3 Root: 56.878 --> Root in between the borders! Added to results. Hyperbolic solved: 8.80900690226663 [20250519_005148.]: Samplename: Sample#4 Root: 8.809 --> Root in between the borders! Added to results. Hyperbolic solved: 18.6793858198319 [20250519_005148.]: Samplename: Sample#5 Root: 18.679 --> Root in between the borders! Added to results. Hyperbolic solved: 29.9638526705022 [20250519_005148.]: Samplename: Sample#6 Root: 29.964 --> Root in between the borders! Added to results. Hyperbolic solved: 42.8669225173731 [20250519_005148.]: Samplename: Sample#7 Root: 42.867 --> Root in between the borders! Added to results. Hyperbolic solved: 86.6206311389839 [20250519_005148.]: Samplename: Sample#8 Root: 86.621 --> Root in between the borders! Added to results. Hyperbolic solved: 1.38882504570857 [20250519_005148.]: Samplename: Sample#9 Root: 1.389 --> Root in between the borders! Added to results. [20250519_005148.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 77.5203002291111 [20250519_005148.]: Samplename: Sample#1 Root: 77.52 --> Root in between the borders! Added to results. Hyperbolic solved: 27.0810020209347 [20250519_005148.]: Samplename: Sample#10 Root: 27.081 --> Root in between the borders! Added to results. Hyperbolic solved: 48.4274819445682 [20250519_005148.]: Samplename: Sample#2 Root: 48.427 --> Root in between the borders! Added to results. Hyperbolic solved: 58.8711296078074 [20250519_005148.]: Samplename: Sample#3 Root: 58.871 --> Root in between the borders! Added to results. Hyperbolic solved: 13.3245830390029 [20250519_005148.]: Samplename: Sample#4 Root: 13.325 --> Root in between the borders! Added to results. Hyperbolic solved: 26.9731029843141 [20250519_005148.]: Samplename: Sample#5 Root: 26.973 --> Root in between the borders! Added to results. Hyperbolic solved: 30.9519756409676 [20250519_005148.]: Samplename: Sample#6 Root: 30.952 --> Root in between the borders! Added to results. Hyperbolic solved: 45.7349250650843 [20250519_005148.]: Samplename: Sample#7 Root: 45.735 --> Root in between the borders! Added to results. Hyperbolic solved: 84.5977214697661 [20250519_005148.]: Samplename: Sample#8 Root: 84.598 --> Root in between the borders! Added to results. Hyperbolic solved: -2.87252278311384 [20250519_005148.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.873 --> '-10 < root < 0' --> substitute 0 [20250519_005148.]: Solving cubic regression for CpG#8 Coefficients: -55.3573333333333Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_005148.]: Samplename: Sample#1 Root: 74.173 --> Root in between the borders! Added to results. Coefficients: -17.574Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_005148.]: Samplename: Sample#10 Root: 30.7 --> Root in between the borders! Added to results. Coefficients: -22.9425714285714Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_005148.]: Samplename: Sample#2 Root: 38.106 --> Root in between the borders! Added to results. Coefficients: -42.849Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_005148.]: Samplename: Sample#3 Root: 61.571 --> Root in between the borders! Added to results. Coefficients: -4.604Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_005149.]: Samplename: Sample#4 Root: 9.446 --> Root in between the borders! Added to results. Coefficients: -11.389Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_005149.]: Samplename: Sample#5 Root: 21.295 --> Root in between the borders! Added to results. Coefficients: -25.784Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_005149.]: Samplename: Sample#6 Root: 41.8 --> Root in between the borders! Added to results. Coefficients: -30.746Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_005149.]: Samplename: Sample#7 Root: 47.937 --> Root in between the borders! Added to results. Coefficients: -66.912Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_005149.]: Samplename: Sample#8 Root: 84.816 --> Root in between the borders! Added to results. Coefficients: 3.176Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_005149.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -7.574 --> '-10 < root < 0' --> substitute 0 [20250519_005149.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 80.5447416574246 [20250519_005149.]: Samplename: Sample#1 Root: 80.545 --> Root in between the borders! Added to results. Hyperbolic solved: 27.8054725532271 [20250519_005149.]: Samplename: Sample#10 Root: 27.805 --> Root in between the borders! Added to results. Hyperbolic solved: 46.257978181877 [20250519_005149.]: Samplename: Sample#2 Root: 46.258 --> Root in between the borders! Added to results. Hyperbolic solved: 57.1842723611193 [20250519_005149.]: Samplename: Sample#3 Root: 57.184 --> Root in between the borders! Added to results. Hyperbolic solved: 8.63689944440251 [20250519_005149.]: Samplename: Sample#4 Root: 8.637 --> Root in between the borders! Added to results. Hyperbolic solved: 24.2116725772085 [20250519_005149.]: Samplename: Sample#5 Root: 24.212 --> Root in between the borders! Added to results. Hyperbolic solved: 39.6334888107959 [20250519_005149.]: Samplename: Sample#6 Root: 39.633 --> Root in between the borders! Added to results. Hyperbolic solved: 44.3019478766814 [20250519_005149.]: Samplename: Sample#7 Root: 44.302 --> Root in between the borders! Added to results. Hyperbolic solved: 87.3231552867261 [20250519_005149.]: Samplename: Sample#8 Root: 87.323 --> Root in between the borders! Added to results. Hyperbolic solved: -1.1792994162273 [20250519_005149.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -1.179 --> '-10 < root < 0' --> substitute 0 [20250519_005149.]: Solving hyperbolic regression for row_means Hyperbolic solved: 76.7712420256811 [20250519_005149.]: Samplename: Sample#1 Root: 76.771 --> Root in between the borders! Added to results. Hyperbolic solved: 28.8491793177963 [20250519_005149.]: Samplename: Sample#10 Root: 28.849 --> Root in between the borders! Added to results. Hyperbolic solved: 43.0342096405004 [20250519_005149.]: Samplename: Sample#2 Root: 43.034 --> Root in between the borders! Added to results. Hyperbolic solved: 57.634070615517 [20250519_005149.]: Samplename: Sample#3 Root: 57.634 --> Root in between the borders! Added to results. Hyperbolic solved: 8.87182398849477 [20250519_005149.]: Samplename: Sample#4 Root: 8.872 --> Root in between the borders! Added to results. Hyperbolic solved: 22.1989155573519 [20250519_005149.]: Samplename: Sample#5 Root: 22.199 --> Root in between the borders! Added to results. Hyperbolic solved: 29.2140219298625 [20250519_005149.]: Samplename: Sample#6 Root: 29.214 --> Root in between the borders! Added to results. Hyperbolic solved: 43.9371957594463 [20250519_005149.]: Samplename: Sample#7 Root: 43.937 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6706924479325 [20250519_005149.]: Samplename: Sample#8 Root: 85.671 --> Root in between the borders! Added to results. Hyperbolic solved: -0.55137023392917 [20250519_005149.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.551 --> '-10 < root < 0' --> substitute 0 [20250519_005149.]: Entered 'solving_equations'-Function [20250519_005149.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 0 [20250519_005149.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 14.1480167109277 [20250519_005149.]: Samplename: 12.5 Root: 14.148 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1398443790446 [20250519_005149.]: Samplename: 25 Root: 26.14 --> Root in between the borders! Added to results. Hyperbolic solved: 39.3762039523382 [20250519_005149.]: Samplename: 37.5 Root: 39.376 --> Root in between the borders! Added to results. Hyperbolic solved: 52.9476244028783 [20250519_005149.]: Samplename: 50 Root: 52.948 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4195105873419 [20250519_005149.]: Samplename: 62.5 Root: 65.42 --> Root in between the borders! Added to results. Hyperbolic solved: 74.433837698331 [20250519_005149.]: Samplename: 75 Root: 74.434 --> Root in between the borders! Added to results. Hyperbolic solved: 80.5559264925808 [20250519_005149.]: Samplename: 87.5 Root: 80.556 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_005149.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_005149.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 0 [20250519_005149.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 10.7812303822548 [20250519_005149.]: Samplename: 12.5 Root: 10.781 --> Root in between the borders! Added to results. Hyperbolic solved: 26.0648314401535 [20250519_005149.]: Samplename: 25 Root: 26.065 --> Root in between the borders! Added to results. Hyperbolic solved: 35.1980950192158 [20250519_005149.]: Samplename: 37.5 Root: 35.198 --> Root in between the borders! Added to results. Hyperbolic solved: 47.9203836090837 [20250519_005149.]: Samplename: 50 Root: 47.92 --> Root in between the borders! Added to results. Hyperbolic solved: 67.275750545092 [20250519_005149.]: Samplename: 62.5 Root: 67.276 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7278143684244 [20250519_005149.]: Samplename: 75 Root: 75.728 --> Root in between the borders! Added to results. Hyperbolic solved: 84.1258428690749 [20250519_005149.]: Samplename: 87.5 Root: 84.126 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_005149.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_005149.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 0 [20250519_005149.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 10.8467198180169 [20250519_005149.]: Samplename: 12.5 Root: 10.847 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1449700612555 [20250519_005149.]: Samplename: 25 Root: 26.145 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2865760013773 [20250519_005149.]: Samplename: 37.5 Root: 37.287 --> Root in between the borders! Added to results. Hyperbolic solved: 51.4114078772697 [20250519_005149.]: Samplename: 50 Root: 51.411 --> Root in between the borders! Added to results. Hyperbolic solved: 65.0139634978039 [20250519_005149.]: Samplename: 62.5 Root: 65.014 --> Root in between the borders! Added to results. Hyperbolic solved: 76.9921394830877 [20250519_005149.]: Samplename: 75 Root: 76.992 --> Root in between the borders! Added to results. Hyperbolic solved: 79.6808818589171 [20250519_005149.]: Samplename: 87.5 Root: 79.681 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_005149.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_005149.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 0 [20250519_005149.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 13.2494913489925 [20250519_005149.]: Samplename: 12.5 Root: 13.249 --> Root in between the borders! Added to results. Hyperbolic solved: 25.0914701740206 [20250519_005149.]: Samplename: 25 Root: 25.091 --> Root in between the borders! Added to results. Hyperbolic solved: 38.8081740444332 [20250519_005149.]: Samplename: 37.5 Root: 38.808 --> Root in between the borders! Added to results. Hyperbolic solved: 49.1133034150896 [20250519_005149.]: Samplename: 50 Root: 49.113 --> Root in between the borders! Added to results. Hyperbolic solved: 67.573566028728 [20250519_005149.]: Samplename: 62.5 Root: 67.574 --> Root in between the borders! Added to results. Hyperbolic solved: 73.7655861480302 [20250519_005149.]: Samplename: 75 Root: 73.766 --> Root in between the borders! Added to results. Hyperbolic solved: 82.0404940898918 [20250519_005149.]: Samplename: 87.5 Root: 82.04 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_005149.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_005149.]: Solving cubic regression for CpG#5 Coefficients: 0Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_005149.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -4.144Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_005149.]: Samplename: 12.5 Root: 10.457 --> Root in between the borders! Added to results. Coefficients: -12.102Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_005149.]: Samplename: 25 Root: 26.323 --> Root in between the borders! Added to results. Coefficients: -20.536Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_005149.]: Samplename: 37.5 Root: 39.937 --> Root in between the borders! Added to results. Coefficients: -30.0715Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_005149.]: Samplename: 50 Root: 53.063 --> Root in between the borders! Added to results. Coefficients: -39.034Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_005149.]: Samplename: 62.5 Root: 63.959 --> Root in between the borders! Added to results. Coefficients: -51.059Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_005149.]: Samplename: 75 Root: 77.051 --> Root in between the borders! Added to results. Coefficients: -60.3906666666667Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_005149.]: Samplename: 87.5 Root: 86.31 --> Root in between the borders! Added to results. Coefficients: -75.446Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_005149.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_005149.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 0 [20250519_005149.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 11.8139244542506 [20250519_005149.]: Samplename: 12.5 Root: 11.814 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5330423964606 [20250519_005149.]: Samplename: 25 Root: 26.533 --> Root in between the borders! Added to results. Hyperbolic solved: 35.365455365759 [20250519_005149.]: Samplename: 37.5 Root: 35.365 --> Root in between the borders! Added to results. Hyperbolic solved: 50.1054345020203 [20250519_005149.]: Samplename: 50 Root: 50.105 --> Root in between the borders! Added to results. Hyperbolic solved: 64.9683746571925 [20250519_005149.]: Samplename: 62.5 Root: 64.968 --> Root in between the borders! Added to results. Hyperbolic solved: 73.6331726521876 [20250519_005149.]: Samplename: 75 Root: 73.633 --> Root in between the borders! Added to results. Hyperbolic solved: 86.9938602772804 [20250519_005149.]: Samplename: 87.5 Root: 86.994 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_005149.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_005149.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 0 [20250519_005149.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 11.7880491100164 [20250519_005149.]: Samplename: 12.5 Root: 11.788 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1959234034328 [20250519_005149.]: Samplename: 25 Root: 26.196 --> Root in between the borders! Added to results. Hyperbolic solved: 39.1978567589215 [20250519_005149.]: Samplename: 37.5 Root: 39.198 --> Root in between the borders! Added to results. Hyperbolic solved: 54.3513505291649 [20250519_005149.]: Samplename: 50 Root: 54.351 --> Root in between the borders! Added to results. Hyperbolic solved: 66.0567953704493 [20250519_005149.]: Samplename: 62.5 Root: 66.057 --> Root in between the borders! Added to results. Hyperbolic solved: 75.1900867269869 [20250519_005149.]: Samplename: 75 Root: 75.19 --> Root in between the borders! Added to results. Hyperbolic solved: 78.6051660349008 [20250519_005149.]: Samplename: 87.5 Root: 78.605 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_005149.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_005149.]: Solving cubic regression for CpG#8 Coefficients: 0Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_005149.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -4.35066666666667Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_005149.]: Samplename: 12.5 Root: 8.961 --> Root in between the borders! Added to results. Coefficients: -15.834Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_005149.]: Samplename: 25 Root: 28.16 --> Root in between the borders! Added to results. Coefficients: -22.254Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_005149.]: Samplename: 37.5 Root: 37.189 --> Root in between the borders! Added to results. Coefficients: -36.529Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_005149.]: Samplename: 50 Root: 54.661 --> Root in between the borders! Added to results. Coefficients: -47.73Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_005149.]: Samplename: 62.5 Root: 66.642 --> Root in between the borders! Added to results. Coefficients: -60.5715Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_005149.]: Samplename: 75 Root: 79.079 --> Root in between the borders! Added to results. Coefficients: -63.414Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_005149.]: Samplename: 87.5 Root: 81.681 --> Root in between the borders! Added to results. Coefficients: -84.964Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_005149.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_005149.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 0 [20250519_005149.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 12.2068234201026 [20250519_005149.]: Samplename: 12.5 Root: 12.207 --> Root in between the borders! Added to results. Hyperbolic solved: 28.0688737636578 [20250519_005149.]: Samplename: 25 Root: 28.069 --> Root in between the borders! Added to results. Hyperbolic solved: 37.6661823177513 [20250519_005149.]: Samplename: 37.5 Root: 37.666 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3684233460174 [20250519_005149.]: Samplename: 50 Root: 52.368 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8636916649907 [20250519_005149.]: Samplename: 62.5 Root: 64.864 --> Root in between the borders! Added to results. Hyperbolic solved: 74.2551330907062 [20250519_005149.]: Samplename: 75 Root: 74.255 --> Root in between the borders! Added to results. Hyperbolic solved: 83.9343288872042 [20250519_005149.]: Samplename: 87.5 Root: 83.934 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_005149.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_005149.]: Solving hyperbolic regression for row_means Hyperbolic solved: 0.000228160853766634 [20250519_005149.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 11.1587839347378 [20250519_005149.]: Samplename: 12.5 Root: 11.159 --> Root in between the borders! Added to results. Hyperbolic solved: 25.8570776700364 [20250519_005149.]: Samplename: 25 Root: 25.857 --> Root in between the borders! Added to results. Hyperbolic solved: 37.065004454281 [20250519_005149.]: Samplename: 37.5 Root: 37.065 --> Root in between the borders! Added to results. Hyperbolic solved: 51.1881763075298 [20250519_005149.]: Samplename: 50 Root: 51.188 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4439754555105 [20250519_005149.]: Samplename: 62.5 Root: 65.444 --> Root in between the borders! Added to results. Hyperbolic solved: 75.3005883283082 [20250519_005149.]: Samplename: 75 Root: 75.301 --> Root in between the borders! Added to results. Hyperbolic solved: 82.6591025877361 [20250519_005149.]: Samplename: 87.5 Root: 82.659 --> Root in between the borders! Added to results. Hyperbolic solved: 100.000153506599 [20250519_005149.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 [20250519_005151.]: on_start: using future::plan("sequential") [20250519_005151.]: Entered 'clean_dt'-Function [20250519_005151.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_005151.]: got experimental data [20250519_005151.]: Entered 'clean_dt'-Function [20250519_005151.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_005151.]: got calibration data [20250519_005151.]: ### Starting with regression calculations ### [20250519_005151.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_005151.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005151.]: Logging df_agg: CpG#1 [20250519_005151.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005151.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005151.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005151.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005151.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005151.]: Entered 'hyperbolic_regression'-Function [20250519_005151.]: 'hyperbolic_regression': minmax = FALSE [20250519_005152.]: Entered 'cubic_regression'-Function [20250519_005152.]: 'cubic_regression': minmax = FALSE [20250519_005152.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005152.]: Logging df_agg: CpG#2 [20250519_005152.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005152.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005152.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005152.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005152.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005152.]: Entered 'hyperbolic_regression'-Function [20250519_005152.]: 'hyperbolic_regression': minmax = FALSE [20250519_005152.]: Entered 'cubic_regression'-Function [20250519_005152.]: 'cubic_regression': minmax = FALSE [20250519_005152.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005152.]: Logging df_agg: CpG#3 [20250519_005152.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005152.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005152.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005152.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005152.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005152.]: Entered 'hyperbolic_regression'-Function [20250519_005152.]: 'hyperbolic_regression': minmax = FALSE [20250519_005152.]: Entered 'cubic_regression'-Function [20250519_005152.]: 'cubic_regression': minmax = FALSE [20250519_005152.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005152.]: Logging df_agg: CpG#4 [20250519_005152.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005152.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005152.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005152.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005152.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005152.]: Entered 'hyperbolic_regression'-Function [20250519_005152.]: 'hyperbolic_regression': minmax = FALSE [20250519_005152.]: Entered 'cubic_regression'-Function [20250519_005152.]: 'cubic_regression': minmax = FALSE [20250519_005153.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005153.]: Logging df_agg: CpG#5 [20250519_005153.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005153.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005153.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005153.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005153.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005153.]: Entered 'hyperbolic_regression'-Function [20250519_005153.]: 'hyperbolic_regression': minmax = FALSE [20250519_005153.]: Entered 'cubic_regression'-Function [20250519_005153.]: 'cubic_regression': minmax = FALSE [20250519_005153.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005153.]: Logging df_agg: CpG#6 [20250519_005153.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005153.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005153.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005153.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005153.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005153.]: Entered 'hyperbolic_regression'-Function [20250519_005153.]: 'hyperbolic_regression': minmax = FALSE [20250519_005153.]: Entered 'cubic_regression'-Function [20250519_005153.]: 'cubic_regression': minmax = FALSE [20250519_005153.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005153.]: Logging df_agg: CpG#7 [20250519_005153.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005153.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005153.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005153.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005153.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005153.]: Entered 'hyperbolic_regression'-Function [20250519_005153.]: 'hyperbolic_regression': minmax = FALSE [20250519_005153.]: Entered 'cubic_regression'-Function [20250519_005153.]: 'cubic_regression': minmax = FALSE [20250519_005153.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005153.]: Logging df_agg: CpG#8 [20250519_005153.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005153.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005153.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005153.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005153.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005153.]: Entered 'hyperbolic_regression'-Function [20250519_005153.]: 'hyperbolic_regression': minmax = FALSE [20250519_005154.]: Entered 'cubic_regression'-Function [20250519_005154.]: 'cubic_regression': minmax = FALSE [20250519_005154.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005154.]: Logging df_agg: CpG#9 [20250519_005154.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005154.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005154.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005154.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005154.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005154.]: Entered 'hyperbolic_regression'-Function [20250519_005154.]: 'hyperbolic_regression': minmax = FALSE [20250519_005154.]: Entered 'cubic_regression'-Function [20250519_005154.]: 'cubic_regression': minmax = FALSE [20250519_005154.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723122, 2.47760998435345)c(5.94911111111111, 0.84962962962963, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703704, 19.6995555555555, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005154.]: Logging df_agg: row_means [20250519_005154.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005154.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005154.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723122, 2.47760998435345)[20250519_005154.]: c(5.94911111111111, 0.84962962962963, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005154.]: c(NA, 6.79703703703704, 19.6995555555555, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005154.]: Entered 'hyperbolic_regression'-Function [20250519_005154.]: 'hyperbolic_regression': minmax = FALSE [20250519_005154.]: Entered 'cubic_regression'-Function [20250519_005154.]: 'cubic_regression': minmax = FALSE [20250519_005155.]: ### Starting with plotting ### [20250519_005155.]: Creating plot No. 1 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG1.png [20250519_005155.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -937.719419567728 , a = -108.56796364622 , b = -937.719419567728 , d = -232.057111323599 [20250519_005155.]: # CpG-site: CpG#1 Cubic: Using a = 6.53413423120091e-05 , b = -0.0055806968734969 , c = 0.784061853455188 , d = 1.93182659932656 [20250519_005158.]: Creating plot No. 2 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG2.png [20250519_005158.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = -2393.1426335766 , a = -141.862264327279 , b = -2393.1426335766 , d = -268.331583599043 [20250519_005158.]: # CpG-site: CpG#2 Cubic: Using a = 8.04237934904601e-06 , b = 0.00293158941798942 , c = 0.514821742825076 , d = 9.27667003367003 [20250519_005200.]: Creating plot No. 3 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG3.png [20250519_005200.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = -1477.46855437885 , a = -170.424507883158 , b = -1477.46855437885 , d = -327.190157171162 [20250519_005200.]: # CpG-site: CpG#3 Cubic: Using a = 2.30555869809204e-05 , b = -0.000797009331409346 , c = 0.62783129228796 , d = 3.88705218855218 [20250519_005202.]: Creating plot No. 4 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG4.png [20250519_005202.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = -2115.87792930847 , a = -162.169768036114 , b = -2115.87792930847 , d = -299.662268971294 [20250519_005202.]: # CpG-site: CpG#4 Cubic: Using a = 3.2166356902357e-05 , b = -0.0015913142857143 , c = 0.697398364598366 , d = 6.17055050505048 [20250519_005204.]: Creating plot No. 5 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG5.png [20250519_005204.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = -1383.32738741788 , a = -151.440067063617 , b = -1383.32738741788 , d = -304.200253800186 [20250519_005204.]: # CpG-site: CpG#5 Cubic: Using a = -4.48459708193036e-06 , b = 0.00375371236171235 , c = 0.422446384479718 , d = 5.17203872053872 [20250519_005207.]: Creating plot No. 6 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG6.png [20250519_005207.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = -1550.59307603208 , a = -125.016146072952 , b = -1550.59307603208 , d = -247.279679418643 [20250519_005207.]: # CpG-site: CpG#6 Cubic: Using a = 2.38852884399552e-05 , b = 0.000918637037037021 , c = 0.561022132435467 , d = 6.1479276094276 [20250519_005209.]: Creating plot No. 7 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG7.png [20250519_005209.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -772.844192345493 , a = -75.2724875322984 , b = -772.844192345493 , d = -226.673147125363 [20250519_005209.]: # CpG-site: CpG#7 Cubic: Using a = 4.98010505050505e-05 , b = -0.00436152150072151 , c = 0.579588917748918 , d = 1.72104545454544 [20250519_005211.]: Creating plot No. 8 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG8.png [20250519_005211.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = -3872.73616547768 , a = -278.017275815885 , b = -3872.73616547768 , d = -438.894268920994 [20250519_005211.]: # CpG-site: CpG#8 Cubic: Using a = -1.50060965207632e-05 , b = 0.00447978143338143 , c = 0.534897737694404 , d = 9.56981481481482 [20250519_005213.]: Creating plot No. 9 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG9.png [20250519_005213.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -843.925451494751 , a = -70.6796095478879 , b = -843.925451494751 , d = -184.482153986057 [20250519_005213.]: # CpG-site: CpG#9 Cubic: Using a = 7.72300426487093e-05 , b = -0.00542281173641174 , c = 0.67899229597563 , d = 2.72413468013467 [20250519_005215.]: Creating plot No. 10 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_rowmeans.png [20250519_005215.]: # CpG-site: row_means Hyperbolic: Using bias_weight = -1525.75758254501 , a = -127.67501720702 , b = -1525.75758254501 , d = -263.003030149428 [20250519_005215.]: # CpG-site: row_means Cubic: Using a = 2.88923726150392e-05 , b = -0.0006299592752926 , c = 0.600117857944524 , d = 5.17789562289563 [20250519_005218.]: Entered 'solving_equations'-Function [20250519_005218.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: -2.23222990163966 [20250519_005218.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.232 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.1698489850618 [20250519_005218.]: Samplename: 12.5 Root: 12.17 --> Root in between the borders! Added to results. Hyperbolic solved: 24.4781920312644 [20250519_005218.]: Samplename: 25 Root: 24.478 --> Root in between the borders! Added to results. Hyperbolic solved: 38.173044740918 [20250519_005218.]: Samplename: 37.5 Root: 38.173 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3349371964438 [20250519_005218.]: Samplename: 50 Root: 52.335 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4582773627666 [20250519_005218.]: Samplename: 62.5 Root: 65.458 --> Root in between the borders! Added to results. Hyperbolic solved: 75.0090795260796 [20250519_005218.]: Samplename: 75 Root: 75.009 --> Root in between the borders! Added to results. Hyperbolic solved: 81.5271920968417 [20250519_005218.]: Samplename: 87.5 Root: 81.527 --> Root in between the borders! Added to results. Hyperbolic solved: 102.400893095062 [20250519_005218.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.401 --> '100 < root < 110' --> substitute 100 [20250519_005218.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 1.13660501904968 [20250519_005218.]: Samplename: 0 Root: 1.137 --> Root in between the borders! Added to results. Hyperbolic solved: 11.4129696733689 [20250519_005218.]: Samplename: 12.5 Root: 11.413 --> Root in between the borders! Added to results. Hyperbolic solved: 26.174000526428 [20250519_005218.]: Samplename: 25 Root: 26.174 --> Root in between the borders! Added to results. Hyperbolic solved: 35.1050449117028 [20250519_005218.]: Samplename: 37.5 Root: 35.105 --> Root in between the borders! Added to results. Hyperbolic solved: 47.685500330611 [20250519_005218.]: Samplename: 50 Root: 47.686 --> Root in between the borders! Added to results. Hyperbolic solved: 67.1440494417104 [20250519_005218.]: Samplename: 62.5 Root: 67.144 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7644668894086 [20250519_005218.]: Samplename: 75 Root: 75.764 --> Root in between the borders! Added to results. Hyperbolic solved: 84.4054158616395 [20250519_005218.]: Samplename: 87.5 Root: 84.405 --> Root in between the borders! Added to results. Hyperbolic solved: 100.94827248399 [20250519_005218.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.948 --> '100 < root < 110' --> substitute 100 [20250519_005218.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 0.51235653688495 [20250519_005218.]: Samplename: 0 Root: 0.512 --> Root in between the borders! Added to results. Hyperbolic solved: 10.7523884294604 [20250519_005218.]: Samplename: 12.5 Root: 10.752 --> Root in between the borders! Added to results. Hyperbolic solved: 25.5218907947761 [20250519_005218.]: Samplename: 25 Root: 25.522 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5270462675211 [20250519_005218.]: Samplename: 37.5 Root: 36.527 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7909245028224 [20250519_005218.]: Samplename: 50 Root: 50.791 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8686317550184 [20250519_005218.]: Samplename: 62.5 Root: 64.869 --> Root in between the borders! Added to results. Hyperbolic solved: 77.5524188495235 [20250519_005218.]: Samplename: 75 Root: 77.552 --> Root in between the borders! Added to results. Hyperbolic solved: 80.4374617358174 [20250519_005218.]: Samplename: 87.5 Root: 80.437 --> Root in between the borders! Added to results. Hyperbolic solved: 102.704024900825 [20250519_005218.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.704 --> '100 < root < 110' --> substitute 100 [20250519_005218.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: -0.519503092357606 [20250519_005218.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.52 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.4934147844872 [20250519_005218.]: Samplename: 12.5 Root: 12.493 --> Root in between the borders! Added to results. Hyperbolic solved: 24.2685420024115 [20250519_005218.]: Samplename: 25 Root: 24.269 --> Root in between the borders! Added to results. Hyperbolic solved: 38.0817128465023 [20250519_005218.]: Samplename: 37.5 Root: 38.082 --> Root in between the borders! Added to results. Hyperbolic solved: 48.5843181174811 [20250519_005218.]: Samplename: 50 Root: 48.584 --> Root in between the borders! Added to results. Hyperbolic solved: 67.6722399183037 [20250519_005218.]: Samplename: 62.5 Root: 67.672 --> Root in between the borders! Added to results. Hyperbolic solved: 74.1549277799119 [20250519_005218.]: Samplename: 75 Root: 74.155 --> Root in between the borders! Added to results. Hyperbolic solved: 82.8821797890026 [20250519_005218.]: Samplename: 87.5 Root: 82.882 --> Root in between the borders! Added to results. Hyperbolic solved: 102.0791269023 [20250519_005218.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.079 --> '100 < root < 110' --> substitute 100 [20250519_005218.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 2.41558626275183 [20250519_005218.]: Samplename: 0 Root: 2.416 --> Root in between the borders! Added to results. Hyperbolic solved: 10.1649674907454 [20250519_005218.]: Samplename: 12.5 Root: 10.165 --> Root in between the borders! Added to results. Hyperbolic solved: 23.9830820412762 [20250519_005218.]: Samplename: 25 Root: 23.983 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2773619900429 [20250519_005218.]: Samplename: 37.5 Root: 37.277 --> Root in between the borders! Added to results. Hyperbolic solved: 50.8659386543864 [20250519_005218.]: Samplename: 50 Root: 50.866 --> Root in between the borders! Added to results. Hyperbolic solved: 62.4342273571069 [20250519_005218.]: Samplename: 62.5 Root: 62.434 --> Root in between the borders! Added to results. Hyperbolic solved: 76.3915260534323 [20250519_005218.]: Samplename: 75 Root: 76.392 --> Root in between the borders! Added to results. Hyperbolic solved: 86.159788778566 [20250519_005218.]: Samplename: 87.5 Root: 86.16 --> Root in between the borders! Added to results. Hyperbolic solved: 100.267759893323 [20250519_005218.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.268 --> '100 < root < 110' --> substitute 100 [20250519_005218.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 0.138163748613034 [20250519_005218.]: Samplename: 0 Root: 0.138 --> Root in between the borders! Added to results. Hyperbolic solved: 11.8635558881981 [20250519_005218.]: Samplename: 12.5 Root: 11.864 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5107449550797 [20250519_005218.]: Samplename: 25 Root: 26.511 --> Root in between the borders! Added to results. Hyperbolic solved: 35.3205073050661 [20250519_005218.]: Samplename: 37.5 Root: 35.321 --> Root in between the borders! Added to results. Hyperbolic solved: 50.0570767570666 [20250519_005218.]: Samplename: 50 Root: 50.057 --> Root in between the borders! Added to results. Hyperbolic solved: 64.9602944381018 [20250519_005218.]: Samplename: 62.5 Root: 64.96 --> Root in between the borders! Added to results. Hyperbolic solved: 73.66890571617 [20250519_005218.]: Samplename: 75 Root: 73.669 --> Root in between the borders! Added to results. Hyperbolic solved: 87.1266086585036 [20250519_005218.]: Samplename: 87.5 Root: 87.127 --> Root in between the borders! Added to results. Hyperbolic solved: 100.261637014212 [20250519_005218.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.262 --> '100 < root < 110' --> substitute 100 [20250519_005218.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: -1.37238087287012 [20250519_005218.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -1.372 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.1993162352498 [20250519_005218.]: Samplename: 12.5 Root: 10.199 --> Root in between the borders! Added to results. Hyperbolic solved: 24.595178967123 [20250519_005218.]: Samplename: 25 Root: 24.595 --> Root in between the borders! Added to results. Hyperbolic solved: 37.8310421041787 [20250519_005218.]: Samplename: 37.5 Root: 37.831 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5588739724067 [20250519_005218.]: Samplename: 50 Root: 53.559 --> Root in between the borders! Added to results. Hyperbolic solved: 65.9364947980258 [20250519_005218.]: Samplename: 62.5 Root: 65.936 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7361094434913 [20250519_005218.]: Samplename: 75 Root: 75.736 --> Root in between the borders! Added to results. Hyperbolic solved: 79.432823759854 [20250519_005218.]: Samplename: 87.5 Root: 79.433 --> Root in between the borders! Added to results. Hyperbolic solved: 103.004237013737 [20250519_005218.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 103.004 --> '100 < root < 110' --> substitute 100 [20250519_005218.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 2.80068218205093 [20250519_005218.]: Samplename: 0 Root: 2.801 --> Root in between the borders! Added to results. Hyperbolic solved: 9.27535134596596 [20250519_005218.]: Samplename: 12.5 Root: 9.275 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4762621928197 [20250519_005218.]: Samplename: 25 Root: 25.476 --> Root in between the borders! Added to results. Hyperbolic solved: 34.0122075735416 [20250519_005218.]: Samplename: 37.5 Root: 34.012 --> Root in between the borders! Added to results. Hyperbolic solved: 51.7842655662325 [20250519_005218.]: Samplename: 50 Root: 51.784 --> Root in between the borders! Added to results. Hyperbolic solved: 64.6732311906145 [20250519_005218.]: Samplename: 62.5 Root: 64.673 --> Root in between the borders! Added to results. Hyperbolic solved: 78.4326978859189 [20250519_005218.]: Samplename: 75 Root: 78.433 --> Root in between the borders! Added to results. Hyperbolic solved: 81.3427232852719 [20250519_005218.]: Samplename: 87.5 Root: 81.343 --> Root in between the borders! Added to results. Hyperbolic solved: 101.964406640583 [20250519_005218.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.964 --> '100 < root < 110' --> substitute 100 [20250519_005218.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: -2.13403721845678 [20250519_005218.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.134 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.5082192457956 [20250519_005218.]: Samplename: 12.5 Root: 10.508 --> Root in between the borders! Added to results. Hyperbolic solved: 26.9164567253388 [20250519_005218.]: Samplename: 25 Root: 26.916 --> Root in between the borders! Added to results. Hyperbolic solved: 36.8334779159501 [20250519_005218.]: Samplename: 37.5 Root: 36.833 --> Root in between the borders! Added to results. Hyperbolic solved: 52.0097895977263 [20250519_005218.]: Samplename: 50 Root: 52.01 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8930527921581 [20250519_005218.]: Samplename: 62.5 Root: 64.893 --> Root in between the borders! Added to results. Hyperbolic solved: 74.5671055499357 [20250519_005218.]: Samplename: 75 Root: 74.567 --> Root in between the borders! Added to results. Hyperbolic solved: 84.5294954832669 [20250519_005218.]: Samplename: 87.5 Root: 84.529 --> Root in between the borders! Added to results. Hyperbolic solved: 101.047146466811 [20250519_005218.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.047 --> '100 < root < 110' --> substitute 100 [20250519_005218.]: Solving hyperbolic regression for row_means Hyperbolic solved: 0.290940467580009 [20250519_005218.]: Samplename: 0 Root: 0.291 --> Root in between the borders! Added to results. Hyperbolic solved: 11.041240609297 [20250519_005218.]: Samplename: 12.5 Root: 11.041 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4081503210647 [20250519_005218.]: Samplename: 25 Root: 25.408 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5243723188613 [20250519_005218.]: Samplename: 37.5 Root: 36.524 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7348829528732 [20250519_005218.]: Samplename: 50 Root: 50.735 --> Root in between the borders! Added to results. Hyperbolic solved: 65.3135214250794 [20250519_005218.]: Samplename: 62.5 Root: 65.314 --> Root in between the borders! Added to results. Hyperbolic solved: 75.5342711952113 [20250519_005218.]: Samplename: 75 Root: 75.534 --> Root in between the borders! Added to results. Hyperbolic solved: 83.2411229564887 [20250519_005218.]: Samplename: 87.5 Root: 83.241 --> Root in between the borders! Added to results. Hyperbolic solved: 101.666942268285 [20250519_005218.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.667 --> '100 < root < 110' --> substitute 100 [20250519_005218.]: ### Starting with regression calculations ### [20250519_005218.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_005218.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.1698489850618, 24.4781920312644, 38.173044740918, 52.3349371964438, 65.4582773627666, 75.0090795260796, 81.5271920968417, 100)c(0, 0.330151014938201, 0.521807968735601, 0.673044740918002, 2.3349371964438, 2.95827736276659, 0.00907952607960283, 5.9728079031583, 0)c(NA, 2.64120811950561, 2.0872318749424, 1.79478597578134, 4.66987439288761, 4.73324378042655, 0.0121060347728038, 6.82606617503806, 0) [20250519_005218.]: Logging df_agg: CpG#1 [20250519_005218.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005218.]: c(0, 12.1698489850618, 24.4781920312644, 38.173044740918, 52.3349371964438, 65.4582773627666, 75.0090795260796, 81.5271920968417, 100)[20250519_005218.]: c(0, 0.330151014938201, 0.521807968735601, 0.673044740918002, 2.3349371964438, 2.95827736276659, 0.00907952607960283, 5.9728079031583, 0)[20250519_005218.]: c(NA, 2.64120811950561, 2.0872318749424, 1.79478597578134, 4.66987439288761, 4.73324378042655, 0.0121060347728038, 6.82606617503806, 0) [20250519_005218.]: Entered 'hyperbolic_regression'-Function [20250519_005218.]: 'hyperbolic_regression': minmax = FALSE [20250519_005218.]: Entered 'cubic_regression'-Function [20250519_005218.]: 'cubic_regression': minmax = FALSE [20250519_005218.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.13660501904968, 11.4129696733689, 26.174000526428, 35.1050449117028, 47.685500330611, 67.1440494417104, 75.7644668894086, 84.4054158616395, 100)c(1.13660501904968, 1.0870303266311, 1.174000526428, 2.3949550882972, 2.314499669389, 4.64404944171039, 0.764466889408595, 3.0945841383605, 0)c(NA, 8.6962426130488, 4.696002105712, 6.38654690212587, 4.628999338778, 7.43047910673663, 1.01928918587813, 3.53666758669772, 0) [20250519_005218.]: Logging df_agg: CpG#2 [20250519_005218.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005218.]: c(1.13660501904968, 11.4129696733689, 26.174000526428, 35.1050449117028, 47.685500330611, 67.1440494417104, 75.7644668894086, 84.4054158616395, 100)[20250519_005218.]: c(1.13660501904968, 1.0870303266311, 1.174000526428, 2.3949550882972, 2.314499669389, 4.64404944171039, 0.764466889408595, 3.0945841383605, 0)[20250519_005218.]: c(NA, 8.6962426130488, 4.696002105712, 6.38654690212587, 4.628999338778, 7.43047910673663, 1.01928918587813, 3.53666758669772, 0) [20250519_005218.]: Entered 'hyperbolic_regression'-Function [20250519_005218.]: 'hyperbolic_regression': minmax = FALSE [20250519_005219.]: Entered 'cubic_regression'-Function [20250519_005219.]: 'cubic_regression': minmax = FALSE [20250519_005219.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.51235653688495, 10.7523884294604, 25.5218907947761, 36.5270462675211, 50.7909245028224, 64.8686317550184, 77.5524188495235, 80.4374617358174, 100)c(0.51235653688495, 1.7476115705396, 0.521890794776098, 0.972953732478899, 0.790924502822399, 2.3686317550184, 2.55241884952351, 7.06253826418261, 0)c(NA, 13.9808925643168, 2.08756317910439, 2.5945432866104, 1.5818490056448, 3.78981080802944, 3.40322513269801, 8.07147230192298, 0) [20250519_005219.]: Logging df_agg: CpG#3 [20250519_005219.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005219.]: c(0.51235653688495, 10.7523884294604, 25.5218907947761, 36.5270462675211, 50.7909245028224, 64.8686317550184, 77.5524188495235, 80.4374617358174, 100)[20250519_005219.]: c(0.51235653688495, 1.7476115705396, 0.521890794776098, 0.972953732478899, 0.790924502822399, 2.3686317550184, 2.55241884952351, 7.06253826418261, 0)[20250519_005219.]: c(NA, 13.9808925643168, 2.08756317910439, 2.5945432866104, 1.5818490056448, 3.78981080802944, 3.40322513269801, 8.07147230192298, 0) [20250519_005219.]: Entered 'hyperbolic_regression'-Function [20250519_005219.]: 'hyperbolic_regression': minmax = FALSE [20250519_005219.]: Entered 'cubic_regression'-Function [20250519_005219.]: 'cubic_regression': minmax = FALSE [20250519_005219.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.4934147844872, 24.2685420024115, 38.0817128465023, 48.5843181174811, 67.6722399183037, 74.1549277799119, 82.8821797890026, 100)c(0, 0.00658521551279989, 0.7314579975885, 0.581712846502299, 1.4156818825189, 5.1722399183037, 0.845072220088099, 4.61782021099739, 0)c(NA, 0.0526817241023991, 2.925831990354, 1.55123425733946, 2.83136376503781, 8.27558386928592, 1.12676296011747, 5.27750881256845, 0) [20250519_005219.]: Logging df_agg: CpG#4 [20250519_005219.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005219.]: c(0, 12.4934147844872, 24.2685420024115, 38.0817128465023, 48.5843181174811, 67.6722399183037, 74.1549277799119, 82.8821797890026, 100)[20250519_005219.]: c(0, 0.00658521551279989, 0.7314579975885, 0.581712846502299, 1.4156818825189, 5.1722399183037, 0.845072220088099, 4.61782021099739, 0)[20250519_005219.]: c(NA, 0.0526817241023991, 2.925831990354, 1.55123425733946, 2.83136376503781, 8.27558386928592, 1.12676296011747, 5.27750881256845, 0) [20250519_005219.]: Entered 'hyperbolic_regression'-Function [20250519_005219.]: 'hyperbolic_regression': minmax = FALSE [20250519_005219.]: Entered 'cubic_regression'-Function [20250519_005219.]: 'cubic_regression': minmax = FALSE [20250519_005219.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.41558626275183, 10.1649674907454, 23.9830820412762, 37.2773619900429, 50.8659386543864, 62.4342273571069, 76.3915260534323, 86.159788778566, 100)c(2.41558626275183, 2.3350325092546, 1.0169179587238, 0.222638009957102, 0.865938654386397, 0.0657726428931014, 1.3915260534323, 1.340211221434, 0)c(NA, 18.6802600740368, 4.06767183489519, 0.593701359885604, 1.73187730877279, 0.105236228628962, 1.85536807124307, 1.53166996735315, 0) [20250519_005219.]: Logging df_agg: CpG#5 [20250519_005219.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005219.]: c(2.41558626275183, 10.1649674907454, 23.9830820412762, 37.2773619900429, 50.8659386543864, 62.4342273571069, 76.3915260534323, 86.159788778566, 100)[20250519_005219.]: c(2.41558626275183, 2.3350325092546, 1.0169179587238, 0.222638009957102, 0.865938654386397, 0.0657726428931014, 1.3915260534323, 1.340211221434, 0)[20250519_005219.]: c(NA, 18.6802600740368, 4.06767183489519, 0.593701359885604, 1.73187730877279, 0.105236228628962, 1.85536807124307, 1.53166996735315, 0) [20250519_005219.]: Entered 'hyperbolic_regression'-Function [20250519_005219.]: 'hyperbolic_regression': minmax = FALSE [20250519_005219.]: Entered 'cubic_regression'-Function [20250519_005219.]: 'cubic_regression': minmax = FALSE [20250519_005219.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.138163748613034, 11.8635558881981, 26.5107449550797, 35.3205073050661, 50.0570767570666, 64.9602944381018, 73.66890571617, 87.1266086585036, 100)c(0.138163748613034, 0.6364441118019, 1.5107449550797, 2.1794926949339, 0.0570767570666035, 2.4602944381018, 1.33109428383, 0.373391341496401, 0)c(NA, 5.0915528944152, 6.04297982031881, 5.81198051982373, 0.114153514133207, 3.93647110096288, 1.77479237844, 0.426732961710172, 0) [20250519_005219.]: Logging df_agg: CpG#6 [20250519_005219.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005219.]: c(0.138163748613034, 11.8635558881981, 26.5107449550797, 35.3205073050661, 50.0570767570666, 64.9602944381018, 73.66890571617, 87.1266086585036, 100)[20250519_005219.]: c(0.138163748613034, 0.6364441118019, 1.5107449550797, 2.1794926949339, 0.0570767570666035, 2.4602944381018, 1.33109428383, 0.373391341496401, 0)[20250519_005219.]: c(NA, 5.0915528944152, 6.04297982031881, 5.81198051982373, 0.114153514133207, 3.93647110096288, 1.77479237844, 0.426732961710172, 0) [20250519_005219.]: Entered 'hyperbolic_regression'-Function [20250519_005219.]: 'hyperbolic_regression': minmax = FALSE [20250519_005220.]: Entered 'cubic_regression'-Function [20250519_005220.]: 'cubic_regression': minmax = FALSE [20250519_005220.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.1993162352498, 24.595178967123, 37.8310421041787, 53.5588739724067, 65.9364947980258, 75.7361094434913, 79.432823759854, 100)c(0, 2.3006837647502, 0.404821032876999, 0.3310421041787, 3.5588739724067, 3.43649479802581, 0.736109443491301, 8.067176240146, 0)c(NA, 18.4054701180016, 1.619284131508, 0.882778944476532, 7.1177479448134, 5.49839167684129, 0.981479257988402, 9.21962998873828, 0) [20250519_005220.]: Logging df_agg: CpG#7 [20250519_005220.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005220.]: c(0, 10.1993162352498, 24.595178967123, 37.8310421041787, 53.5588739724067, 65.9364947980258, 75.7361094434913, 79.432823759854, 100)[20250519_005220.]: c(0, 2.3006837647502, 0.404821032876999, 0.3310421041787, 3.5588739724067, 3.43649479802581, 0.736109443491301, 8.067176240146, 0)[20250519_005220.]: c(NA, 18.4054701180016, 1.619284131508, 0.882778944476532, 7.1177479448134, 5.49839167684129, 0.981479257988402, 9.21962998873828, 0) [20250519_005220.]: Entered 'hyperbolic_regression'-Function [20250519_005220.]: 'hyperbolic_regression': minmax = FALSE [20250519_005220.]: Entered 'cubic_regression'-Function [20250519_005220.]: 'cubic_regression': minmax = FALSE [20250519_005220.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.80068218205093, 9.27535134596596, 25.4762621928197, 34.0122075735416, 51.7842655662325, 64.6732311906145, 78.4326978859189, 81.3427232852719, 100)c(2.80068218205093, 3.22464865403404, 0.4762621928197, 3.4877924264584, 1.7842655662325, 2.1732311906145, 3.4326978859189, 6.1572767147281, 0)c(NA, 25.7971892322723, 1.9050487712788, 9.30077980388906, 3.568531132465, 3.4771699049832, 4.57693051455853, 7.03688767397497, 0) [20250519_005220.]: Logging df_agg: CpG#8 [20250519_005220.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005220.]: c(2.80068218205093, 9.27535134596596, 25.4762621928197, 34.0122075735416, 51.7842655662325, 64.6732311906145, 78.4326978859189, 81.3427232852719, 100)[20250519_005220.]: c(2.80068218205093, 3.22464865403404, 0.4762621928197, 3.4877924264584, 1.7842655662325, 2.1732311906145, 3.4326978859189, 6.1572767147281, 0)[20250519_005220.]: c(NA, 25.7971892322723, 1.9050487712788, 9.30077980388906, 3.568531132465, 3.4771699049832, 4.57693051455853, 7.03688767397497, 0) [20250519_005220.]: Entered 'hyperbolic_regression'-Function [20250519_005220.]: 'hyperbolic_regression': minmax = FALSE [20250519_005220.]: Entered 'cubic_regression'-Function [20250519_005220.]: 'cubic_regression': minmax = FALSE [20250519_005220.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.5082192457956, 26.9164567253388, 36.8334779159501, 52.0097895977263, 64.8930527921581, 74.5671055499357, 84.5294954832669, 100)c(0, 1.9917807542044, 1.9164567253388, 0.666522084049902, 2.0097895977263, 2.3930527921581, 0.432894450064296, 2.9705045167331, 0)c(NA, 15.9342460336352, 7.6658269013552, 1.77739222413307, 4.01957919545259, 3.82888446745296, 0.577192600085728, 3.39486230483783, 0) [20250519_005220.]: Logging df_agg: CpG#9 [20250519_005220.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005220.]: c(0, 10.5082192457956, 26.9164567253388, 36.8334779159501, 52.0097895977263, 64.8930527921581, 74.5671055499357, 84.5294954832669, 100)[20250519_005220.]: c(0, 1.9917807542044, 1.9164567253388, 0.666522084049902, 2.0097895977263, 2.3930527921581, 0.432894450064296, 2.9705045167331, 0)[20250519_005220.]: c(NA, 15.9342460336352, 7.6658269013552, 1.77739222413307, 4.01957919545259, 3.82888446745296, 0.577192600085728, 3.39486230483783, 0) [20250519_005220.]: Entered 'hyperbolic_regression'-Function [20250519_005220.]: 'hyperbolic_regression': minmax = FALSE [20250519_005220.]: Entered 'cubic_regression'-Function [20250519_005220.]: 'cubic_regression': minmax = FALSE [20250519_005220.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.290940467580009, 11.041240609297, 25.4081503210647, 36.5243723188613, 50.7348829528732, 65.3135214250794, 75.5342711952113, 83.2411229564887, 100)c(0.290940467580009, 1.458759390703, 0.408150321064699, 0.975627681138697, 0.7348829528732, 2.8135214250794, 0.534271195211304, 4.2588770435113, 0)c(NA, 11.670075125624, 1.6326012842588, 2.60167381636986, 1.4697659057464, 4.50163428012704, 0.712361593615071, 4.8672880497272, 0) [20250519_005220.]: Logging df_agg: row_means [20250519_005220.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005220.]: c(0.290940467580009, 11.041240609297, 25.4081503210647, 36.5243723188613, 50.7348829528732, 65.3135214250794, 75.5342711952113, 83.2411229564887, 100)[20250519_005220.]: c(0.290940467580009, 1.458759390703, 0.408150321064699, 0.975627681138697, 0.7348829528732, 2.8135214250794, 0.534271195211304, 4.2588770435113, 0)[20250519_005220.]: c(NA, 11.670075125624, 1.6326012842588, 2.60167381636986, 1.4697659057464, 4.50163428012704, 0.712361593615071, 4.8672880497272, 0) [20250519_005220.]: Entered 'hyperbolic_regression'-Function [20250519_005220.]: 'hyperbolic_regression': minmax = FALSE [20250519_005220.]: Entered 'cubic_regression'-Function [20250519_005220.]: 'cubic_regression': minmax = FALSE [20250519_005221.]: ### Starting with plotting ### [20250519_005221.]: Creating BiasCorrected (hyperbolic) plot No. 1 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG1_corrected_h.png [20250519_005221.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -657.853491622191 , a = 985.267531706584 , b = -657.853491622191 , d = 903.45414536045 [20250519_005221.]: # CpG-site: CpG#1 Cubic: Using a = 5.18463232357963e-06 , b = -0.00180592068110451 , c = 1.11404020909943 , d = -0.858600112691325 [20250519_005223.]: Creating BiasCorrected (hyperbolic) plot No. 2 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG2_corrected_h.png [20250519_005223.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = 3399.55230393578 , a = 30276.0421015763 , b = 3399.55230393578 , d = 30352.4984502595 [20250519_005223.]: # CpG-site: CpG#2 Cubic: Using a = -2.58097549676629e-05 , b = 0.00383753278164476 , c = 0.8516077608074 , d = 0.957127237506517 [20250519_005225.]: Creating BiasCorrected (hyperbolic) plot No. 3 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG3_corrected_h.png [20250519_005225.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = -945.757057025882 , a = 1333.61975246108 , b = -945.757057025882 , d = 1252.95856205348 [20250519_005225.]: # CpG-site: CpG#3 Cubic: Using a = -1.13948611491254e-05 , b = 0.000937180276062016 , c = 0.998630620080636 , d = -0.383337686690576 [20250519_005227.]: Creating BiasCorrected (hyperbolic) plot No. 4 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG4_corrected_h.png [20250519_005227.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = -754.050736416394 , a = 1637.26786961468 , b = -754.050736416394 , d = 1558.27361713715 [20250519_005227.]: # CpG-site: CpG#4 Cubic: Using a = -3.25779988054422e-06 , b = -0.000129596030700936 , c = 1.03077246222439 , d = -0.370343704072989 [20250519_005229.]: Creating BiasCorrected (hyperbolic) plot No. 5 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG5_corrected_h.png [20250519_005229.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = -1917.44420974597 , a = -5621.4534701009 , b = -1917.44420974597 , d = -5734.47300613195 [20250519_005229.]: # CpG-site: CpG#5 Cubic: Using a = -3.16782834876228e-05 , b = 0.00493527189107616 , c = 0.800194601834967 , d = 1.38653015028503 [20250519_005231.]: Creating BiasCorrected (hyperbolic) plot No. 6 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG6_corrected_h.png [20250519_005231.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = -1255.67712347228 , a = 13775.5934089325 , b = -1255.67712347228 , d = 13690.4825730065 [20250519_005231.]: # CpG-site: CpG#6 Cubic: Using a = -3.70201684653088e-06 , b = 0.000482210694203131 , c = 0.985293133118367 , d = 0.0293568698433451 [20250519_005232.]: Creating BiasCorrected (hyperbolic) plot No. 7 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG7_corrected_h.png [20250519_005232.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -1065.05415478934 , a = 722.083975601617 , b = -1065.05415478934 , d = 634.402060442185 [20250519_005232.]: # CpG-site: CpG#7 Cubic: Using a = -1.04111972402046e-06 , b = -0.00130150084904922 , c = 1.12399516228677 , d = -1.5985899870939 [20250519_005234.]: Creating BiasCorrected (hyperbolic) plot No. 8 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG8_corrected_h.png [20250519_005234.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = 68.5784871630731 , a = 4784.90969361316 , b = 68.5784871630731 , d = 4739.12296431021 [20250519_005234.]: # CpG-site: CpG#8 Cubic: Using a = -4.65730502627556e-05 , b = 0.00676525570440644 , c = 0.747405744075959 , d = 1.52337158754213 [20250519_005236.]: Creating BiasCorrected (hyperbolic) plot No. 9 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG9_corrected_h.png [20250519_005236.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -1022.70169872734 , a = 1273.73039431947 , b = -1022.70169872734 , d = 1181.86496720377 [20250519_005236.]: # CpG-site: CpG#9 Cubic: Using a = 4.24790097543708e-06 , b = -0.00144169637811555 , c = 1.09837791472274 , d = -0.97898795630014 [20250519_005238.]: Creating BiasCorrected (hyperbolic) plot No. 10 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_rowmeans_corrected_h.png [20250519_005238.]: # CpG-site: row_means Hyperbolic: Using bias_weight = -1166.73858297355 , a = 1740.63784318208 , b = -1166.73858297355 , d = 1653.86071043116 [20250519_005238.]: # CpG-site: row_means Cubic: Using a = -8.71186803343537e-06 , b = 0.000715553560638339 , c = 1.00248252890793 , d = -0.420554394203132 [20250519_005240.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG1_corrected_h.png [20250519_005242.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG2_corrected_h.png [20250519_005244.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG3_corrected_h.png [20250519_005245.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG4_corrected_h.png [20250519_005247.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG5_corrected_h.png [20250519_005249.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG6_corrected_h.png [20250519_005251.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG7_corrected_h.png [20250519_005252.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG8_corrected_h.png [20250519_005254.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG9_corrected_h.png [20250519_005256.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_rowmeans_corrected_h.png [20250519_005258.]: Entered 'solving_equations'-Function [20250519_005258.]: Solving cubic regression for CpG#1 Coefficients: -1.03617340067344Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_005258.]: Samplename: 0 Root: 1.334 --> Root in between the borders! Added to results. Coefficients: -8.34150673400678Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_005258.]: Samplename: 12.5 Root: 11.446 --> Root in between the borders! Added to results. Coefficients: -15.3881734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_005258.]: Samplename: 25 Root: 22.228 --> Root in between the borders! Added to results. Coefficients: -24.2801734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_005258.]: Samplename: 37.5 Root: 36.374 --> Root in between the borders! Added to results. Coefficients: -34.9006734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_005258.]: Samplename: 50 Root: 52.044 --> Root in between the borders! Added to results. Coefficients: -46.3541734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_005258.]: Samplename: 62.5 Root: 66.144 --> Root in between the borders! Added to results. Coefficients: -55.8931734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_005258.]: Samplename: 75 Root: 75.864 --> Root in between the borders! Added to results. Coefficients: -63.0981734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_005258.]: Samplename: 87.5 Root: 82.254 --> Root in between the borders! Added to results. Coefficients: -91.0461734006735Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_005258.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.877 --> '100 < root < 110' --> substitute 100 [20250519_005258.]: Solving cubic regression for CpG#2 Coefficients: -0.283329966329966Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_005258.]: Samplename: 0 Root: 0.549 --> Root in between the borders! Added to results. Coefficients: -6.33999663299663Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_005258.]: Samplename: 12.5 Root: 11.533 --> Root in between the borders! Added to results. Coefficients: -15.93932996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_005258.]: Samplename: 25 Root: 26.628 --> Root in between the borders! Added to results. Coefficients: -22.33732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_005258.]: Samplename: 37.5 Root: 35.509 --> Root in between the borders! Added to results. Coefficients: -32.22832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_005258.]: Samplename: 50 Root: 47.851 --> Root in between the borders! Added to results. Coefficients: -49.96332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_005258.]: Samplename: 62.5 Root: 66.893 --> Root in between the borders! Added to results. Coefficients: -58.96582996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_005258.]: Samplename: 75 Root: 75.431 --> Root in between the borders! Added to results. Coefficients: -68.8366632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_005258.]: Samplename: 87.5 Root: 84.118 --> Root in between the borders! Added to results. Coefficients: -90.57732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_005258.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.287 --> '100 < root < 110' --> substitute 100 [20250519_005258.]: Solving cubic regression for CpG#3 Coefficients: -0.90294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_005258.]: Samplename: 0 Root: 1.441 --> Root in between the borders! Added to results. Coefficients: -6.57294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_005258.]: Samplename: 12.5 Root: 10.568 --> Root in between the borders! Added to results. Coefficients: -15.4289478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_005258.]: Samplename: 25 Root: 24.796 --> Root in between the borders! Added to results. Coefficients: -22.6129478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_005258.]: Samplename: 37.5 Root: 35.952 --> Root in between the borders! Added to results. Coefficients: -32.7754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_005258.]: Samplename: 50 Root: 50.684 --> Root in between the borders! Added to results. Coefficients: -43.8889478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_005258.]: Samplename: 62.5 Root: 65.142 --> Root in between the borders! Added to results. Coefficients: -54.9754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_005258.]: Samplename: 75 Root: 77.905 --> Root in between the borders! Added to results. Coefficients: -57.6562811447812Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_005258.]: Samplename: 87.5 Root: 80.767 --> Root in between the borders! Added to results. Coefficients: -80.6649478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_005258.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.38 --> '100 < root < 110' --> substitute 100 [20250519_005258.]: Solving cubic regression for CpG#4 Coefficients: -0.597449494949524Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_005258.]: Samplename: 0 Root: 0.858 --> Root in between the borders! Added to results. Coefficients: -8.25278282828286Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_005258.]: Samplename: 12.5 Root: 12.086 --> Root in between the borders! Added to results. Coefficients: -15.8034494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_005258.]: Samplename: 25 Root: 23.316 --> Root in between the borders! Added to results. Coefficients: -25.5274494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_005258.]: Samplename: 37.5 Root: 37.383 --> Root in between the borders! Added to results. Coefficients: -33.6369494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_005258.]: Samplename: 50 Root: 48.353 --> Root in between the borders! Added to results. Coefficients: -50.2554494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_005258.]: Samplename: 62.5 Root: 68.082 --> Root in between the borders! Added to results. Coefficients: -56.5394494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_005258.]: Samplename: 75 Root: 74.615 --> Root in between the borders! Added to results. Coefficients: -65.5927828282829Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_005258.]: Samplename: 87.5 Root: 83.254 --> Root in between the borders! Added to results. Coefficients: -88.3214494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_005258.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.715 --> '100 < root < 110' --> substitute 100 [20250519_005258.]: Solving cubic regression for CpG#5 Coefficients: -0.623961279461278Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_005258.]: Samplename: 0 Root: 1.458 --> Root in between the borders! Added to results. Coefficients: -4.76796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_005258.]: Samplename: 12.5 Root: 10.347 --> Root in between the borders! Added to results. Coefficients: -12.7259612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_005258.]: Samplename: 25 Root: 24.815 --> Root in between the borders! Added to results. Coefficients: -21.1599612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_005258.]: Samplename: 37.5 Root: 37.902 --> Root in between the borders! Added to results. Coefficients: -30.6954612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_005258.]: Samplename: 50 Root: 50.977 --> Root in between the borders! Added to results. Coefficients: -39.6579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_005258.]: Samplename: 62.5 Root: 62.126 --> Root in between the borders! Added to results. Coefficients: -51.6829612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_005258.]: Samplename: 75 Root: 75.852 --> Root in between the borders! Added to results. Coefficients: -61.0146279461279Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_005258.]: Samplename: 87.5 Root: 85.767 --> Root in between the borders! Added to results. Coefficients: -76.0699612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_005258.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.743 --> '100 < root < 110' --> substitute 100 [20250519_005258.]: Solving cubic regression for CpG#6 Coefficients: -0.196072390572403Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_005258.]: Samplename: 0 Root: 0.349 --> Root in between the borders! Added to results. Coefficients: -6.73873905723907Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_005258.]: Samplename: 12.5 Root: 11.718 --> Root in between the borders! Added to results. Coefficients: -15.8880723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_005258.]: Samplename: 25 Root: 26.396 --> Root in between the borders! Added to results. Coefficients: -22.0000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_005258.]: Samplename: 37.5 Root: 35.301 --> Root in between the borders! Added to results. Coefficients: -33.4445723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_005258.]: Samplename: 50 Root: 50.134 --> Root in between the borders! Added to results. Coefficients: -46.9000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_005258.]: Samplename: 62.5 Root: 64.993 --> Root in between the borders! Added to results. Coefficients: -55.8320723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_005258.]: Samplename: 75 Root: 73.639 --> Root in between the borders! Added to results. Coefficients: -71.5454057239057Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_005258.]: Samplename: 87.5 Root: 87.043 --> Root in between the borders! Added to results. Coefficients: -89.6560723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_005258.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.329 --> '100 < root < 110' --> substitute 100 [20250519_005258.]: Solving cubic regression for CpG#7 Coefficients: -1.21495454545456Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_005258.]: Samplename: 0 Root: 2.13 --> Root in between the borders! Added to results. Coefficients: -5.39562121212123Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_005258.]: Samplename: 12.5 Root: 9.973 --> Root in between the borders! Added to results. Coefficients: -11.2649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_005258.]: Samplename: 25 Root: 22.206 --> Root in between the borders! Added to results. Coefficients: -17.4509545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_005258.]: Samplename: 37.5 Root: 35.814 --> Root in between the borders! Added to results. Coefficients: -26.0314545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_005258.]: Samplename: 50 Root: 53.28 --> Root in between the borders! Added to results. Coefficients: -33.9649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_005258.]: Samplename: 62.5 Root: 66.598 --> Root in between the borders! Added to results. Coefficients: -41.1689545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_005258.]: Samplename: 75 Root: 76.575 --> Root in between the borders! Added to results. Coefficients: -44.1356212121212Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_005258.]: Samplename: 87.5 Root: 80.219 --> Root in between the borders! Added to results. Coefficients: -67.2229545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_005258.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.506 --> '100 < root < 110' --> substitute 100 [20250519_005258.]: Solving cubic regression for CpG#8 Coefficients: -1.09618518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_005258.]: Samplename: 0 Root: 2.016 --> Root in between the borders! Added to results. Coefficients: -5.44685185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_005258.]: Samplename: 12.5 Root: 9.458 --> Root in between the borders! Added to results. Coefficients: -16.9301851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_005258.]: Samplename: 25 Root: 26.35 --> Root in between the borders! Added to results. Coefficients: -23.3501851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_005258.]: Samplename: 37.5 Root: 34.728 --> Root in between the borders! Added to results. Coefficients: -37.6251851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_005258.]: Samplename: 50 Root: 51.781 --> Root in between the borders! Added to results. Coefficients: -48.8261851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_005258.]: Samplename: 62.5 Root: 64.192 --> Root in between the borders! Added to results. Coefficients: -61.6676851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_005258.]: Samplename: 75 Root: 77.804 --> Root in between the borders! Added to results. Coefficients: -64.5101851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_005258.]: Samplename: 87.5 Root: 80.758 --> Root in between the borders! Added to results. Coefficients: -86.0601851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_005258.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.834 --> '100 < root < 110' --> substitute 100 [20250519_005258.]: Solving cubic regression for CpG#9 Coefficients: -0.989865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_005258.]: Samplename: 0 Root: 1.475 --> Root in between the borders! Added to results. Coefficients: -6.39586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_005258.]: Samplename: 12.5 Root: 10.12 --> Root in between the borders! Added to results. Coefficients: -14.7058653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_005258.]: Samplename: 25 Root: 24.844 --> Root in between the borders! Added to results. Coefficients: -20.6238653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_005258.]: Samplename: 37.5 Root: 35.327 --> Root in between the borders! Added to results. Coefficients: -31.3958653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_005258.]: Samplename: 50 Root: 51.855 --> Root in between the borders! Added to results. Coefficients: -42.6858653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_005258.]: Samplename: 62.5 Root: 65.265 --> Root in between the borders! Added to results. Coefficients: -52.9033653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_005258.]: Samplename: 75 Root: 74.915 --> Root in between the borders! Added to results. Coefficients: -65.492531986532Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_005258.]: Samplename: 87.5 Root: 84.67 --> Root in between the borders! Added to results. Coefficients: -92.9898653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_005258.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.082 --> '100 < root < 110' --> substitute 100 [20250519_005258.]: Solving cubic regression for row_means Coefficients: -0.771215488215478Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_005258.]: Samplename: 0 Root: 1.287 --> Root in between the borders! Added to results. Coefficients: -6.47247474747474Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_005258.]: Samplename: 12.5 Root: 10.847 --> Root in between the borders! Added to results. Coefficients: -14.8972154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_005258.]: Samplename: 25 Root: 24.737 --> Root in between the borders! Added to results. Coefficients: -22.1492154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_005258.]: Samplename: 37.5 Root: 36.02 --> Root in between the borders! Added to results. Coefficients: -32.5259932659933Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_005258.]: Samplename: 50 Root: 50.639 --> Root in between the borders! Added to results. Coefficients: -44.7218821548821Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_005258.]: Samplename: 62.5 Root: 65.497 --> Root in between the borders! Added to results. Coefficients: -54.4032154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_005258.]: Samplename: 75 Root: 75.751 --> Root in between the borders! Added to results. Coefficients: -62.4313636363636Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_005258.]: Samplename: 87.5 Root: 83.403 --> Root in between the borders! Added to results. Coefficients: -84.7343265993266Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_005258.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.573 --> '100 < root < 110' --> substitute 100 [20250519_005258.]: ### Starting with regression calculations ### [20250519_005258.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_005258.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) [20250519_005258.]: Logging df_agg: CpG#1 [20250519_005258.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005258.]: c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)[20250519_005258.]: c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)[20250519_005258.]: c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) [20250519_005258.]: Entered 'hyperbolic_regression'-Function [20250519_005258.]: 'hyperbolic_regression': minmax = FALSE [20250519_005258.]: Entered 'cubic_regression'-Function [20250519_005258.]: 'cubic_regression': minmax = FALSE [20250519_005258.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) [20250519_005258.]: Logging df_agg: CpG#2 [20250519_005258.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005258.]: c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)[20250519_005258.]: c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)[20250519_005258.]: c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) [20250519_005258.]: Entered 'hyperbolic_regression'-Function [20250519_005258.]: 'hyperbolic_regression': minmax = FALSE [20250519_005259.]: Entered 'cubic_regression'-Function [20250519_005259.]: 'cubic_regression': minmax = FALSE [20250519_005259.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) [20250519_005259.]: Logging df_agg: CpG#3 [20250519_005259.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005259.]: c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)[20250519_005259.]: c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)[20250519_005259.]: c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) [20250519_005259.]: Entered 'hyperbolic_regression'-Function [20250519_005259.]: 'hyperbolic_regression': minmax = FALSE [20250519_005259.]: Entered 'cubic_regression'-Function [20250519_005259.]: 'cubic_regression': minmax = FALSE [20250519_005259.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) [20250519_005259.]: Logging df_agg: CpG#4 [20250519_005259.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005259.]: c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)[20250519_005259.]: c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)[20250519_005259.]: c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) [20250519_005259.]: Entered 'hyperbolic_regression'-Function [20250519_005259.]: 'hyperbolic_regression': minmax = FALSE [20250519_005259.]: Entered 'cubic_regression'-Function [20250519_005259.]: 'cubic_regression': minmax = FALSE [20250519_005259.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) [20250519_005259.]: Logging df_agg: CpG#5 [20250519_005259.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005259.]: c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)[20250519_005259.]: c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)[20250519_005259.]: c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) [20250519_005259.]: Entered 'hyperbolic_regression'-Function [20250519_005259.]: 'hyperbolic_regression': minmax = FALSE [20250519_005259.]: Entered 'cubic_regression'-Function [20250519_005259.]: 'cubic_regression': minmax = FALSE [20250519_005259.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) [20250519_005259.]: Logging df_agg: CpG#6 [20250519_005259.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005259.]: c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)[20250519_005259.]: c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)[20250519_005259.]: c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) [20250519_005259.]: Entered 'hyperbolic_regression'-Function [20250519_005259.]: 'hyperbolic_regression': minmax = FALSE [20250519_005300.]: Entered 'cubic_regression'-Function [20250519_005300.]: 'cubic_regression': minmax = FALSE [20250519_005300.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) [20250519_005300.]: Logging df_agg: CpG#7 [20250519_005300.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005300.]: c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)[20250519_005300.]: c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)[20250519_005300.]: c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) [20250519_005300.]: Entered 'hyperbolic_regression'-Function [20250519_005300.]: 'hyperbolic_regression': minmax = FALSE [20250519_005300.]: Entered 'cubic_regression'-Function [20250519_005300.]: 'cubic_regression': minmax = FALSE [20250519_005300.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) [20250519_005300.]: Logging df_agg: CpG#8 [20250519_005300.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005300.]: c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)[20250519_005300.]: c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)[20250519_005300.]: c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) [20250519_005300.]: Entered 'hyperbolic_regression'-Function [20250519_005300.]: 'hyperbolic_regression': minmax = FALSE [20250519_005300.]: Entered 'cubic_regression'-Function [20250519_005300.]: 'cubic_regression': minmax = FALSE [20250519_005300.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) [20250519_005300.]: Logging df_agg: CpG#9 [20250519_005300.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005300.]: c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)[20250519_005300.]: c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)[20250519_005300.]: c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) [20250519_005300.]: Entered 'hyperbolic_regression'-Function [20250519_005300.]: 'hyperbolic_regression': minmax = FALSE [20250519_005300.]: Entered 'cubic_regression'-Function [20250519_005300.]: 'cubic_regression': minmax = FALSE [20250519_005300.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) [20250519_005300.]: Logging df_agg: row_means [20250519_005300.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005300.]: c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)[20250519_005300.]: c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)[20250519_005300.]: c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) [20250519_005300.]: Entered 'hyperbolic_regression'-Function [20250519_005300.]: 'hyperbolic_regression': minmax = FALSE [20250519_005300.]: Entered 'cubic_regression'-Function [20250519_005300.]: 'cubic_regression': minmax = FALSE [20250519_005301.]: ### Starting with plotting ### [20250519_005301.]: Creating BiasCorrected (cubic) plot No. 1 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG1_corrected_c.png [20250519_005301.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -1164.77580684101 , a = 2039.49844669959 , b = -1164.77580684101 , d = 1955.83553683067 [20250519_005301.]: # CpG-site: CpG#1 Cubic: Using a = -3.42821196240366e-05 , b = 0.00462173155013798 , c = 0.850326258865826 , d = 0.500073874211125 [20250519_005303.]: Creating BiasCorrected (cubic) plot No. 2 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG2_corrected_c.png [20250519_005303.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = -464.128853555559 , a = 4988.85258428155 , b = -464.128853555559 , d = 4925.32559808582 [20250519_005303.]: # CpG-site: CpG#2 Cubic: Using a = -1.40811796906314e-05 , b = 0.00190849431754485 , c = 0.933455870941473 , d = 0.363096210099674 [20250519_005305.]: Creating BiasCorrected (cubic) plot No. 3 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG3_corrected_c.png [20250519_005305.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = -745.611435752166 , a = 2026.53033232932 , b = -745.611435752166 , d = 1953.28002286922 [20250519_005305.]: # CpG-site: CpG#3 Cubic: Using a = -2.78835051857186e-05 , b = 0.00366863707889089 , c = 0.880665171220893 , d = 0.510779308833653 [20250519_005306.]: Creating BiasCorrected (cubic) plot No. 4 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG4_corrected_c.png [20250519_005306.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = -833.073059054674 , a = 2871.1407505286 , b = -833.073059054674 , d = 2796.8155099796 [20250519_005306.]: # CpG-site: CpG#4 Cubic: Using a = -2.30028865643e-05 , b = 0.00309017851940692 , c = 0.897073107777645 , d = 0.443455761841866 [20250519_005308.]: Creating BiasCorrected (cubic) plot No. 5 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG5_corrected_c.png [20250519_005308.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = 222.127659665624 , a = 12477.5957294815 , b = 222.127659665624 , d = 12432.2636419037 [20250519_005308.]: # CpG-site: CpG#5 Cubic: Using a = -1.28632696989134e-05 , b = 0.00184822285299014 , c = 0.931001046631176 , d = 0.437963095185599 [20250519_005310.]: Creating BiasCorrected (cubic) plot No. 6 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG6_corrected_c.png [20250519_005310.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = -700.510534379779 , a = 21299.1825568909 , b = -700.510534379779 , d = 21234.5878606004 [20250519_005310.]: # CpG-site: CpG#6 Cubic: Using a = -5.838335151375e-06 , b = 0.000828447163218526 , c = 0.970047764475166 , d = 0.158074701412251 [20250519_005312.]: Creating BiasCorrected (cubic) plot No. 7 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG7_corrected_c.png [20250519_005312.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -1144.90822837929 , a = 1477.91227480711 , b = -1144.90822837929 , d = 1394.70075909546 [20250519_005312.]: # CpG-site: CpG#7 Cubic: Using a = -4.54220535614047e-05 , b = 0.00607993130565131 , c = 0.805411493452389 , d = 0.615125382790254 [20250519_005314.]: Creating BiasCorrected (cubic) plot No. 8 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG8_corrected_c.png [20250519_005314.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = -349.378022452652 , a = 2377.37834502072 , b = -349.378022452652 , d = 2315.2055766781 [20250519_005314.]: # CpG-site: CpG#8 Cubic: Using a = -2.64622306669639e-05 , b = 0.00353621593528015 , c = 0.877916844709074 , d = 0.698684606493051 [20250519_005316.]: Creating BiasCorrected (cubic) plot No. 9 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG9_corrected_c.png [20250519_005316.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -1536.91601393605 , a = 3745.17687624221 , b = -1536.91601393605 , d = 3656.2687244524 [20250519_005316.]: # CpG-site: CpG#9 Cubic: Using a = -2.70976130611725e-05 , b = 0.00378478247182342 , c = 0.871825263051793 , d = 0.455475740974113 [20250519_005317.]: Creating BiasCorrected (cubic) plot No. 10 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_rowmeans_corrected_c.png [20250519_005317.]: # CpG-site: row_means Hyperbolic: Using bias_weight = -820.650348722241 , a = 3198.46441212347 , b = -820.650348722241 , d = 3124.20895615508 [20250519_005317.]: # CpG-site: row_means Cubic: Using a = -2.34328362100009e-05 , b = 0.00319139073176266 , c = 0.891842168032129 , d = 0.493517689787135 [20250519_005319.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG1_corrected_c.png [20250519_005321.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG2_corrected_c.png [20250519_005323.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG3_corrected_c.png [20250519_005325.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG4_corrected_c.png [20250519_005327.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG5_corrected_c.png [20250519_005328.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG6_corrected_c.png [20250519_005330.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG7_corrected_c.png [20250519_005332.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG8_corrected_c.png [20250519_005334.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG9_corrected_c.png [20250519_005335.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_rowmeans_corrected_c.png [20250519_005337.]: Entered 'solving_equations'-Function [20250519_005337.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 79.8673456895745 [20250519_005337.]: Samplename: Sample#1 Root: 79.867 --> Root in between the borders! Added to results. Hyperbolic solved: 29.7900184340805 [20250519_005337.]: Samplename: Sample#10 Root: 29.79 --> Root in between the borders! Added to results. Hyperbolic solved: 41.6525415639691 [20250519_005337.]: Samplename: Sample#2 Root: 41.653 --> Root in between the borders! Added to results. Hyperbolic solved: 57.4652090254513 [20250519_005337.]: Samplename: Sample#3 Root: 57.465 --> Root in between the borders! Added to results. Hyperbolic solved: 9.2007130627765 [20250519_005337.]: Samplename: Sample#4 Root: 9.201 --> Root in between the borders! Added to results. Hyperbolic solved: 21.8059600538131 [20250519_005337.]: Samplename: Sample#5 Root: 21.806 --> Root in between the borders! Added to results. Hyperbolic solved: 23.083796735881 [20250519_005337.]: Samplename: Sample#6 Root: 23.084 --> Root in between the borders! Added to results. Hyperbolic solved: 45.5034245569385 [20250519_005337.]: Samplename: Sample#7 Root: 45.503 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6987904075704 [20250519_005337.]: Samplename: Sample#8 Root: 85.699 --> Root in between the borders! Added to results. Hyperbolic solved: -3.66512807265101 [20250519_005337.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -3.665 --> '-10 < root < 0' --> substitute 0 [20250519_005337.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 76.7227931065881 [20250519_005337.]: Samplename: Sample#1 Root: 76.723 --> Root in between the borders! Added to results. Hyperbolic solved: 30.992427511473 [20250519_005337.]: Samplename: Sample#10 Root: 30.992 --> Root in between the borders! Added to results. Hyperbolic solved: 42.6810882780827 [20250519_005337.]: Samplename: Sample#2 Root: 42.681 --> Root in between the borders! Added to results. Hyperbolic solved: 58.9337908379889 [20250519_005337.]: Samplename: Sample#3 Root: 58.934 --> Root in between the borders! Added to results. Hyperbolic solved: 4.98020457313721 [20250519_005337.]: Samplename: Sample#4 Root: 4.98 --> Root in between the borders! Added to results. Hyperbolic solved: 20.2480263675669 [20250519_005337.]: Samplename: Sample#5 Root: 20.248 --> Root in between the borders! Added to results. Hyperbolic solved: 17.8133085643845 [20250519_005337.]: Samplename: Sample#6 Root: 17.813 --> Root in between the borders! Added to results. Hyperbolic solved: 40.9176745340012 [20250519_005337.]: Samplename: Sample#7 Root: 40.918 --> Root in between the borders! Added to results. Hyperbolic solved: 86.0450715732916 [20250519_005337.]: Samplename: Sample#8 Root: 86.045 --> Root in between the borders! Added to results. Hyperbolic solved: 3.11579770508969 [20250519_005337.]: Samplename: Sample#9 Root: 3.116 --> Root in between the borders! Added to results. [20250519_005337.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 74.9349254100163 [20250519_005337.]: Samplename: Sample#1 Root: 74.935 --> Root in between the borders! Added to results. Hyperbolic solved: 27.6844381581493 [20250519_005337.]: Samplename: Sample#10 Root: 27.684 --> Root in between the borders! Added to results. Hyperbolic solved: 41.852019114379 [20250519_005337.]: Samplename: Sample#2 Root: 41.852 --> Root in between the borders! Added to results. Hyperbolic solved: 55.8325180209418 [20250519_005337.]: Samplename: Sample#3 Root: 55.833 --> Root in between the borders! Added to results. Hyperbolic solved: 8.03519251633153 [20250519_005337.]: Samplename: Sample#4 Root: 8.035 --> Root in between the borders! Added to results. Hyperbolic solved: 24.1066315721853 [20250519_005337.]: Samplename: Sample#5 Root: 24.107 --> Root in between the borders! Added to results. Hyperbolic solved: 26.2419820027673 [20250519_005337.]: Samplename: Sample#6 Root: 26.242 --> Root in between the borders! Added to results. Hyperbolic solved: 44.0944922703422 [20250519_005337.]: Samplename: Sample#7 Root: 44.094 --> Root in between the borders! Added to results. Hyperbolic solved: 85.8279382585787 [20250519_005337.]: Samplename: Sample#8 Root: 85.828 --> Root in between the borders! Added to results. Hyperbolic solved: -0.666482392725758 [20250519_005337.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.666 --> '-10 < root < 0' --> substitute 0 [20250519_005337.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 76.3495278640236 [20250519_005337.]: Samplename: Sample#1 Root: 76.35 --> Root in between the borders! Added to results. Hyperbolic solved: 28.2568553570941 [20250519_005337.]: Samplename: Sample#10 Root: 28.257 --> Root in between the borders! Added to results. Hyperbolic solved: 43.4089839390807 [20250519_005337.]: Samplename: Sample#2 Root: 43.409 --> Root in between the borders! Added to results. Hyperbolic solved: 58.5435236860146 [20250519_005337.]: Samplename: Sample#3 Root: 58.544 --> Root in between the borders! Added to results. Hyperbolic solved: 10.3087045690571 [20250519_005337.]: Samplename: Sample#4 Root: 10.309 --> Root in between the borders! Added to results. Hyperbolic solved: 22.183045165659 [20250519_005337.]: Samplename: Sample#5 Root: 22.183 --> Root in between the borders! Added to results. Hyperbolic solved: 27.1337769553499 [20250519_005337.]: Samplename: Sample#6 Root: 27.134 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8321096080155 [20250519_005337.]: Samplename: Sample#7 Root: 41.832 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6890189074743 [20250519_005337.]: Samplename: Sample#8 Root: 85.689 --> Root in between the borders! Added to results. Hyperbolic solved: 2.42232098177269 [20250519_005337.]: Samplename: Sample#9 Root: 2.422 --> Root in between the borders! Added to results. [20250519_005337.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 72.8126914355011 [20250519_005337.]: Samplename: Sample#1 Root: 72.813 --> Root in between the borders! Added to results. Hyperbolic solved: 26.4206935815673 [20250519_005337.]: Samplename: Sample#10 Root: 26.421 --> Root in between the borders! Added to results. Hyperbolic solved: 44.2738765057261 [20250519_005337.]: Samplename: Sample#2 Root: 44.274 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5324386232341 [20250519_005337.]: Samplename: Sample#3 Root: 53.532 --> Root in between the borders! Added to results. Hyperbolic solved: 10.0373717962154 [20250519_005337.]: Samplename: Sample#4 Root: 10.037 --> Root in between the borders! Added to results. Hyperbolic solved: 22.8733115052607 [20250519_005337.]: Samplename: Sample#5 Root: 22.873 --> Root in between the borders! Added to results. Hyperbolic solved: 25.9417915784748 [20250519_005337.]: Samplename: Sample#6 Root: 25.942 --> Root in between the borders! Added to results. Hyperbolic solved: 42.7945074918604 [20250519_005337.]: Samplename: Sample#7 Root: 42.795 --> Root in between the borders! Added to results. Hyperbolic solved: 88.8741214630346 [20250519_005337.]: Samplename: Sample#8 Root: 88.874 --> Root in between the borders! Added to results. Hyperbolic solved: 2.34647529966966 [20250519_005337.]: Samplename: Sample#9 Root: 2.346 --> Root in between the borders! Added to results. [20250519_005337.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 79.2780593622711 [20250519_005337.]: Samplename: Sample#1 Root: 79.278 --> Root in between the borders! Added to results. Hyperbolic solved: 30.2012458984074 [20250519_005337.]: Samplename: Sample#10 Root: 30.201 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8474393624107 [20250519_005337.]: Samplename: Sample#2 Root: 41.847 --> Root in between the borders! Added to results. Hyperbolic solved: 56.8423517321508 [20250519_005337.]: Samplename: Sample#3 Root: 56.842 --> Root in between the borders! Added to results. Hyperbolic solved: 8.87856046118588 [20250519_005337.]: Samplename: Sample#4 Root: 8.879 --> Root in between the borders! Added to results. Hyperbolic solved: 18.69015950004 [20250519_005337.]: Samplename: Sample#5 Root: 18.69 --> Root in between the borders! Added to results. Hyperbolic solved: 29.9309263534749 [20250519_005337.]: Samplename: Sample#6 Root: 29.931 --> Root in between the borders! Added to results. Hyperbolic solved: 42.8148560027697 [20250519_005337.]: Samplename: Sample#7 Root: 42.815 --> Root in between the borders! Added to results. Hyperbolic solved: 86.7501831416152 [20250519_005337.]: Samplename: Sample#8 Root: 86.75 --> Root in between the borders! Added to results. Hyperbolic solved: 1.51516194985267 [20250519_005337.]: Samplename: Sample#9 Root: 1.515 --> Root in between the borders! Added to results. [20250519_005337.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 78.2565592569279 [20250519_005337.]: Samplename: Sample#1 Root: 78.257 --> Root in between the borders! Added to results. Hyperbolic solved: 25.488739349283 [20250519_005337.]: Samplename: Sample#10 Root: 25.489 --> Root in between the borders! Added to results. Hyperbolic solved: 47.3712258915285 [20250519_005337.]: Samplename: Sample#2 Root: 47.371 --> Root in between the borders! Added to results. Hyperbolic solved: 58.3142673189298 [20250519_005337.]: Samplename: Sample#3 Root: 58.314 --> Root in between the borders! Added to results. Hyperbolic solved: 11.7212231360573 [20250519_005337.]: Samplename: Sample#4 Root: 11.721 --> Root in between the borders! Added to results. Hyperbolic solved: 25.3797485992238 [20250519_005337.]: Samplename: Sample#5 Root: 25.38 --> Root in between the borders! Added to results. Hyperbolic solved: 29.4095133062523 [20250519_005337.]: Samplename: Sample#6 Root: 29.41 --> Root in between the borders! Added to results. Hyperbolic solved: 44.5755071469546 [20250519_005337.]: Samplename: Sample#7 Root: 44.576 --> Root in between the borders! Added to results. Hyperbolic solved: 85.9628731021447 [20250519_005337.]: Samplename: Sample#8 Root: 85.963 --> Root in between the borders! Added to results. Hyperbolic solved: -4.1645647175353 [20250519_005337.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -4.165 --> '-10 < root < 0' --> substitute 0 [20250519_005337.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 72.9696605050898 [20250519_005337.]: Samplename: Sample#1 Root: 72.97 --> Root in between the borders! Added to results. Hyperbolic solved: 27.8250956361743 [20250519_005337.]: Samplename: Sample#10 Root: 27.825 --> Root in between the borders! Added to results. Hyperbolic solved: 34.9068388175484 [20250519_005337.]: Samplename: Sample#2 Root: 34.907 --> Root in between the borders! Added to results. Hyperbolic solved: 59.163743824213 [20250519_005337.]: Samplename: Sample#3 Root: 59.164 --> Root in between the borders! Added to results. Hyperbolic solved: 9.64644413255063 [20250519_005337.]: Samplename: Sample#4 Root: 9.646 --> Root in between the borders! Added to results. Hyperbolic solved: 19.3522607837916 [20250519_005337.]: Samplename: Sample#5 Root: 19.352 --> Root in between the borders! Added to results. Hyperbolic solved: 38.5571436812761 [20250519_005337.]: Samplename: Sample#6 Root: 38.557 --> Root in between the borders! Added to results. Hyperbolic solved: 44.7759625743335 [20250519_005337.]: Samplename: Sample#7 Root: 44.776 --> Root in between the borders! Added to results. Hyperbolic solved: 84.8599671624974 [20250519_005337.]: Samplename: Sample#8 Root: 84.86 --> Root in between the borders! Added to results. Hyperbolic solved: -2.05044894070214 [20250519_005337.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.05 --> '-10 < root < 0' --> substitute 0 [20250519_005338.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 81.041667802328 [20250519_005338.]: Samplename: Sample#1 Root: 81.042 --> Root in between the borders! Added to results. Hyperbolic solved: 26.6441666289306 [20250519_005338.]: Samplename: Sample#10 Root: 26.644 --> Root in between the borders! Added to results. Hyperbolic solved: 45.7046199658262 [20250519_005338.]: Samplename: Sample#2 Root: 45.705 --> Root in between the borders! Added to results. Hyperbolic solved: 56.9767986854544 [20250519_005338.]: Samplename: Sample#3 Root: 56.977 --> Root in between the borders! Added to results. Hyperbolic solved: 6.81231222322667 [20250519_005338.]: Samplename: Sample#4 Root: 6.812 --> Root in between the borders! Added to results. Hyperbolic solved: 22.9284780361346 [20250519_005338.]: Samplename: Sample#5 Root: 22.928 --> Root in between the borders! Added to results. Hyperbolic solved: 38.8653203361426 [20250519_005338.]: Samplename: Sample#6 Root: 38.865 --> Root in between the borders! Added to results. Hyperbolic solved: 43.6855631975613 [20250519_005338.]: Samplename: Sample#7 Root: 43.686 --> Root in between the borders! Added to results. Hyperbolic solved: 88.0155364495973 [20250519_005338.]: Samplename: Sample#8 Root: 88.016 --> Root in between the borders! Added to results. Hyperbolic solved: -3.35610072407525 [20250519_005338.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -3.356 --> '-10 < root < 0' --> substitute 0 [20250519_005338.]: Solving hyperbolic regression for row_means Hyperbolic solved: 77.0692799954538 [20250519_005338.]: Samplename: Sample#1 Root: 77.069 --> Root in between the borders! Added to results. Hyperbolic solved: 28.3620043244584 [20250519_005338.]: Samplename: Sample#10 Root: 28.362 --> Root in between the borders! Added to results. Hyperbolic solved: 42.5026175468089 [20250519_005338.]: Samplename: Sample#2 Root: 42.503 --> Root in between the borders! Added to results. Hyperbolic solved: 57.2972050444248 [20250519_005338.]: Samplename: Sample#3 Root: 57.297 --> Root in between the borders! Added to results. Hyperbolic solved: 8.82704012618393 [20250519_005338.]: Samplename: Sample#4 Root: 8.827 --> Root in between the borders! Added to results. Hyperbolic solved: 21.8102592524881 [20250519_005338.]: Samplename: Sample#5 Root: 21.81 --> Root in between the borders! Added to results. Hyperbolic solved: 28.7228660045975 [20250519_005338.]: Samplename: Sample#6 Root: 28.723 --> Root in between the borders! Added to results. Hyperbolic solved: 43.410510290696 [20250519_005338.]: Samplename: Sample#7 Root: 43.411 --> Root in between the borders! Added to results. Hyperbolic solved: 86.4143551963301 [20250519_005338.]: Samplename: Sample#8 Root: 86.414 --> Root in between the borders! Added to results. Hyperbolic solved: -0.237020571201611 [20250519_005338.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.237 --> '-10 < root < 0' --> substitute 0 [20250519_005338.]: on_start: using future::plan("sequential") [20250519_005338.]: Entered 'clean_dt'-Function [20250519_005338.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_005338.]: got experimental data [20250519_005338.]: Entered 'clean_dt'-Function [20250519_005338.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_005338.]: got calibration data [20250519_005338.]: ### Starting with regression calculations ### [20250519_005338.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_005338.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005338.]: Logging df_agg: CpG#1 [20250519_005338.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005338.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005338.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005338.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005338.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005338.]: Entered 'hyperbolic_regression'-Function [20250519_005338.]: 'hyperbolic_regression': minmax = FALSE [20250519_005338.]: Entered 'cubic_regression'-Function [20250519_005338.]: 'cubic_regression': minmax = FALSE [20250519_005338.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005338.]: Logging df_agg: CpG#2 [20250519_005338.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005338.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005338.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005338.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005338.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005338.]: Entered 'hyperbolic_regression'-Function [20250519_005338.]: 'hyperbolic_regression': minmax = FALSE [20250519_005338.]: Entered 'cubic_regression'-Function [20250519_005338.]: 'cubic_regression': minmax = FALSE [20250519_005338.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005338.]: Logging df_agg: CpG#3 [20250519_005338.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005338.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005338.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005338.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005338.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005338.]: Entered 'hyperbolic_regression'-Function [20250519_005338.]: 'hyperbolic_regression': minmax = FALSE [20250519_005339.]: Entered 'cubic_regression'-Function [20250519_005339.]: 'cubic_regression': minmax = FALSE [20250519_005339.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005339.]: Logging df_agg: CpG#4 [20250519_005339.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005339.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005339.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005339.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005339.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005339.]: Entered 'hyperbolic_regression'-Function [20250519_005339.]: 'hyperbolic_regression': minmax = FALSE [20250519_005339.]: Entered 'cubic_regression'-Function [20250519_005339.]: 'cubic_regression': minmax = FALSE [20250519_005339.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005339.]: Logging df_agg: CpG#5 [20250519_005339.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005339.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005339.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005339.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005339.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005339.]: Entered 'hyperbolic_regression'-Function [20250519_005339.]: 'hyperbolic_regression': minmax = FALSE [20250519_005339.]: Entered 'cubic_regression'-Function [20250519_005339.]: 'cubic_regression': minmax = FALSE [20250519_005339.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005339.]: Logging df_agg: CpG#6 [20250519_005339.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005339.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005339.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005339.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005339.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005339.]: Entered 'hyperbolic_regression'-Function [20250519_005339.]: 'hyperbolic_regression': minmax = FALSE [20250519_005339.]: Entered 'cubic_regression'-Function [20250519_005339.]: 'cubic_regression': minmax = FALSE [20250519_005339.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005339.]: Logging df_agg: CpG#7 [20250519_005339.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005339.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005339.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005339.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005339.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005339.]: Entered 'hyperbolic_regression'-Function [20250519_005339.]: 'hyperbolic_regression': minmax = FALSE [20250519_005339.]: Entered 'cubic_regression'-Function [20250519_005339.]: 'cubic_regression': minmax = FALSE [20250519_005339.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005339.]: Logging df_agg: CpG#8 [20250519_005339.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005339.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005339.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005339.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005339.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005339.]: Entered 'hyperbolic_regression'-Function [20250519_005339.]: 'hyperbolic_regression': minmax = FALSE [20250519_005340.]: Entered 'cubic_regression'-Function [20250519_005340.]: 'cubic_regression': minmax = FALSE [20250519_005340.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005340.]: Logging df_agg: CpG#9 [20250519_005340.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005340.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005340.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005340.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005340.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005340.]: Entered 'hyperbolic_regression'-Function [20250519_005340.]: 'hyperbolic_regression': minmax = FALSE [20250519_005340.]: Entered 'cubic_regression'-Function [20250519_005340.]: 'cubic_regression': minmax = FALSE [20250519_005340.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723122, 2.47760998435345)c(5.94911111111111, 0.84962962962963, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703704, 19.6995555555555, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005340.]: Logging df_agg: row_means [20250519_005340.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005340.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005340.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723122, 2.47760998435345)[20250519_005340.]: c(5.94911111111111, 0.84962962962963, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005340.]: c(NA, 6.79703703703704, 19.6995555555555, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005340.]: Entered 'hyperbolic_regression'-Function [20250519_005340.]: 'hyperbolic_regression': minmax = FALSE [20250519_005340.]: Entered 'cubic_regression'-Function [20250519_005340.]: 'cubic_regression': minmax = FALSE [20250519_005341.]: ### Starting with plotting ### [20250519_005341.]: Creating plot No. 1 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG1.png [20250519_005341.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -937.71833725671 , a = -108.568188547925 , b = -937.71833725671 , d = -232.05735859911 [20250519_005341.]: # CpG-site: CpG#1 Cubic: Using a = 6.53413423120091e-05 , b = -0.0055806968734969 , c = 0.784061853455188 , d = 1.93182659932656 [20250519_005343.]: Creating plot No. 2 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG2.png [20250519_005343.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = -2393.14264996499 , a = -141.862267639951 , b = -2393.14264996499 , d = -268.331587255735 [20250519_005343.]: # CpG-site: CpG#2 Cubic: Using a = 8.04237934904601e-06 , b = 0.00293158941798942 , c = 0.514821742825076 , d = 9.27667003367003 [20250519_005345.]: Creating plot No. 3 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG3.png [20250519_005345.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = -1477.46867711742 , a = -170.424279984248 , b = -1477.46867711742 , d = -327.189866698889 [20250519_005345.]: # CpG-site: CpG#3 Cubic: Using a = 2.30555869809204e-05 , b = -0.000797009331409346 , c = 0.62783129228796 , d = 3.88705218855218 [20250519_005347.]: Creating plot No. 4 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG4.png [20250519_005347.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = -2115.87814484424 , a = -162.169929539519 , b = -2115.87814484424 , d = -299.662454768746 [20250519_005347.]: # CpG-site: CpG#4 Cubic: Using a = 3.2166356902357e-05 , b = -0.0015913142857143 , c = 0.697398364598366 , d = 6.17055050505048 [20250519_005350.]: Creating plot No. 5 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG5.png [20250519_005350.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = -1383.32734884945 , a = -151.440088502132 , b = -1383.32734884945 , d = -304.200280997675 [20250519_005350.]: # CpG-site: CpG#5 Cubic: Using a = -4.48459708193036e-06 , b = 0.00375371236171235 , c = 0.422446384479718 , d = 5.17203872053872 [20250519_005352.]: Creating plot No. 6 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG6.png [20250519_005352.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = -1550.59304894212 , a = -125.016150436792 , b = -1550.59304894212 , d = -247.279683783527 [20250519_005352.]: # CpG-site: CpG#6 Cubic: Using a = 2.38852884399552e-05 , b = 0.000918637037037021 , c = 0.561022132435467 , d = 6.1479276094276 [20250519_005354.]: Creating plot No. 7 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG7.png [20250519_005354.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -772.843265948977 , a = -75.2726697123378 , b = -772.843265948977 , d = -226.67343046903 [20250519_005354.]: # CpG-site: CpG#7 Cubic: Using a = 4.98010505050505e-05 , b = -0.00436152150072151 , c = 0.579588917748918 , d = 1.72104545454544 [20250519_005356.]: Creating plot No. 8 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG8.png [20250519_005356.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = -3872.73534838154 , a = -278.017155483789 , b = -3872.73534838154 , d = -438.894126934244 [20250519_005356.]: # CpG-site: CpG#8 Cubic: Using a = -1.50060965207632e-05 , b = 0.00447978143338143 , c = 0.534897737694404 , d = 9.56981481481482 [20250519_005358.]: Creating plot No. 9 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG9.png [20250519_005358.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -843.925271611492 , a = -70.6796326247736 , b = -843.925271611492 , d = -184.482178799669 [20250519_005358.]: # CpG-site: CpG#9 Cubic: Using a = 7.72300426487093e-05 , b = -0.00542281173641174 , c = 0.67899229597563 , d = 2.72413468013467 [20250519_005400.]: Creating plot No. 10 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_rowmeans.png [20250519_005400.]: # CpG-site: row_means Hyperbolic: Using bias_weight = -1525.75751056682 , a = -127.675093193972 , b = -1525.75751056682 , d = -263.003121358972 [20250519_005400.]: # CpG-site: row_means Cubic: Using a = 2.88923726150392e-05 , b = -0.0006299592752926 , c = 0.600117857944524 , d = 5.17789562289563 [20250519_005403.]: Entered 'solving_equations'-Function [20250519_005403.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: -2.23220911684257 [20250519_005403.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.232 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.1698564372971 [20250519_005403.]: Samplename: 12.5 Root: 12.17 --> Root in between the borders! Added to results. Hyperbolic solved: 24.478190918656 [20250519_005403.]: Samplename: 25 Root: 24.478 --> Root in between the borders! Added to results. Hyperbolic solved: 38.1730371634976 [20250519_005403.]: Samplename: 37.5 Root: 38.173 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3349263290239 [20250519_005403.]: Samplename: 50 Root: 52.335 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4582665283612 [20250519_005403.]: Samplename: 62.5 Root: 65.458 --> Root in between the borders! Added to results. Hyperbolic solved: 75.0090705794847 [20250519_005403.]: Samplename: 75 Root: 75.009 --> Root in between the borders! Added to results. Hyperbolic solved: 81.5271853400487 [20250519_005403.]: Samplename: 87.5 Root: 81.527 --> Root in between the borders! Added to results. Hyperbolic solved: 102.400898271831 [20250519_005403.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.401 --> '100 < root < 110' --> substitute 100 [20250519_005403.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 1.13660511681856 [20250519_005403.]: Samplename: 0 Root: 1.137 --> Root in between the borders! Added to results. Hyperbolic solved: 11.4129696918445 [20250519_005403.]: Samplename: 12.5 Root: 11.413 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1740004612661 [20250519_005403.]: Samplename: 25 Root: 26.174 --> Root in between the borders! Added to results. Hyperbolic solved: 35.1050448132614 [20250519_005403.]: Samplename: 37.5 Root: 35.105 --> Root in between the borders! Added to results. Hyperbolic solved: 47.6855002074492 [20250519_005403.]: Samplename: 50 Root: 47.686 --> Root in between the borders! Added to results. Hyperbolic solved: 67.1440493313574 [20250519_005403.]: Samplename: 62.5 Root: 67.144 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7644668045486 [20250519_005403.]: Samplename: 75 Root: 75.764 --> Root in between the borders! Added to results. Hyperbolic solved: 84.4054158145455 [20250519_005403.]: Samplename: 87.5 Root: 84.405 --> Root in between the borders! Added to results. Hyperbolic solved: 100.948272543309 [20250519_005403.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.948 --> '100 < root < 110' --> substitute 100 [20250519_005403.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 0.512348561877098 [20250519_005403.]: Samplename: 0 Root: 0.512 --> Root in between the borders! Added to results. Hyperbolic solved: 10.7523845008661 [20250519_005403.]: Samplename: 12.5 Root: 10.752 --> Root in between the borders! Added to results. Hyperbolic solved: 25.5218912317424 [20250519_005403.]: Samplename: 25 Root: 25.522 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5270488279988 [20250519_005403.]: Samplename: 37.5 Root: 36.527 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7909283805111 [20250519_005403.]: Samplename: 50 Root: 50.791 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8686353441458 [20250519_005403.]: Samplename: 62.5 Root: 64.869 --> Root in between the borders! Added to results. Hyperbolic solved: 77.5524208271734 [20250519_005403.]: Samplename: 75 Root: 77.552 --> Root in between the borders! Added to results. Hyperbolic solved: 80.4374631680835 [20250519_005403.]: Samplename: 87.5 Root: 80.437 --> Root in between the borders! Added to results. Hyperbolic solved: 102.704019894026 [20250519_005403.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.704 --> '100 < root < 110' --> substitute 100 [20250519_005403.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: -0.519496428117613 [20250519_005403.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.519 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.4934173132007 [20250519_005403.]: Samplename: 12.5 Root: 12.493 --> Root in between the borders! Added to results. Hyperbolic solved: 24.2685417185317 [20250519_005403.]: Samplename: 25 Root: 24.269 --> Root in between the borders! Added to results. Hyperbolic solved: 38.0817103888785 [20250519_005403.]: Samplename: 37.5 Root: 38.082 --> Root in between the borders! Added to results. Hyperbolic solved: 48.5843148203973 [20250519_005403.]: Samplename: 50 Root: 48.584 --> Root in between the borders! Added to results. Hyperbolic solved: 67.672236894341 [20250519_005403.]: Samplename: 62.5 Root: 67.672 --> Root in between the borders! Added to results. Hyperbolic solved: 74.1549253766253 [20250519_005403.]: Samplename: 75 Root: 74.155 --> Root in between the borders! Added to results. Hyperbolic solved: 82.8821786440368 [20250519_005403.]: Samplename: 87.5 Root: 82.882 --> Root in between the borders! Added to results. Hyperbolic solved: 102.079130232402 [20250519_005403.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.079 --> '100 < root < 110' --> substitute 100 [20250519_005403.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 2.41558718123367 [20250519_005403.]: Samplename: 0 Root: 2.416 --> Root in between the borders! Added to results. Hyperbolic solved: 10.1649680545674 [20250519_005403.]: Samplename: 12.5 Root: 10.165 --> Root in between the borders! Added to results. Hyperbolic solved: 23.9830821073418 [20250519_005403.]: Samplename: 25 Root: 23.983 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2773617400679 [20250519_005403.]: Samplename: 37.5 Root: 37.277 --> Root in between the borders! Added to results. Hyperbolic solved: 50.8659382464167 [20250519_005403.]: Samplename: 50 Root: 50.866 --> Root in between the borders! Added to results. Hyperbolic solved: 62.4342269461158 [20250519_005403.]: Samplename: 62.5 Root: 62.434 --> Root in between the borders! Added to results. Hyperbolic solved: 76.3915257997575 [20250519_005403.]: Samplename: 75 Root: 76.392 --> Root in between the borders! Added to results. Hyperbolic solved: 86.1597887396996 [20250519_005403.]: Samplename: 87.5 Root: 86.16 --> Root in between the borders! Added to results. Hyperbolic solved: 100.267760316884 [20250519_005403.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.268 --> '100 < root < 110' --> substitute 100 [20250519_005403.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 0.138164161050506 [20250519_005403.]: Samplename: 0 Root: 0.138 --> Root in between the borders! Added to results. Hyperbolic solved: 11.8635561171135 [20250519_005403.]: Samplename: 12.5 Root: 11.864 --> Root in between the borders! Added to results. Hyperbolic solved: 26.510745006666 [20250519_005403.]: Samplename: 25 Root: 26.511 --> Root in between the borders! Added to results. Hyperbolic solved: 35.3205072777727 [20250519_005403.]: Samplename: 37.5 Root: 35.321 --> Root in between the borders! Added to results. Hyperbolic solved: 50.0570766444694 [20250519_005403.]: Samplename: 50 Root: 50.057 --> Root in between the borders! Added to results. Hyperbolic solved: 64.9602942986125 [20250519_005403.]: Samplename: 62.5 Root: 64.96 --> Root in between the borders! Added to results. Hyperbolic solved: 73.6689055886064 [20250519_005403.]: Samplename: 75 Root: 73.669 --> Root in between the borders! Added to results. Hyperbolic solved: 87.1266085894673 [20250519_005403.]: Samplename: 87.5 Root: 87.127 --> Root in between the borders! Added to results. Hyperbolic solved: 100.261637049252 [20250519_005403.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.262 --> '100 < root < 110' --> substitute 100 [20250519_005403.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: -1.37235519395329 [20250519_005403.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -1.372 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.1993294013604 [20250519_005403.]: Samplename: 12.5 Root: 10.199 --> Root in between the borders! Added to results. Hyperbolic solved: 24.5951803850766 [20250519_005403.]: Samplename: 25 Root: 24.595 --> Root in between the borders! Added to results. Hyperbolic solved: 37.8310364564335 [20250519_005403.]: Samplename: 37.5 Root: 37.831 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5588645818264 [20250519_005403.]: Samplename: 50 Root: 53.559 --> Root in between the borders! Added to results. Hyperbolic solved: 65.9364860153449 [20250519_005403.]: Samplename: 62.5 Root: 65.936 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7361033620359 [20250519_005403.]: Samplename: 75 Root: 75.736 --> Root in between the borders! Added to results. Hyperbolic solved: 79.4328192070151 [20250519_005403.]: Samplename: 87.5 Root: 79.433 --> Root in between the borders! Added to results. Hyperbolic solved: 103.004248773309 [20250519_005403.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 103.004 --> '100 < root < 110' --> substitute 100 [20250519_005403.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 2.80068093389159 [20250519_005403.]: Samplename: 0 Root: 2.801 --> Root in between the borders! Added to results. Hyperbolic solved: 9.27535066703723 [20250519_005403.]: Samplename: 12.5 Root: 9.275 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4762625870809 [20250519_005403.]: Samplename: 25 Root: 25.476 --> Root in between the borders! Added to results. Hyperbolic solved: 34.0122083314201 [20250519_005403.]: Samplename: 37.5 Root: 34.012 --> Root in between the borders! Added to results. Hyperbolic solved: 51.7842666342759 [20250519_005403.]: Samplename: 50 Root: 51.784 --> Root in between the borders! Added to results. Hyperbolic solved: 64.6732321058682 [20250519_005403.]: Samplename: 62.5 Root: 64.673 --> Root in between the borders! Added to results. Hyperbolic solved: 78.4326982875914 [20250519_005403.]: Samplename: 75 Root: 78.433 --> Root in between the borders! Added to results. Hyperbolic solved: 81.3427235319597 [20250519_005403.]: Samplename: 87.5 Root: 81.343 --> Root in between the borders! Added to results. Hyperbolic solved: 101.964405325155 [20250519_005403.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.964 --> '100 < root < 110' --> substitute 100 [20250519_005403.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: -2.1340328997008 [20250519_005403.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.134 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.5082212970134 [20250519_005403.]: Samplename: 12.5 Root: 10.508 --> Root in between the borders! Added to results. Hyperbolic solved: 26.9164566258777 [20250519_005403.]: Samplename: 25 Root: 26.916 --> Root in between the borders! Added to results. Hyperbolic solved: 36.8334769505904 [20250519_005403.]: Samplename: 37.5 Root: 36.833 --> Root in between the borders! Added to results. Hyperbolic solved: 52.0097879402751 [20250519_005403.]: Samplename: 50 Root: 52.01 --> Root in between the borders! Added to results. Hyperbolic solved: 64.893051148082 [20250519_005403.]: Samplename: 62.5 Root: 64.893 --> Root in between the borders! Added to results. Hyperbolic solved: 74.5671042786367 [20250519_005403.]: Samplename: 75 Root: 74.567 --> Root in between the borders! Added to results. Hyperbolic solved: 84.5294949210319 [20250519_005403.]: Samplename: 87.5 Root: 84.529 --> Root in between the borders! Added to results. Hyperbolic solved: 101.047147807246 [20250519_005403.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.047 --> '100 < root < 110' --> substitute 100 [20250519_005403.]: Solving hyperbolic regression for row_means Hyperbolic solved: 0.290944901553815 [20250519_005403.]: Samplename: 0 Root: 0.291 --> Root in between the borders! Added to results. Hyperbolic solved: 11.0412427310408 [20250519_005403.]: Samplename: 12.5 Root: 11.041 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4081501338029 [20250519_005403.]: Samplename: 25 Root: 25.408 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5243709582085 [20250519_005403.]: Samplename: 37.5 Root: 36.524 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7348808712519 [20250519_005403.]: Samplename: 50 Root: 50.735 --> Root in between the borders! Added to results. Hyperbolic solved: 65.3135195123017 [20250519_005403.]: Samplename: 62.5 Root: 65.314 --> Root in between the borders! Added to results. Hyperbolic solved: 75.5342699494589 [20250519_005403.]: Samplename: 75 Root: 75.534 --> Root in between the borders! Added to results. Hyperbolic solved: 83.2411225127701 [20250519_005403.]: Samplename: 87.5 Root: 83.241 --> Root in between the borders! Added to results. Hyperbolic solved: 101.666944784378 [20250519_005403.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.667 --> '100 < root < 110' --> substitute 100 [20250519_005403.]: ### Starting with regression calculations ### [20250519_005403.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_005403.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.1698564372971, 24.478190918656, 38.1730371634976, 52.3349263290239, 65.4582665283612, 75.0090705794847, 81.5271853400487, 100)c(0, 0.330143562702901, 0.521809081343999, 0.673037163497597, 2.3349263290239, 2.95826652836119, 0.00907057948469969, 5.9728146599513, 0)c(NA, 2.64114850162321, 2.087236325376, 1.79476576932692, 4.6698526580478, 4.73322644537791, 0.0120941059795996, 6.8260738970872, 0) [20250519_005403.]: Logging df_agg: CpG#1 [20250519_005403.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005403.]: c(0, 12.1698564372971, 24.478190918656, 38.1730371634976, 52.3349263290239, 65.4582665283612, 75.0090705794847, 81.5271853400487, 100)[20250519_005403.]: c(0, 0.330143562702901, 0.521809081343999, 0.673037163497597, 2.3349263290239, 2.95826652836119, 0.00907057948469969, 5.9728146599513, 0)[20250519_005403.]: c(NA, 2.64114850162321, 2.087236325376, 1.79476576932692, 4.6698526580478, 4.73322644537791, 0.0120941059795996, 6.8260738970872, 0) [20250519_005403.]: Entered 'hyperbolic_regression'-Function [20250519_005403.]: 'hyperbolic_regression': minmax = FALSE [20250519_005403.]: Entered 'cubic_regression'-Function [20250519_005403.]: 'cubic_regression': minmax = FALSE [20250519_005403.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.13660511681856, 11.4129696918445, 26.1740004612661, 35.1050448132614, 47.6855002074492, 67.1440493313574, 75.7644668045486, 84.4054158145455, 100)c(1.13660511681856, 1.0870303081555, 1.1740004612661, 2.3949551867386, 2.3144997925508, 4.64404933135739, 0.764466804548604, 3.09458418545449, 0)c(NA, 8.696242465244, 4.6960018450644, 6.38654716463626, 4.6289995851016, 7.43047893017183, 1.01928907273147, 3.53666764051942, 0) [20250519_005403.]: Logging df_agg: CpG#2 [20250519_005403.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005403.]: c(1.13660511681856, 11.4129696918445, 26.1740004612661, 35.1050448132614, 47.6855002074492, 67.1440493313574, 75.7644668045486, 84.4054158145455, 100)[20250519_005403.]: c(1.13660511681856, 1.0870303081555, 1.1740004612661, 2.3949551867386, 2.3144997925508, 4.64404933135739, 0.764466804548604, 3.09458418545449, 0)[20250519_005403.]: c(NA, 8.696242465244, 4.6960018450644, 6.38654716463626, 4.6289995851016, 7.43047893017183, 1.01928907273147, 3.53666764051942, 0) [20250519_005403.]: Entered 'hyperbolic_regression'-Function [20250519_005403.]: 'hyperbolic_regression': minmax = FALSE [20250519_005404.]: Entered 'cubic_regression'-Function [20250519_005404.]: 'cubic_regression': minmax = FALSE [20250519_005404.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.512348561877098, 10.7523845008661, 25.5218912317424, 36.5270488279988, 50.7909283805111, 64.8686353441458, 77.5524208271734, 80.4374631680835, 100)c(0.512348561877098, 1.7476154991339, 0.521891231742401, 0.972951172001203, 0.790928380511097, 2.3686353441458, 2.55242082717341, 7.0625368319165, 0)c(NA, 13.9809239930712, 2.08756492696961, 2.59453645866987, 1.58185676102219, 3.78981655063328, 3.40322776956454, 8.07147066504743, 0) [20250519_005404.]: Logging df_agg: CpG#3 [20250519_005404.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005404.]: c(0.512348561877098, 10.7523845008661, 25.5218912317424, 36.5270488279988, 50.7909283805111, 64.8686353441458, 77.5524208271734, 80.4374631680835, 100)[20250519_005404.]: c(0.512348561877098, 1.7476154991339, 0.521891231742401, 0.972951172001203, 0.790928380511097, 2.3686353441458, 2.55242082717341, 7.0625368319165, 0)[20250519_005404.]: c(NA, 13.9809239930712, 2.08756492696961, 2.59453645866987, 1.58185676102219, 3.78981655063328, 3.40322776956454, 8.07147066504743, 0) [20250519_005404.]: Entered 'hyperbolic_regression'-Function [20250519_005404.]: 'hyperbolic_regression': minmax = FALSE [20250519_005404.]: Entered 'cubic_regression'-Function [20250519_005404.]: 'cubic_regression': minmax = FALSE [20250519_005404.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.4934173132007, 24.2685417185317, 38.0817103888785, 48.5843148203973, 67.672236894341, 74.1549253766253, 82.8821786440368, 100)c(0, 0.00658268679929996, 0.731458281468299, 0.581710388878498, 1.4156851796027, 5.172236894341, 0.8450746233747, 4.6178213559632, 0)c(NA, 0.0526614943943997, 2.9258331258732, 1.551227703676, 2.83137035920539, 8.27557903094559, 1.1267661644996, 5.2775101211008, 0) [20250519_005404.]: Logging df_agg: CpG#4 [20250519_005404.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005404.]: c(0, 12.4934173132007, 24.2685417185317, 38.0817103888785, 48.5843148203973, 67.672236894341, 74.1549253766253, 82.8821786440368, 100)[20250519_005404.]: c(0, 0.00658268679929996, 0.731458281468299, 0.581710388878498, 1.4156851796027, 5.172236894341, 0.8450746233747, 4.6178213559632, 0)[20250519_005404.]: c(NA, 0.0526614943943997, 2.9258331258732, 1.551227703676, 2.83137035920539, 8.27557903094559, 1.1267661644996, 5.2775101211008, 0) [20250519_005404.]: Entered 'hyperbolic_regression'-Function [20250519_005404.]: 'hyperbolic_regression': minmax = FALSE [20250519_005404.]: Entered 'cubic_regression'-Function [20250519_005404.]: 'cubic_regression': minmax = FALSE [20250519_005404.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.41558718123367, 10.1649680545674, 23.9830821073418, 37.2773617400679, 50.8659382464167, 62.4342269461158, 76.3915257997575, 86.1597887396996, 100)c(2.41558718123367, 2.3350319454326, 1.0169178926582, 0.222638259932097, 0.865938246416697, 0.0657730538842003, 1.3915257997575, 1.34021126030041, 0)c(NA, 18.6802555634608, 4.0676715706328, 0.593702026485592, 1.73187649283339, 0.10523688621472, 1.85536773301, 1.53167001177189, 0) [20250519_005404.]: Logging df_agg: CpG#5 [20250519_005404.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005404.]: c(2.41558718123367, 10.1649680545674, 23.9830821073418, 37.2773617400679, 50.8659382464167, 62.4342269461158, 76.3915257997575, 86.1597887396996, 100)[20250519_005404.]: c(2.41558718123367, 2.3350319454326, 1.0169178926582, 0.222638259932097, 0.865938246416697, 0.0657730538842003, 1.3915257997575, 1.34021126030041, 0)[20250519_005404.]: c(NA, 18.6802555634608, 4.0676715706328, 0.593702026485592, 1.73187649283339, 0.10523688621472, 1.85536773301, 1.53167001177189, 0) [20250519_005404.]: Entered 'hyperbolic_regression'-Function [20250519_005404.]: 'hyperbolic_regression': minmax = FALSE [20250519_005404.]: Entered 'cubic_regression'-Function [20250519_005404.]: 'cubic_regression': minmax = FALSE [20250519_005404.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.138164161050506, 11.8635561171135, 26.510745006666, 35.3205072777727, 50.0570766444694, 64.9602942986125, 73.6689055886064, 87.1266085894673, 100)c(0.138164161050506, 0.6364438828865, 1.510745006666, 2.1794927222273, 0.0570766444693973, 2.4602942986125, 1.33109441139359, 0.373391410532705, 0)c(NA, 5.091551063092, 6.042980026664, 5.81198059260614, 0.114153288938795, 3.93647087777999, 1.77479254852479, 0.426733040608805, 0) [20250519_005404.]: Logging df_agg: CpG#6 [20250519_005404.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005404.]: c(0.138164161050506, 11.8635561171135, 26.510745006666, 35.3205072777727, 50.0570766444694, 64.9602942986125, 73.6689055886064, 87.1266085894673, 100)[20250519_005404.]: c(0.138164161050506, 0.6364438828865, 1.510745006666, 2.1794927222273, 0.0570766444693973, 2.4602942986125, 1.33109441139359, 0.373391410532705, 0)[20250519_005404.]: c(NA, 5.091551063092, 6.042980026664, 5.81198059260614, 0.114153288938795, 3.93647087777999, 1.77479254852479, 0.426733040608805, 0) [20250519_005404.]: Entered 'hyperbolic_regression'-Function [20250519_005404.]: 'hyperbolic_regression': minmax = FALSE [20250519_005404.]: Entered 'cubic_regression'-Function [20250519_005404.]: 'cubic_regression': minmax = FALSE [20250519_005404.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.1993294013604, 24.5951803850766, 37.8310364564335, 53.5588645818264, 65.9364860153449, 75.7361033620359, 79.4328192070151, 100)c(0, 2.3006705986396, 0.404819614923401, 0.331036456433502, 3.5588645818264, 3.4364860153449, 0.736103362035905, 8.0671807929849, 0)c(NA, 18.4053647891168, 1.6192784596936, 0.882763883822671, 7.1177291636528, 5.49837762455184, 0.981471149381207, 9.21963519198274, 0) [20250519_005404.]: Logging df_agg: CpG#7 [20250519_005404.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005404.]: c(0, 10.1993294013604, 24.5951803850766, 37.8310364564335, 53.5588645818264, 65.9364860153449, 75.7361033620359, 79.4328192070151, 100)[20250519_005404.]: c(0, 2.3006705986396, 0.404819614923401, 0.331036456433502, 3.5588645818264, 3.4364860153449, 0.736103362035905, 8.0671807929849, 0)[20250519_005404.]: c(NA, 18.4053647891168, 1.6192784596936, 0.882763883822671, 7.1177291636528, 5.49837762455184, 0.981471149381207, 9.21963519198274, 0) [20250519_005404.]: Entered 'hyperbolic_regression'-Function [20250519_005405.]: 'hyperbolic_regression': minmax = FALSE [20250519_005405.]: Entered 'cubic_regression'-Function [20250519_005405.]: 'cubic_regression': minmax = FALSE [20250519_005405.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.80068093389159, 9.27535066703723, 25.4762625870809, 34.0122083314201, 51.7842666342759, 64.6732321058682, 78.4326982875914, 81.3427235319597, 100)c(2.80068093389159, 3.22464933296277, 0.4762625870809, 3.4877916685799, 1.7842666342759, 2.17323210586819, 3.43269828759141, 6.15727646804029, 0)c(NA, 25.7971946637022, 1.9050503483236, 9.30077778287974, 3.5685332685518, 3.47717136938911, 4.57693105012188, 7.03688739204605, 0) [20250519_005405.]: Logging df_agg: CpG#8 [20250519_005405.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005405.]: c(2.80068093389159, 9.27535066703723, 25.4762625870809, 34.0122083314201, 51.7842666342759, 64.6732321058682, 78.4326982875914, 81.3427235319597, 100)[20250519_005405.]: c(2.80068093389159, 3.22464933296277, 0.4762625870809, 3.4877916685799, 1.7842666342759, 2.17323210586819, 3.43269828759141, 6.15727646804029, 0)[20250519_005405.]: c(NA, 25.7971946637022, 1.9050503483236, 9.30077778287974, 3.5685332685518, 3.47717136938911, 4.57693105012188, 7.03688739204605, 0) [20250519_005405.]: Entered 'hyperbolic_regression'-Function [20250519_005405.]: 'hyperbolic_regression': minmax = FALSE [20250519_005405.]: Entered 'cubic_regression'-Function [20250519_005405.]: 'cubic_regression': minmax = FALSE [20250519_005405.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.5082212970134, 26.9164566258777, 36.8334769505904, 52.0097879402751, 64.893051148082, 74.5671042786367, 84.5294949210319, 100)c(0, 1.9917787029866, 1.9164566258777, 0.666523049409598, 2.0097879402751, 2.39305114808199, 0.432895721363295, 2.9705050789681, 0)c(NA, 15.9342296238928, 7.6658265035108, 1.77739479842559, 4.01957588055021, 3.82888183693119, 0.57719429515106, 3.39486294739212, 0) [20250519_005405.]: Logging df_agg: CpG#9 [20250519_005405.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005405.]: c(0, 10.5082212970134, 26.9164566258777, 36.8334769505904, 52.0097879402751, 64.893051148082, 74.5671042786367, 84.5294949210319, 100)[20250519_005405.]: c(0, 1.9917787029866, 1.9164566258777, 0.666523049409598, 2.0097879402751, 2.39305114808199, 0.432895721363295, 2.9705050789681, 0)[20250519_005405.]: c(NA, 15.9342296238928, 7.6658265035108, 1.77739479842559, 4.01957588055021, 3.82888183693119, 0.57719429515106, 3.39486294739212, 0) [20250519_005405.]: Entered 'hyperbolic_regression'-Function [20250519_005405.]: 'hyperbolic_regression': minmax = FALSE [20250519_005405.]: Entered 'cubic_regression'-Function [20250519_005405.]: 'cubic_regression': minmax = FALSE [20250519_005405.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.290944901553815, 11.0412427310408, 25.4081501338029, 36.5243709582085, 50.7348808712519, 65.3135195123017, 75.5342699494589, 83.2411225127701, 100)c(0.290944901553815, 1.4587572689592, 0.408150133802899, 0.9756290417915, 0.734880871251903, 2.8135195123017, 0.534269949458903, 4.2588774872299, 0)c(NA, 11.6700581516736, 1.63260053521159, 2.60167744477733, 1.46976174250381, 4.50163121968271, 0.71235993261187, 4.86728855683417, 0) [20250519_005405.]: Logging df_agg: row_means [20250519_005405.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005405.]: c(0.290944901553815, 11.0412427310408, 25.4081501338029, 36.5243709582085, 50.7348808712519, 65.3135195123017, 75.5342699494589, 83.2411225127701, 100)[20250519_005405.]: c(0.290944901553815, 1.4587572689592, 0.408150133802899, 0.9756290417915, 0.734880871251903, 2.8135195123017, 0.534269949458903, 4.2588774872299, 0)[20250519_005405.]: c(NA, 11.6700581516736, 1.63260053521159, 2.60167744477733, 1.46976174250381, 4.50163121968271, 0.71235993261187, 4.86728855683417, 0) [20250519_005405.]: Entered 'hyperbolic_regression'-Function [20250519_005405.]: 'hyperbolic_regression': minmax = FALSE [20250519_005405.]: Entered 'cubic_regression'-Function [20250519_005405.]: 'cubic_regression': minmax = FALSE [20250519_005406.]: ### Starting with plotting ### [20250519_005406.]: Creating BiasCorrected (hyperbolic) plot No. 1 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG1_corrected_h.png [20250519_005406.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -657.85070009309 , a = 985.271206705952 , b = -657.85070009309 , d = 903.457967400565 [20250519_005406.]: # CpG-site: CpG#1 Cubic: Using a = 5.1847317144537e-06 , b = -0.00180593132520811 , c = 1.11404026275692 , d = -0.858597345049636 [20250519_005408.]: Creating BiasCorrected (hyperbolic) plot No. 2 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG2_corrected_h.png [20250519_005408.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = 3399.63077807263 , a = 30276.8974003894 , b = 3399.63077807263 , d = 30353.3579426379 [20250519_005408.]: # CpG-site: CpG#2 Cubic: Using a = -2.58097552031906e-05 , b = 0.00383753288450718 , c = 0.851607751869747 , d = 0.95712734045721 [20250519_005410.]: Creating BiasCorrected (hyperbolic) plot No. 3 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG3_corrected_h.png [20250519_005410.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = -945.763900328712 , a = 1333.61328542459 , b = -945.763900328712 , d = 1252.95190710086 [20250519_005410.]: # CpG-site: CpG#3 Cubic: Using a = -1.13948418252831e-05 , b = 0.000937174314708123 , c = 0.998631105115713 , d = -0.383346041194064 [20250519_005411.]: Creating BiasCorrected (hyperbolic) plot No. 4 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG4_corrected_h.png [20250519_005411.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = -754.050808709341 , a = 1637.26986382636 , b = -754.050808709341 , d = 1558.27566152171 [20250519_005411.]: # CpG-site: CpG#4 Cubic: Using a = -3.25777472656452e-06 , b = -0.000129598485618069 , c = 1.03077245158202 , d = -0.370342672245111 [20250519_005413.]: Creating BiasCorrected (hyperbolic) plot No. 5 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG5_corrected_h.png [20250519_005413.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = -1917.44470782279 , a = -5621.43051220384 , b = -1917.44470782279 , d = -5734.45004174505 [20250519_005413.]: # CpG-site: CpG#5 Cubic: Using a = -3.16782847952728e-05 , b = 0.00493527243554485 , c = 0.800194551229801 , d = 1.38653114075915 [20250519_005415.]: Creating BiasCorrected (hyperbolic) plot No. 6 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG6_corrected_h.png [20250519_005415.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = -1255.66539296107 , a = 13775.5132875201 , b = -1255.66539296107 , d = 13690.4025642689 [20250519_005415.]: # CpG-site: CpG#6 Cubic: Using a = -3.70201699726522e-06 , b = 0.000482210843999232 , c = 0.98529311552838 , d = 0.0293572853154747 [20250519_005417.]: Creating BiasCorrected (hyperbolic) plot No. 7 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG7_corrected_h.png [20250519_005417.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -1065.05219601462 , a = 722.085443879134 , b = -1065.05219601462 , d = 634.403611801521 [20250519_005417.]: # CpG-site: CpG#7 Cubic: Using a = -1.04101209223167e-06 , b = -0.00130151331776721 , c = 1.1239953084199 , d = -1.59858561306358 [20250519_005419.]: Creating BiasCorrected (hyperbolic) plot No. 8 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG8_corrected_h.png [20250519_005419.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = 68.5259802694693 , a = 4784.81913306904 , b = 68.5259802694693 , d = 4739.03101269703 [20250519_005419.]: # CpG-site: CpG#8 Cubic: Using a = -4.65730461761547e-05 , b = 0.00676525443932464 , c = 0.747405842988874 , d = 1.5233701793389 [20250519_005421.]: Creating BiasCorrected (hyperbolic) plot No. 9 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG9_corrected_h.png [20250519_005421.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -1022.70071430348 , a = 1273.73160369723 , b = -1022.70071430348 , d = 1181.86620562781 [20250519_005421.]: # CpG-site: CpG#9 Cubic: Using a = 4.24791805783875e-06 , b = -0.00144169825546529 , c = 1.09837792836035 , d = -0.978987254622136 [20250519_005422.]: Creating BiasCorrected (hyperbolic) plot No. 10 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_rowmeans_corrected_h.png [20250519_005422.]: # CpG-site: row_means Hyperbolic: Using bias_weight = -1166.73385465972 , a = 1740.64188131784 , b = -1166.73385465972 , d = 1653.86486365297 [20250519_005422.]: # CpG-site: row_means Cubic: Using a = -8.71187897501959e-06 , b = 0.000715556896368103 , c = 1.00248226010187 , d = -0.420549765740683 [20250519_005425.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG1_corrected_h.png [20250519_005426.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG2_corrected_h.png [20250519_005428.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG3_corrected_h.png [20250519_005430.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG4_corrected_h.png [20250519_005432.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG5_corrected_h.png [20250519_005433.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG6_corrected_h.png [20250519_005435.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG7_corrected_h.png [20250519_005437.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG8_corrected_h.png [20250519_005439.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG9_corrected_h.png [20250519_005440.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_rowmeans_corrected_h.png [20250519_005442.]: Entered 'solving_equations'-Function [20250519_005442.]: Solving cubic regression for CpG#1 Coefficients: -1.03617340067344Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_005442.]: Samplename: 0 Root: 1.334 --> Root in between the borders! Added to results. Coefficients: -8.34150673400678Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_005442.]: Samplename: 12.5 Root: 11.446 --> Root in between the borders! Added to results. Coefficients: -15.3881734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_005442.]: Samplename: 25 Root: 22.228 --> Root in between the borders! Added to results. Coefficients: -24.2801734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_005442.]: Samplename: 37.5 Root: 36.374 --> Root in between the borders! Added to results. Coefficients: -34.9006734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_005442.]: Samplename: 50 Root: 52.044 --> Root in between the borders! Added to results. Coefficients: -46.3541734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_005442.]: Samplename: 62.5 Root: 66.144 --> Root in between the borders! Added to results. Coefficients: -55.8931734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_005442.]: Samplename: 75 Root: 75.864 --> Root in between the borders! Added to results. Coefficients: -63.0981734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_005442.]: Samplename: 87.5 Root: 82.254 --> Root in between the borders! Added to results. Coefficients: -91.0461734006735Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_005442.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.877 --> '100 < root < 110' --> substitute 100 [20250519_005442.]: Solving cubic regression for CpG#2 Coefficients: -0.283329966329966Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_005442.]: Samplename: 0 Root: 0.549 --> Root in between the borders! Added to results. Coefficients: -6.33999663299663Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_005442.]: Samplename: 12.5 Root: 11.533 --> Root in between the borders! Added to results. Coefficients: -15.93932996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_005442.]: Samplename: 25 Root: 26.628 --> Root in between the borders! Added to results. Coefficients: -22.33732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_005442.]: Samplename: 37.5 Root: 35.509 --> Root in between the borders! Added to results. Coefficients: -32.22832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_005442.]: Samplename: 50 Root: 47.851 --> Root in between the borders! Added to results. Coefficients: -49.96332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_005442.]: Samplename: 62.5 Root: 66.893 --> Root in between the borders! Added to results. Coefficients: -58.96582996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_005442.]: Samplename: 75 Root: 75.431 --> Root in between the borders! Added to results. Coefficients: -68.8366632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_005442.]: Samplename: 87.5 Root: 84.118 --> Root in between the borders! Added to results. Coefficients: -90.57732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_005442.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.287 --> '100 < root < 110' --> substitute 100 [20250519_005442.]: Solving cubic regression for CpG#3 Coefficients: -0.90294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_005442.]: Samplename: 0 Root: 1.441 --> Root in between the borders! Added to results. Coefficients: -6.57294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_005442.]: Samplename: 12.5 Root: 10.568 --> Root in between the borders! Added to results. Coefficients: -15.4289478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_005442.]: Samplename: 25 Root: 24.796 --> Root in between the borders! Added to results. Coefficients: -22.6129478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_005442.]: Samplename: 37.5 Root: 35.952 --> Root in between the borders! Added to results. Coefficients: -32.7754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_005442.]: Samplename: 50 Root: 50.684 --> Root in between the borders! Added to results. Coefficients: -43.8889478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_005442.]: Samplename: 62.5 Root: 65.142 --> Root in between the borders! Added to results. Coefficients: -54.9754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_005442.]: Samplename: 75 Root: 77.905 --> Root in between the borders! Added to results. Coefficients: -57.6562811447812Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_005442.]: Samplename: 87.5 Root: 80.767 --> Root in between the borders! Added to results. Coefficients: -80.6649478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_005442.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.38 --> '100 < root < 110' --> substitute 100 [20250519_005442.]: Solving cubic regression for CpG#4 Coefficients: -0.597449494949524Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_005442.]: Samplename: 0 Root: 0.858 --> Root in between the borders! Added to results. Coefficients: -8.25278282828286Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_005442.]: Samplename: 12.5 Root: 12.086 --> Root in between the borders! Added to results. Coefficients: -15.8034494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_005442.]: Samplename: 25 Root: 23.316 --> Root in between the borders! Added to results. Coefficients: -25.5274494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_005442.]: Samplename: 37.5 Root: 37.383 --> Root in between the borders! Added to results. Coefficients: -33.6369494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_005442.]: Samplename: 50 Root: 48.353 --> Root in between the borders! Added to results. Coefficients: -50.2554494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_005442.]: Samplename: 62.5 Root: 68.082 --> Root in between the borders! Added to results. Coefficients: -56.5394494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_005442.]: Samplename: 75 Root: 74.615 --> Root in between the borders! Added to results. Coefficients: -65.5927828282829Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_005442.]: Samplename: 87.5 Root: 83.254 --> Root in between the borders! Added to results. Coefficients: -88.3214494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_005442.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.715 --> '100 < root < 110' --> substitute 100 [20250519_005442.]: Solving cubic regression for CpG#5 Coefficients: -0.623961279461278Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_005442.]: Samplename: 0 Root: 1.458 --> Root in between the borders! Added to results. Coefficients: -4.76796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_005442.]: Samplename: 12.5 Root: 10.347 --> Root in between the borders! Added to results. Coefficients: -12.7259612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_005442.]: Samplename: 25 Root: 24.815 --> Root in between the borders! Added to results. Coefficients: -21.1599612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_005442.]: Samplename: 37.5 Root: 37.902 --> Root in between the borders! Added to results. Coefficients: -30.6954612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_005442.]: Samplename: 50 Root: 50.977 --> Root in between the borders! Added to results. Coefficients: -39.6579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_005442.]: Samplename: 62.5 Root: 62.126 --> Root in between the borders! Added to results. Coefficients: -51.6829612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_005442.]: Samplename: 75 Root: 75.852 --> Root in between the borders! Added to results. Coefficients: -61.0146279461279Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_005442.]: Samplename: 87.5 Root: 85.767 --> Root in between the borders! Added to results. Coefficients: -76.0699612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_005442.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.743 --> '100 < root < 110' --> substitute 100 [20250519_005442.]: Solving cubic regression for CpG#6 Coefficients: -0.196072390572403Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_005442.]: Samplename: 0 Root: 0.349 --> Root in between the borders! Added to results. Coefficients: -6.73873905723907Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_005442.]: Samplename: 12.5 Root: 11.718 --> Root in between the borders! Added to results. Coefficients: -15.8880723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_005442.]: Samplename: 25 Root: 26.396 --> Root in between the borders! Added to results. Coefficients: -22.0000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_005442.]: Samplename: 37.5 Root: 35.301 --> Root in between the borders! Added to results. Coefficients: -33.4445723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_005442.]: Samplename: 50 Root: 50.134 --> Root in between the borders! Added to results. Coefficients: -46.9000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_005442.]: Samplename: 62.5 Root: 64.993 --> Root in between the borders! Added to results. Coefficients: -55.8320723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_005442.]: Samplename: 75 Root: 73.639 --> Root in between the borders! Added to results. Coefficients: -71.5454057239057Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_005442.]: Samplename: 87.5 Root: 87.043 --> Root in between the borders! Added to results. Coefficients: -89.6560723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_005442.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.329 --> '100 < root < 110' --> substitute 100 [20250519_005442.]: Solving cubic regression for CpG#7 Coefficients: -1.21495454545456Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_005442.]: Samplename: 0 Root: 2.13 --> Root in between the borders! Added to results. Coefficients: -5.39562121212123Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_005442.]: Samplename: 12.5 Root: 9.973 --> Root in between the borders! Added to results. Coefficients: -11.2649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_005442.]: Samplename: 25 Root: 22.206 --> Root in between the borders! Added to results. Coefficients: -17.4509545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_005442.]: Samplename: 37.5 Root: 35.814 --> Root in between the borders! Added to results. Coefficients: -26.0314545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_005442.]: Samplename: 50 Root: 53.28 --> Root in between the borders! Added to results. Coefficients: -33.9649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_005442.]: Samplename: 62.5 Root: 66.598 --> Root in between the borders! Added to results. Coefficients: -41.1689545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_005442.]: Samplename: 75 Root: 76.575 --> Root in between the borders! Added to results. Coefficients: -44.1356212121212Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_005442.]: Samplename: 87.5 Root: 80.219 --> Root in between the borders! Added to results. Coefficients: -67.2229545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_005442.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.506 --> '100 < root < 110' --> substitute 100 [20250519_005442.]: Solving cubic regression for CpG#8 Coefficients: -1.09618518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_005442.]: Samplename: 0 Root: 2.016 --> Root in between the borders! Added to results. Coefficients: -5.44685185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_005442.]: Samplename: 12.5 Root: 9.458 --> Root in between the borders! Added to results. Coefficients: -16.9301851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_005442.]: Samplename: 25 Root: 26.35 --> Root in between the borders! Added to results. Coefficients: -23.3501851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_005442.]: Samplename: 37.5 Root: 34.728 --> Root in between the borders! Added to results. Coefficients: -37.6251851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_005442.]: Samplename: 50 Root: 51.781 --> Root in between the borders! Added to results. Coefficients: -48.8261851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_005442.]: Samplename: 62.5 Root: 64.192 --> Root in between the borders! Added to results. Coefficients: -61.6676851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_005442.]: Samplename: 75 Root: 77.804 --> Root in between the borders! Added to results. Coefficients: -64.5101851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_005442.]: Samplename: 87.5 Root: 80.758 --> Root in between the borders! Added to results. Coefficients: -86.0601851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_005442.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.834 --> '100 < root < 110' --> substitute 100 [20250519_005442.]: Solving cubic regression for CpG#9 Coefficients: -0.989865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_005442.]: Samplename: 0 Root: 1.475 --> Root in between the borders! Added to results. Coefficients: -6.39586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_005442.]: Samplename: 12.5 Root: 10.12 --> Root in between the borders! Added to results. Coefficients: -14.7058653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_005442.]: Samplename: 25 Root: 24.844 --> Root in between the borders! Added to results. Coefficients: -20.6238653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_005442.]: Samplename: 37.5 Root: 35.327 --> Root in between the borders! Added to results. Coefficients: -31.3958653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_005442.]: Samplename: 50 Root: 51.855 --> Root in between the borders! Added to results. Coefficients: -42.6858653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_005442.]: Samplename: 62.5 Root: 65.265 --> Root in between the borders! Added to results. Coefficients: -52.9033653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_005442.]: Samplename: 75 Root: 74.915 --> Root in between the borders! Added to results. Coefficients: -65.492531986532Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_005442.]: Samplename: 87.5 Root: 84.67 --> Root in between the borders! Added to results. Coefficients: -92.9898653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_005442.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.082 --> '100 < root < 110' --> substitute 100 [20250519_005442.]: Solving cubic regression for row_means Coefficients: -0.771215488215478Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_005442.]: Samplename: 0 Root: 1.287 --> Root in between the borders! Added to results. Coefficients: -6.47247474747474Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_005442.]: Samplename: 12.5 Root: 10.847 --> Root in between the borders! Added to results. Coefficients: -14.8972154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_005442.]: Samplename: 25 Root: 24.737 --> Root in between the borders! Added to results. Coefficients: -22.1492154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_005442.]: Samplename: 37.5 Root: 36.02 --> Root in between the borders! Added to results. Coefficients: -32.5259932659933Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_005442.]: Samplename: 50 Root: 50.639 --> Root in between the borders! Added to results. Coefficients: -44.7218821548821Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_005442.]: Samplename: 62.5 Root: 65.497 --> Root in between the borders! Added to results. Coefficients: -54.4032154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_005442.]: Samplename: 75 Root: 75.751 --> Root in between the borders! Added to results. Coefficients: -62.4313636363636Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_005442.]: Samplename: 87.5 Root: 83.403 --> Root in between the borders! Added to results. Coefficients: -84.7343265993266Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_005442.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.573 --> '100 < root < 110' --> substitute 100 [20250519_005442.]: ### Starting with regression calculations ### [20250519_005442.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_005443.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) [20250519_005443.]: Logging df_agg: CpG#1 [20250519_005443.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005443.]: c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)[20250519_005443.]: c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)[20250519_005443.]: c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) [20250519_005443.]: Entered 'hyperbolic_regression'-Function [20250519_005443.]: 'hyperbolic_regression': minmax = FALSE [20250519_005443.]: Entered 'cubic_regression'-Function [20250519_005443.]: 'cubic_regression': minmax = FALSE [20250519_005443.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) [20250519_005443.]: Logging df_agg: CpG#2 [20250519_005443.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005443.]: c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)[20250519_005443.]: c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)[20250519_005443.]: c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) [20250519_005443.]: Entered 'hyperbolic_regression'-Function [20250519_005443.]: 'hyperbolic_regression': minmax = FALSE [20250519_005443.]: Entered 'cubic_regression'-Function [20250519_005443.]: 'cubic_regression': minmax = FALSE [20250519_005443.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) [20250519_005443.]: Logging df_agg: CpG#3 [20250519_005443.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005443.]: c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)[20250519_005443.]: c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)[20250519_005443.]: c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) [20250519_005443.]: Entered 'hyperbolic_regression'-Function [20250519_005443.]: 'hyperbolic_regression': minmax = FALSE [20250519_005443.]: Entered 'cubic_regression'-Function [20250519_005443.]: 'cubic_regression': minmax = FALSE [20250519_005443.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) [20250519_005443.]: Logging df_agg: CpG#4 [20250519_005443.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005443.]: c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)[20250519_005443.]: c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)[20250519_005443.]: c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) [20250519_005443.]: Entered 'hyperbolic_regression'-Function [20250519_005443.]: 'hyperbolic_regression': minmax = FALSE [20250519_005444.]: Entered 'cubic_regression'-Function [20250519_005444.]: 'cubic_regression': minmax = FALSE [20250519_005444.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) [20250519_005444.]: Logging df_agg: CpG#5 [20250519_005444.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005444.]: c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)[20250519_005444.]: c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)[20250519_005444.]: c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) [20250519_005444.]: Entered 'hyperbolic_regression'-Function [20250519_005444.]: 'hyperbolic_regression': minmax = FALSE [20250519_005444.]: Entered 'cubic_regression'-Function [20250519_005444.]: 'cubic_regression': minmax = FALSE [20250519_005444.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) [20250519_005444.]: Logging df_agg: CpG#6 [20250519_005444.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005444.]: c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)[20250519_005444.]: c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)[20250519_005444.]: c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) [20250519_005444.]: Entered 'hyperbolic_regression'-Function [20250519_005444.]: 'hyperbolic_regression': minmax = FALSE [20250519_005444.]: Entered 'cubic_regression'-Function [20250519_005444.]: 'cubic_regression': minmax = FALSE [20250519_005444.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) [20250519_005444.]: Logging df_agg: CpG#7 [20250519_005444.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005444.]: c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)[20250519_005444.]: c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)[20250519_005444.]: c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) [20250519_005444.]: Entered 'hyperbolic_regression'-Function [20250519_005444.]: 'hyperbolic_regression': minmax = FALSE [20250519_005444.]: Entered 'cubic_regression'-Function [20250519_005444.]: 'cubic_regression': minmax = FALSE [20250519_005444.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) [20250519_005444.]: Logging df_agg: CpG#8 [20250519_005444.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005444.]: c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)[20250519_005444.]: c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)[20250519_005444.]: c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) [20250519_005444.]: Entered 'hyperbolic_regression'-Function [20250519_005444.]: 'hyperbolic_regression': minmax = FALSE [20250519_005445.]: Entered 'cubic_regression'-Function [20250519_005445.]: 'cubic_regression': minmax = FALSE [20250519_005445.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) [20250519_005445.]: Logging df_agg: CpG#9 [20250519_005445.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005445.]: c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)[20250519_005445.]: c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)[20250519_005445.]: c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) [20250519_005445.]: Entered 'hyperbolic_regression'-Function [20250519_005445.]: 'hyperbolic_regression': minmax = FALSE [20250519_005445.]: Entered 'cubic_regression'-Function [20250519_005445.]: 'cubic_regression': minmax = FALSE [20250519_005445.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) [20250519_005445.]: Logging df_agg: row_means [20250519_005445.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005445.]: c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)[20250519_005445.]: c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)[20250519_005445.]: c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) [20250519_005445.]: Entered 'hyperbolic_regression'-Function [20250519_005445.]: 'hyperbolic_regression': minmax = FALSE [20250519_005445.]: Entered 'cubic_regression'-Function [20250519_005445.]: 'cubic_regression': minmax = FALSE [20250519_005445.]: ### Starting with plotting ### [20250519_005446.]: Creating BiasCorrected (cubic) plot No. 1 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG1_corrected_c.png [20250519_005446.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -1164.78037112998 , a = 2039.49073397227 , b = -1164.78037112998 , d = 1955.827713817 [20250519_005446.]: # CpG-site: CpG#1 Cubic: Using a = -3.42821196240366e-05 , b = 0.00462173155013798 , c = 0.850326258865826 , d = 0.500073874211125 [20250519_005447.]: Creating BiasCorrected (cubic) plot No. 2 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG2_corrected_c.png [20250519_005447.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = -464.14479041434 , a = 4988.85430873436 , b = -464.14479041434 , d = 4925.32709283236 [20250519_005447.]: # CpG-site: CpG#2 Cubic: Using a = -1.40811796906314e-05 , b = 0.00190849431754485 , c = 0.933455870941473 , d = 0.363096210099674 [20250519_005449.]: Creating BiasCorrected (cubic) plot No. 3 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG3_corrected_c.png [20250519_005449.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = -745.616753604673 , a = 2026.5285182947 , b = -745.616753604673 , d = 1953.27811759735 [20250519_005449.]: # CpG-site: CpG#3 Cubic: Using a = -2.78835051857186e-05 , b = 0.00366863707889089 , c = 0.880665171220893 , d = 0.510779308833653 [20250519_005451.]: Creating BiasCorrected (cubic) plot No. 4 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG4_corrected_c.png [20250519_005451.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = -833.077868829112 , a = 2871.13688289992 , b = -833.077868829112 , d = 2796.8115417971 [20250519_005451.]: # CpG-site: CpG#4 Cubic: Using a = -2.30028865643e-05 , b = 0.00309017851940692 , c = 0.897073107777645 , d = 0.443455761841866 [20250519_005453.]: Creating BiasCorrected (cubic) plot No. 5 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG5_corrected_c.png [20250519_005453.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = 222.093251203934 , a = 12477.4109611091 , b = 222.093251203934 , d = 12432.0779293841 [20250519_005453.]: # CpG-site: CpG#5 Cubic: Using a = -1.28632696989134e-05 , b = 0.00184822285299014 , c = 0.931001046631176 , d = 0.437963095185599 [20250519_005455.]: Creating BiasCorrected (cubic) plot No. 6 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG6_corrected_c.png [20250519_005455.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = -700.460688613084 , a = 21299.059407053 , b = -700.460688613084 , d = 21234.4652853188 [20250519_005455.]: # CpG-site: CpG#6 Cubic: Using a = -5.838335151375e-06 , b = 0.000828447163218526 , c = 0.970047764475166 , d = 0.158074701412251 [20250519_005457.]: Creating BiasCorrected (cubic) plot No. 7 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG7_corrected_c.png [20250519_005457.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -1144.91191228206 , a = 1477.91098401837 , b = -1144.91191228206 , d = 1394.69940178972 [20250519_005457.]: # CpG-site: CpG#7 Cubic: Using a = -4.54220535614047e-05 , b = 0.00607993130565131 , c = 0.805411493452389 , d = 0.615125382790254 [20250519_005458.]: Creating BiasCorrected (cubic) plot No. 8 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG8_corrected_c.png [20250519_005458.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = -349.37367288786 , a = 2377.38756952122 , b = -349.37367288786 , d = 2315.21494778767 [20250519_005458.]: # CpG-site: CpG#8 Cubic: Using a = -2.64622306669639e-05 , b = 0.00353621593528015 , c = 0.877916844709074 , d = 0.698684606493051 [20250519_005500.]: Creating BiasCorrected (cubic) plot No. 9 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG9_corrected_c.png [20250519_005500.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -1536.91699417279 , a = 3745.17619112231 , b = -1536.91699417279 , d = 3656.26801968932 [20250519_005500.]: # CpG-site: CpG#9 Cubic: Using a = -2.70976130611725e-05 , b = 0.00378478247182342 , c = 0.871825263051793 , d = 0.455475740974113 [20250519_005502.]: Creating BiasCorrected (cubic) plot No. 10 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_rowmeans_corrected_c.png [20250519_005502.]: # CpG-site: row_means Hyperbolic: Using bias_weight = -820.643805549713 , a = 3198.46619445775 , b = -820.643805549713 , d = 3124.21083989374 [20250519_005502.]: # CpG-site: row_means Cubic: Using a = -2.34328362100009e-05 , b = 0.00319139073176266 , c = 0.891842168032129 , d = 0.493517689787135 [20250519_005504.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG1_corrected_c.png [20250519_005506.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG2_corrected_c.png [20250519_005508.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG3_corrected_c.png [20250519_005510.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG4_corrected_c.png [20250519_005511.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG5_corrected_c.png [20250519_005513.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG6_corrected_c.png [20250519_005515.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG7_corrected_c.png [20250519_005517.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG8_corrected_c.png [20250519_005518.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG9_corrected_c.png [20250519_005520.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_rowmeans_corrected_c.png [20250519_005522.]: Entered 'solving_equations'-Function [20250519_005522.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 79.867338305743 [20250519_005522.]: Samplename: Sample#1 Root: 79.867 --> Root in between the borders! Added to results. Hyperbolic solved: 29.7900144307253 [20250519_005522.]: Samplename: Sample#10 Root: 29.79 --> Root in between the borders! Added to results. Hyperbolic solved: 41.6525328583179 [20250519_005522.]: Samplename: Sample#2 Root: 41.653 --> Root in between the borders! Added to results. Hyperbolic solved: 57.4651978180159 [20250519_005522.]: Samplename: Sample#3 Root: 57.465 --> Root in between the borders! Added to results. Hyperbolic solved: 9.20072297150156 [20250519_005522.]: Samplename: Sample#4 Root: 9.201 --> Root in between the borders! Added to results. Hyperbolic solved: 21.8059605790798 [20250519_005522.]: Samplename: Sample#5 Root: 21.806 --> Root in between the borders! Added to results. Hyperbolic solved: 23.0837964625959 [20250519_005522.]: Samplename: Sample#6 Root: 23.084 --> Root in between the borders! Added to results. Hyperbolic solved: 45.5034148455809 [20250519_005522.]: Samplename: Sample#7 Root: 45.503 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6987854360424 [20250519_005522.]: Samplename: Sample#8 Root: 85.699 --> Root in between the borders! Added to results. Hyperbolic solved: -3.66510576608508 [20250519_005522.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -3.665 --> '-10 < root < 0' --> substitute 0 [20250519_005522.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 76.7227930253137 [20250519_005522.]: Samplename: Sample#1 Root: 76.723 --> Root in between the borders! Added to results. Hyperbolic solved: 30.9924274267338 [20250519_005522.]: Samplename: Sample#10 Root: 30.992 --> Root in between the borders! Added to results. Hyperbolic solved: 42.6810881616502 [20250519_005522.]: Samplename: Sample#2 Root: 42.681 --> Root in between the borders! Added to results. Hyperbolic solved: 58.93379071467 [20250519_005522.]: Samplename: Sample#3 Root: 58.934 --> Root in between the borders! Added to results. Hyperbolic solved: 4.98020463922415 [20250519_005522.]: Samplename: Sample#4 Root: 4.98 --> Root in between the borders! Added to results. Hyperbolic solved: 20.2480263316954 [20250519_005522.]: Samplename: Sample#5 Root: 20.248 --> Root in between the borders! Added to results. Hyperbolic solved: 17.8133085422138 [20250519_005522.]: Samplename: Sample#6 Root: 17.813 --> Root in between the borders! Added to results. Hyperbolic solved: 40.917674420917 [20250519_005522.]: Samplename: Sample#7 Root: 40.918 --> Root in between the borders! Added to results. Hyperbolic solved: 86.045071534744 [20250519_005522.]: Samplename: Sample#8 Root: 86.045 --> Root in between the borders! Added to results. Hyperbolic solved: 3.1157977862424 [20250519_005522.]: Samplename: Sample#9 Root: 3.116 --> Root in between the borders! Added to results. [20250519_005522.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 74.9349278251272 [20250519_005522.]: Samplename: Sample#1 Root: 74.935 --> Root in between the borders! Added to results. Hyperbolic solved: 27.6844390885298 [20250519_005522.]: Samplename: Sample#10 Root: 27.684 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8520223561161 [20250519_005522.]: Samplename: Sample#2 Root: 41.852 --> Root in between the borders! Added to results. Hyperbolic solved: 55.8325219766746 [20250519_005522.]: Samplename: Sample#3 Root: 55.833 --> Root in between the borders! Added to results. Hyperbolic solved: 8.03518759540723 [20250519_005522.]: Samplename: Sample#4 Root: 8.035 --> Root in between the borders! Added to results. Hyperbolic solved: 24.1066316660805 [20250519_005522.]: Samplename: Sample#5 Root: 24.107 --> Root in between the borders! Added to results. Hyperbolic solved: 26.2419826081677 [20250519_005522.]: Samplename: Sample#6 Root: 26.242 --> Root in between the borders! Added to results. Hyperbolic solved: 44.0944957314077 [20250519_005522.]: Samplename: Sample#7 Root: 44.094 --> Root in between the borders! Added to results. Hyperbolic solved: 85.8279384942248 [20250519_005522.]: Samplename: Sample#8 Root: 85.828 --> Root in between the borders! Added to results. Hyperbolic solved: -0.66649088715512 [20250519_005522.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.666 --> '-10 < root < 0' --> substitute 0 [20250519_005522.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 76.349525731503 [20250519_005522.]: Samplename: Sample#1 Root: 76.35 --> Root in between the borders! Added to results. Hyperbolic solved: 28.2568543207918 [20250519_005522.]: Samplename: Sample#10 Root: 28.257 --> Root in between the borders! Added to results. Hyperbolic solved: 43.408980967851 [20250519_005522.]: Samplename: Sample#2 Root: 43.409 --> Root in between the borders! Added to results. Hyperbolic solved: 58.5435202418955 [20250519_005522.]: Samplename: Sample#3 Root: 58.544 --> Root in between the borders! Added to results. Hyperbolic solved: 10.308707716736 [20250519_005522.]: Samplename: Sample#4 Root: 10.309 --> Root in between the borders! Added to results. Hyperbolic solved: 22.1830453155629 [20250519_005522.]: Samplename: Sample#5 Root: 22.183 --> Root in between the borders! Added to results. Hyperbolic solved: 27.133776120676 [20250519_005522.]: Samplename: Sample#6 Root: 27.134 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8321067699793 [20250519_005522.]: Samplename: Sample#7 Root: 41.832 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6890182703099 [20250519_005522.]: Samplename: Sample#8 Root: 85.689 --> Root in between the borders! Added to results. Hyperbolic solved: 2.42232661674155 [20250519_005522.]: Samplename: Sample#9 Root: 2.422 --> Root in between the borders! Added to results. [20250519_005522.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 72.8126911247073 [20250519_005522.]: Samplename: Sample#1 Root: 72.813 --> Root in between the borders! Added to results. Hyperbolic solved: 26.4206935777278 [20250519_005522.]: Samplename: Sample#10 Root: 26.421 --> Root in between the borders! Added to results. Hyperbolic solved: 44.2738761535648 [20250519_005522.]: Samplename: Sample#2 Root: 44.274 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5324382038438 [20250519_005522.]: Samplename: Sample#3 Root: 53.532 --> Root in between the borders! Added to results. Hyperbolic solved: 10.0373723654377 [20250519_005522.]: Samplename: Sample#4 Root: 10.037 --> Root in between the borders! Added to results. Hyperbolic solved: 22.8733116049306 [20250519_005522.]: Samplename: Sample#5 Root: 22.873 --> Root in between the borders! Added to results. Hyperbolic solved: 25.9417915879453 [20250519_005522.]: Samplename: Sample#6 Root: 25.942 --> Root in between the borders! Added to results. Hyperbolic solved: 42.7945071576182 [20250519_005522.]: Samplename: Sample#7 Root: 42.795 --> Root in between the borders! Added to results. Hyperbolic solved: 88.8741214991654 [20250519_005522.]: Samplename: Sample#8 Root: 88.874 --> Root in between the borders! Added to results. Hyperbolic solved: 2.34647622155857 [20250519_005522.]: Samplename: Sample#9 Root: 2.346 --> Root in between the borders! Added to results. [20250519_005522.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 79.278059253184 [20250519_005522.]: Samplename: Sample#1 Root: 79.278 --> Root in between the borders! Added to results. Hyperbolic solved: 30.2012459144106 [20250519_005522.]: Samplename: Sample#10 Root: 30.201 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8474392901329 [20250519_005522.]: Samplename: Sample#2 Root: 41.847 --> Root in between the borders! Added to results. Hyperbolic solved: 56.8423515999057 [20250519_005522.]: Samplename: Sample#3 Root: 56.842 --> Root in between the borders! Added to results. Hyperbolic solved: 8.87856073331453 [20250519_005522.]: Samplename: Sample#4 Root: 8.879 --> Root in between the borders! Added to results. Hyperbolic solved: 18.6901596391315 [20250519_005522.]: Samplename: Sample#5 Root: 18.69 --> Root in between the borders! Added to results. Hyperbolic solved: 29.9309263719602 [20250519_005522.]: Samplename: Sample#6 Root: 29.931 --> Root in between the borders! Added to results. Hyperbolic solved: 42.814855924799 [20250519_005522.]: Samplename: Sample#7 Root: 42.815 --> Root in between the borders! Added to results. Hyperbolic solved: 86.75018307028 [20250519_005522.]: Samplename: Sample#8 Root: 86.75 --> Root in between the borders! Added to results. Hyperbolic solved: 1.51516233882239 [20250519_005522.]: Samplename: Sample#9 Root: 1.515 --> Root in between the borders! Added to results. [20250519_005522.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 78.2565541874121 [20250519_005522.]: Samplename: Sample#1 Root: 78.257 --> Root in between the borders! Added to results. Hyperbolic solved: 25.488740177574 [20250519_005522.]: Samplename: Sample#10 Root: 25.489 --> Root in between the borders! Added to results. Hyperbolic solved: 47.3712173704701 [20250519_005522.]: Samplename: Sample#2 Root: 47.371 --> Root in between the borders! Added to results. Hyperbolic solved: 58.3142577916519 [20250519_005522.]: Samplename: Sample#3 Root: 58.314 --> Root in between the borders! Added to results. Hyperbolic solved: 11.7212348600366 [20250519_005522.]: Samplename: Sample#4 Root: 11.721 --> Root in between the borders! Added to results. Hyperbolic solved: 25.3797494985646 [20250519_005522.]: Samplename: Sample#5 Root: 25.38 --> Root in between the borders! Added to results. Hyperbolic solved: 29.4095117400265 [20250519_005522.]: Samplename: Sample#6 Root: 29.41 --> Root in between the borders! Added to results. Hyperbolic solved: 44.5754992753069 [20250519_005522.]: Samplename: Sample#7 Root: 44.575 --> Root in between the borders! Added to results. Hyperbolic solved: 85.9628719316752 [20250519_005522.]: Samplename: Sample#8 Root: 85.963 --> Root in between the borders! Added to results. Hyperbolic solved: -4.16453560967523 [20250519_005522.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -4.165 --> '-10 < root < 0' --> substitute 0 [20250519_005522.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 72.9696611539955 [20250519_005522.]: Samplename: Sample#1 Root: 72.97 --> Root in between the borders! Added to results. Hyperbolic solved: 27.8250961443825 [20250519_005522.]: Samplename: Sample#10 Root: 27.825 --> Root in between the borders! Added to results. Hyperbolic solved: 34.9068396054724 [20250519_005522.]: Samplename: Sample#2 Root: 34.907 --> Root in between the borders! Added to results. Hyperbolic solved: 59.163744843639 [20250519_005522.]: Samplename: Sample#3 Root: 59.164 --> Root in between the borders! Added to results. Hyperbolic solved: 9.64644348381889 [20250519_005522.]: Samplename: Sample#4 Root: 9.646 --> Root in between the borders! Added to results. Hyperbolic solved: 19.3522608313684 [20250519_005522.]: Samplename: Sample#5 Root: 19.352 --> Root in between the borders! Added to results. Hyperbolic solved: 38.5571445759356 [20250519_005522.]: Samplename: Sample#6 Root: 38.557 --> Root in between the borders! Added to results. Hyperbolic solved: 44.7759635921689 [20250519_005522.]: Samplename: Sample#7 Root: 44.776 --> Root in between the borders! Added to results. Hyperbolic solved: 84.8599672002532 [20250519_005522.]: Samplename: Sample#8 Root: 84.86 --> Root in between the borders! Added to results. Hyperbolic solved: -2.05045066787228 [20250519_005522.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.05 --> '-10 < root < 0' --> substitute 0 [20250519_005522.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 81.0416669543132 [20250519_005522.]: Samplename: Sample#1 Root: 81.042 --> Root in between the borders! Added to results. Hyperbolic solved: 26.6441665578561 [20250519_005522.]: Samplename: Sample#10 Root: 26.644 --> Root in between the borders! Added to results. Hyperbolic solved: 45.7046185029368 [20250519_005522.]: Samplename: Sample#2 Root: 45.705 --> Root in between the borders! Added to results. Hyperbolic solved: 56.9767969678006 [20250519_005522.]: Samplename: Sample#3 Root: 56.977 --> Root in between the borders! Added to results. Hyperbolic solved: 6.81231488238752 [20250519_005522.]: Samplename: Sample#4 Root: 6.812 --> Root in between the borders! Added to results. Hyperbolic solved: 22.928478377057 [20250519_005522.]: Samplename: Sample#5 Root: 22.928 --> Root in between the borders! Added to results. Hyperbolic solved: 38.8653192337298 [20250519_005522.]: Samplename: Sample#6 Root: 38.865 --> Root in between the borders! Added to results. Hyperbolic solved: 43.6855618249121 [20250519_005522.]: Samplename: Sample#7 Root: 43.686 --> Root in between the borders! Added to results. Hyperbolic solved: 88.0155362134065 [20250519_005522.]: Samplename: Sample#8 Root: 88.016 --> Root in between the borders! Added to results. Hyperbolic solved: -3.35609615796437 [20250519_005522.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -3.356 --> '-10 < root < 0' --> substitute 0 [20250519_005522.]: Solving hyperbolic regression for row_means Hyperbolic solved: 77.0692788889392 [20250519_005522.]: Samplename: Sample#1 Root: 77.069 --> Root in between the borders! Added to results. Hyperbolic solved: 28.3620037732087 [20250519_005522.]: Samplename: Sample#10 Root: 28.362 --> Root in between the borders! Added to results. Hyperbolic solved: 42.5026157763218 [20250519_005522.]: Samplename: Sample#2 Root: 42.503 --> Root in between the borders! Added to results. Hyperbolic solved: 57.2972029249363 [20250519_005522.]: Samplename: Sample#3 Root: 57.297 --> Root in between the borders! Added to results. Hyperbolic solved: 8.82704268325745 [20250519_005522.]: Samplename: Sample#4 Root: 8.827 --> Root in between the borders! Added to results. Hyperbolic solved: 21.8102595595999 [20250519_005522.]: Samplename: Sample#5 Root: 21.81 --> Root in between the borders! Added to results. Hyperbolic solved: 28.7228654114699 [20250519_005522.]: Samplename: Sample#6 Root: 28.723 --> Root in between the borders! Added to results. Hyperbolic solved: 43.4105084715017 [20250519_005522.]: Samplename: Sample#7 Root: 43.411 --> Root in between the borders! Added to results. Hyperbolic solved: 86.4143551575745 [20250519_005522.]: Samplename: Sample#8 Root: 86.414 --> Root in between the borders! Added to results. Hyperbolic solved: -0.237016010782264 [20250519_005522.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.237 --> '-10 < root < 0' --> substitute 0 [20250519_005522.]: on_start: using future::plan("sequential") [20250519_005522.]: Entered 'clean_dt'-Function [20250519_005522.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_005522.]: got experimental data [20250519_005522.]: Entered 'clean_dt'-Function [20250519_005522.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_005522.]: got calibration data [20250519_005522.]: ### Starting with regression calculations ### [20250519_005522.]: Entered 'regression_type1'-Function [20250519_005522.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005522.]: Logging df_agg: CpG#1 [20250519_005522.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005522.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005522.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005522.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005522.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005522.]: Entered 'hyperbolic_regression'-Function [20250519_005522.]: 'hyperbolic_regression': minmax = FALSE [20250519_005522.]: Entered 'cubic_regression'-Function [20250519_005522.]: 'cubic_regression': minmax = FALSE [20250519_005523.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005523.]: Logging df_agg: CpG#2 [20250519_005523.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005523.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005523.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005523.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005523.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005523.]: Entered 'hyperbolic_regression'-Function [20250519_005523.]: 'hyperbolic_regression': minmax = FALSE [20250519_005523.]: Entered 'cubic_regression'-Function [20250519_005523.]: 'cubic_regression': minmax = FALSE [20250519_005523.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005523.]: Logging df_agg: CpG#3 [20250519_005523.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005523.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005523.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005523.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005523.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005523.]: Entered 'hyperbolic_regression'-Function [20250519_005523.]: 'hyperbolic_regression': minmax = FALSE [20250519_005523.]: Entered 'cubic_regression'-Function [20250519_005523.]: 'cubic_regression': minmax = FALSE [20250519_005523.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005523.]: Logging df_agg: CpG#4 [20250519_005523.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005523.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005523.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005523.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005523.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005523.]: Entered 'hyperbolic_regression'-Function [20250519_005523.]: 'hyperbolic_regression': minmax = FALSE [20250519_005523.]: Entered 'cubic_regression'-Function [20250519_005523.]: 'cubic_regression': minmax = FALSE [20250519_005523.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005523.]: Logging df_agg: CpG#5 [20250519_005523.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005523.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005523.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005523.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005523.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005523.]: Entered 'hyperbolic_regression'-Function [20250519_005523.]: 'hyperbolic_regression': minmax = FALSE [20250519_005523.]: Entered 'cubic_regression'-Function [20250519_005523.]: 'cubic_regression': minmax = FALSE [20250519_005523.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005523.]: Logging df_agg: CpG#6 [20250519_005523.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005523.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005523.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005523.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005523.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005523.]: Entered 'hyperbolic_regression'-Function [20250519_005523.]: 'hyperbolic_regression': minmax = FALSE [20250519_005523.]: Entered 'cubic_regression'-Function [20250519_005523.]: 'cubic_regression': minmax = FALSE [20250519_005523.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005523.]: Logging df_agg: CpG#7 [20250519_005523.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005523.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005523.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005523.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005523.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005523.]: Entered 'hyperbolic_regression'-Function [20250519_005523.]: 'hyperbolic_regression': minmax = FALSE [20250519_005523.]: Entered 'cubic_regression'-Function [20250519_005523.]: 'cubic_regression': minmax = FALSE [20250519_005523.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005523.]: Logging df_agg: CpG#8 [20250519_005523.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005523.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005523.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005523.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005523.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005523.]: Entered 'hyperbolic_regression'-Function [20250519_005523.]: 'hyperbolic_regression': minmax = FALSE [20250519_005523.]: Entered 'cubic_regression'-Function [20250519_005523.]: 'cubic_regression': minmax = FALSE [20250519_005523.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005523.]: Logging df_agg: CpG#9 [20250519_005523.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005523.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005523.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005523.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005523.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005523.]: Entered 'hyperbolic_regression'-Function [20250519_005523.]: 'hyperbolic_regression': minmax = FALSE [20250519_005523.]: Entered 'cubic_regression'-Function [20250519_005523.]: 'cubic_regression': minmax = FALSE [20250519_005523.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723122, 2.47760998435345)c(5.94911111111111, 0.84962962962963, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703704, 19.6995555555555, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005523.]: Logging df_agg: row_means [20250519_005523.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005523.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005523.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723122, 2.47760998435345)[20250519_005523.]: c(5.94911111111111, 0.84962962962963, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005523.]: c(NA, 6.79703703703704, 19.6995555555555, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005523.]: Entered 'hyperbolic_regression'-Function [20250519_005523.]: 'hyperbolic_regression': minmax = FALSE [20250519_005523.]: Entered 'cubic_regression'-Function [20250519_005523.]: 'cubic_regression': minmax = FALSE [20250519_005523.]: ### Starting with plotting ### [20250519_005524.]: Creating plot No. 1 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG1.png [20250519_005524.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -123249135.40483 , a = 49589952.0989949 , b = -123249135.40483 , d = 59280789.0121927 [20250519_005524.]: # CpG-site: CpG#1 Cubic: Using a = 6.53413423120091e-05 , b = -0.0055806968734969 , c = 0.784061853455188 , d = 1.93182659932656 [20250519_005526.]: Creating plot No. 2 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG2.png [20250519_005526.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = 167929575.171327 , a = 42299444.0962795 , b = 167929575.171327 , d = 47897274.2220611 [20250519_005526.]: # CpG-site: CpG#2 Cubic: Using a = 8.04237934904601e-06 , b = 0.00293158941798942 , c = 0.514821742825076 , d = 9.27667003367003 [20250519_005528.]: Creating plot No. 3 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG3.png [20250519_005528.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = 31004745.3756238 , a = 31050253.3779659 , b = 31004745.3756238 , d = 40665231.758814 [20250519_005528.]: # CpG-site: CpG#3 Cubic: Using a = 2.30555869809204e-05 , b = -0.000797009331409346 , c = 0.62783129228796 , d = 3.88705218855218 [20250519_005530.]: Creating plot No. 4 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG4.png [20250519_005530.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = 128022258.276155 , a = 42779497.5538086 , b = 128022258.276155 , d = 50999439.9227065 [20250519_005530.]: # CpG-site: CpG#4 Cubic: Using a = 3.2166356902357e-05 , b = -0.0015913142857143 , c = 0.697398364598366 , d = 6.17055050505048 [20250519_005532.]: Creating plot No. 5 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG5.png [20250519_005532.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = 29447513.6184781 , a = 41864399.8673718 , b = 29447513.6184781 , d = 55382346.8470806 [20250519_005532.]: # CpG-site: CpG#5 Cubic: Using a = -4.48459708193036e-06 , b = 0.00375371236171235 , c = 0.422446384479718 , d = 5.17203872053872 [20250519_005534.]: Creating plot No. 6 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG6.png [20250519_005534.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = 17150080.5960938 , a = 40932834.1078748 , b = 17150080.5960938 , d = 46723471.6686748 [20250519_005534.]: # CpG-site: CpG#6 Cubic: Using a = 2.38852884399552e-05 , b = 0.000918637037037021 , c = 0.561022132435467 , d = 6.1479276094276 [20250519_005537.]: Creating plot No. 7 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG7.png [20250519_005537.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -57204506.2862076 , a = 29554652.6579632 , b = -57204506.2862076 , d = 48548425.6483363 [20250519_005537.]: # CpG-site: CpG#7 Cubic: Using a = 4.98010505050505e-05 , b = -0.00436152150072151 , c = 0.579588917748918 , d = 1.72104545454544 [20250519_005539.]: Creating plot No. 8 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG8.png [20250519_005539.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = 263871870.982016 , a = 38159683.1948542 , b = 263871870.982016 , d = 45284547.6704654 [20250519_005539.]: # CpG-site: CpG#8 Cubic: Using a = -1.50060965207632e-05 , b = 0.00447978143338143 , c = 0.534897737694404 , d = 9.56981481481482 [20250519_005541.]: Creating plot No. 9 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG9.png [20250519_005541.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -224404013.660658 , a = 51672526.9851193 , b = -224404013.660658 , d = 60201205.3472543 [20250519_005541.]: # CpG-site: CpG#9 Cubic: Using a = 7.72300426487093e-05 , b = -0.00542281173641174 , c = 0.67899229597563 , d = 2.72413468013467 [20250519_005543.]: Creating plot No. 10 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_rowmeans.png [20250519_005543.]: # CpG-site: row_means Hyperbolic: Using bias_weight = 40333655.1288316 , a = 44937623.5310126 , b = 40333655.1288316 , d = 55678831.3245349 [20250519_005543.]: # CpG-site: row_means Cubic: Using a = 2.88923726150392e-05 , b = -0.0006299592752926 , c = 0.600117857944524 , d = 5.17789562289563 [20250519_005545.]: Entered 'solving_equations'-Function [20250519_005545.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 6.03337011705089 [20250519_005545.]: Samplename: 0 Root: 6.033 --> Root in between the borders! Added to results. Hyperbolic solved: 14.7663097441095 [20250519_005545.]: Samplename: 12.5 Root: 14.766 --> Root in between the borders! Added to results. Hyperbolic solved: 23.1900365713551 [20250519_005545.]: Samplename: 25 Root: 23.19 --> Root in between the borders! Added to results. Hyperbolic solved: 33.8197153352119 [20250519_005545.]: Samplename: 37.5 Root: 33.82 --> Root in between the borders! Added to results. Hyperbolic solved: 46.5156833662696 [20250519_005545.]: Samplename: 50 Root: 46.516 --> Root in between the borders! Added to results. Hyperbolic solved: 60.2074431300908 [20250519_005545.]: Samplename: 62.5 Root: 60.207 --> Root in between the borders! Added to results. Hyperbolic solved: 71.6105733239506 [20250519_005545.]: Samplename: 75 Root: 71.611 --> Root in between the borders! Added to results. Hyperbolic solved: 80.2235915426204 [20250519_005545.]: Samplename: 87.5 Root: 80.224 --> Root in between the borders! Added to results. Hyperbolic solved: 113.633279788745 [20250519_005545.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20250519_005545.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 6.85513576175294 [20250519_005545.]: Samplename: 0 Root: 6.855 --> Root in between the borders! Added to results. Hyperbolic solved: 13.7133339963707 [20250519_005545.]: Samplename: 12.5 Root: 13.713 --> Root in between the borders! Added to results. Hyperbolic solved: 24.5830349243156 [20250519_005545.]: Samplename: 25 Root: 24.583 --> Root in between the borders! Added to results. Hyperbolic solved: 31.8277435330554 [20250519_005545.]: Samplename: 37.5 Root: 31.828 --> Root in between the borders! Added to results. Hyperbolic solved: 43.0277185945539 [20250519_005545.]: Samplename: 50 Root: 43.028 --> Root in between the borders! Added to results. Hyperbolic solved: 63.1097818281222 [20250519_005545.]: Samplename: 62.5 Root: 63.11 --> Root in between the borders! Added to results. Hyperbolic solved: 73.3036858899805 [20250519_005545.]: Samplename: 75 Root: 73.304 --> Root in between the borders! Added to results. Hyperbolic solved: 84.4808447799272 [20250519_005545.]: Samplename: 87.5 Root: 84.481 --> Root in between the borders! Added to results. Hyperbolic solved: 109.0987325611 [20250519_005545.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 109.099 --> '100 < root < 110' --> substitute 100 [20250519_005545.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 5.27473119209009 [20250519_005545.]: Samplename: 0 Root: 5.275 --> Root in between the borders! Added to results. Hyperbolic solved: 12.7004989900866 [20250519_005545.]: Samplename: 12.5 Root: 12.7 --> Root in between the borders! Added to results. Hyperbolic solved: 24.2988465005425 [20250519_005545.]: Samplename: 25 Root: 24.299 --> Root in between the borders! Added to results. Hyperbolic solved: 33.7074476249853 [20250519_005545.]: Samplename: 37.5 Root: 33.707 --> Root in between the borders! Added to results. Hyperbolic solved: 47.0168800224423 [20250519_005545.]: Samplename: 50 Root: 47.017 --> Root in between the borders! Added to results. Hyperbolic solved: 61.5718102366584 [20250519_005545.]: Samplename: 62.5 Root: 61.572 --> Root in between the borders! Added to results. Hyperbolic solved: 76.091389954119 [20250519_005545.]: Samplename: 75 Root: 76.091 --> Root in between the borders! Added to results. Hyperbolic solved: 79.6023799465703 [20250519_005545.]: Samplename: 87.5 Root: 79.602 --> Root in between the borders! Added to results. Hyperbolic solved: 109.736019452694 [20250519_005545.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 109.736 --> '100 < root < 110' --> substitute 100 [20250519_005545.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 5.07584235183922 [20250519_005545.]: Samplename: 0 Root: 5.076 --> Root in between the borders! Added to results. Hyperbolic solved: 14.2021272601476 [20250519_005545.]: Samplename: 12.5 Root: 14.202 --> Root in between the borders! Added to results. Hyperbolic solved: 23.203637284743 [20250519_005545.]: Samplename: 25 Root: 23.204 --> Root in between the borders! Added to results. Hyperbolic solved: 34.7960863609446 [20250519_005545.]: Samplename: 37.5 Root: 34.796 --> Root in between the borders! Added to results. Hyperbolic solved: 44.4638162018573 [20250519_005545.]: Samplename: 50 Root: 44.464 --> Root in between the borders! Added to results. Hyperbolic solved: 64.2755507391393 [20250519_005545.]: Samplename: 62.5 Root: 64.276 --> Root in between the borders! Added to results. Hyperbolic solved: 71.7670214785718 [20250519_005545.]: Samplename: 75 Root: 71.767 --> Root in between the borders! Added to results. Hyperbolic solved: 82.5599568822802 [20250519_005545.]: Samplename: 87.5 Root: 82.56 --> Root in between the borders! Added to results. Hyperbolic solved: 109.655967198106 [20250519_005545.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 109.656 --> '100 < root < 110' --> substitute 100 [20250519_005546.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 6.96411772281142 [20250519_005546.]: Samplename: 0 Root: 6.964 --> Root in between the borders! Added to results. Hyperbolic solved: 12.4462106087166 [20250519_005546.]: Samplename: 12.5 Root: 12.446 --> Root in between the borders! Added to results. Hyperbolic solved: 22.9738427992134 [20250519_005546.]: Samplename: 25 Root: 22.974 --> Root in between the borders! Added to results. Hyperbolic solved: 34.1311793989123 [20250519_005546.]: Samplename: 37.5 Root: 34.131 --> Root in between the borders! Added to results. Hyperbolic solved: 46.7456955019114 [20250519_005546.]: Samplename: 50 Root: 46.746 --> Root in between the borders! Added to results. Hyperbolic solved: 58.6021949543013 [20250519_005546.]: Samplename: 62.5 Root: 58.602 --> Root in between the borders! Added to results. Hyperbolic solved: 74.5100864344263 [20250519_005546.]: Samplename: 75 Root: 74.51 --> Root in between the borders! Added to results. Hyperbolic solved: 86.8549692943716 [20250519_005546.]: Samplename: 87.5 Root: 86.855 --> Root in between the borders! Added to results. Hyperbolic solved: 106.771715286513 [20250519_005546.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 106.772 --> '100 < root < 110' --> substitute 100 [20250519_005546.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 6.82248549455073 [20250519_005546.]: Samplename: 0 Root: 6.822 --> Root in between the borders! Added to results. Hyperbolic solved: 14.2907258003629 [20250519_005546.]: Samplename: 12.5 Root: 14.291 --> Root in between the borders! Added to results. Hyperbolic solved: 24.7343949718638 [20250519_005546.]: Samplename: 25 Root: 24.734 --> Root in between the borders! Added to results. Hyperbolic solved: 31.7110486196861 [20250519_005546.]: Samplename: 37.5 Root: 31.711 --> Root in between the borders! Added to results. Hyperbolic solved: 44.7745870553137 [20250519_005546.]: Samplename: 50 Root: 44.775 --> Root in between the borders! Added to results. Hyperbolic solved: 60.1336283719223 [20250519_005546.]: Samplename: 62.5 Root: 60.134 --> Root in between the borders! Added to results. Hyperbolic solved: 70.3292384995337 [20250519_005546.]: Samplename: 75 Root: 70.329 --> Root in between the borders! Added to results. Hyperbolic solved: 88.2655480529845 [20250519_005546.]: Samplename: 87.5 Root: 88.266 --> Root in between the borders! Added to results. Hyperbolic solved: 108.938360440145 [20250519_005546.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 108.938 --> '100 < root < 110' --> substitute 100 [20250519_005546.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 6.75841824784972 [20250519_005546.]: Samplename: 0 Root: 6.758 --> Root in between the borders! Added to results. Hyperbolic solved: 13.6258602138932 [20250519_005546.]: Samplename: 12.5 Root: 13.626 --> Root in between the borders! Added to results. Hyperbolic solved: 23.2672219789518 [20250519_005546.]: Samplename: 25 Root: 23.267 --> Root in between the borders! Added to results. Hyperbolic solved: 33.4287658578254 [20250519_005546.]: Samplename: 37.5 Root: 33.429 --> Root in between the borders! Added to results. Hyperbolic solved: 47.5236851621158 [20250519_005546.]: Samplename: 50 Root: 47.524 --> Root in between the borders! Added to results. Hyperbolic solved: 60.5558050598499 [20250519_005546.]: Samplename: 62.5 Root: 60.556 --> Root in between the borders! Added to results. Hyperbolic solved: 72.3896034879297 [20250519_005546.]: Samplename: 75 Root: 72.39 --> Root in between the borders! Added to results. Hyperbolic solved: 77.2628610417933 [20250519_005546.]: Samplename: 87.5 Root: 77.263 --> Root in between the borders! Added to results. Hyperbolic solved: 115.187789384315 [20250519_005546.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20250519_005546.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 5.74253131510683 [20250519_005546.]: Samplename: 0 Root: 5.743 --> Root in between the borders! Added to results. Hyperbolic solved: 10.9055216254216 [20250519_005546.]: Samplename: 12.5 Root: 10.906 --> Root in between the borders! Added to results. Hyperbolic solved: 24.5329419436721 [20250519_005546.]: Samplename: 25 Root: 24.533 --> Root in between the borders! Added to results. Hyperbolic solved: 32.1516426249373 [20250519_005546.]: Samplename: 37.5 Root: 32.152 --> Root in between the borders! Added to results. Hyperbolic solved: 49.0919870496865 [20250519_005546.]: Samplename: 50 Root: 49.092 --> Root in between the borders! Added to results. Hyperbolic solved: 62.3843810658735 [20250519_005546.]: Samplename: 62.5 Root: 62.384 --> Root in between the borders! Added to results. Hyperbolic solved: 77.6235905386846 [20250519_005546.]: Samplename: 75 Root: 77.624 --> Root in between the borders! Added to results. Hyperbolic solved: 80.9968312605936 [20250519_005546.]: Samplename: 87.5 Root: 80.997 --> Root in between the borders! Added to results. Hyperbolic solved: 106.570581137258 [20250519_005546.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 106.571 --> '100 < root < 110' --> substitute 100 [20250519_005546.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 8.66981641228949 [20250519_005546.]: Samplename: 0 Root: 8.67 --> Root in between the borders! Added to results. Hyperbolic solved: 14.9680920996829 [20250519_005546.]: Samplename: 12.5 Root: 14.968 --> Root in between the borders! Added to results. Hyperbolic solved: 24.6496838227806 [20250519_005546.]: Samplename: 25 Root: 24.65 --> Root in between the borders! Added to results. Hyperbolic solved: 31.5444702961411 [20250519_005546.]: Samplename: 37.5 Root: 31.544 --> Root in between the borders! Added to results. Hyperbolic solved: 44.0944303975514 [20250519_005546.]: Samplename: 50 Root: 44.094 --> Root in between the borders! Added to results. Hyperbolic solved: 57.2478940106457 [20250519_005546.]: Samplename: 62.5 Root: 57.248 --> Root in between the borders! Added to results. Hyperbolic solved: 69.1518417879612 [20250519_005546.]: Samplename: 75 Root: 69.152 --> Root in between the borders! Added to results. Hyperbolic solved: 83.8189177560508 [20250519_005546.]: Samplename: 87.5 Root: 83.819 --> Root in between the borders! Added to results. Hyperbolic solved: 115.854857900091 [20250519_005546.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20250519_005546.]: Solving hyperbolic regression for row_means Hyperbolic solved: 6.47354965864506 [20250519_005546.]: Samplename: 0 Root: 6.474 --> Root in between the borders! Added to results. Hyperbolic solved: 13.5375540918194 [20250519_005546.]: Samplename: 12.5 Root: 13.538 --> Root in between the borders! Added to results. Hyperbolic solved: 23.976024251167 [20250519_005546.]: Samplename: 25 Root: 23.976 --> Root in between the borders! Added to results. Hyperbolic solved: 32.9614416466039 [20250519_005546.]: Samplename: 37.5 Root: 32.961 --> Root in between the borders! Added to results. Hyperbolic solved: 45.8185453601259 [20250519_005546.]: Samplename: 50 Root: 45.819 --> Root in between the borders! Added to results. Hyperbolic solved: 60.9295837234236 [20250519_005546.]: Samplename: 62.5 Root: 60.93 --> Root in between the borders! Added to results. Hyperbolic solved: 72.9250251350637 [20250519_005546.]: Samplename: 75 Root: 72.925 --> Root in between the borders! Added to results. Hyperbolic solved: 82.8721280175692 [20250519_005546.]: Samplename: 87.5 Root: 82.872 --> Root in between the borders! Added to results. Hyperbolic solved: 110.506149316569 [20250519_005546.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20250519_005546.]: ### Starting with regression calculations ### [20250519_005546.]: Entered 'regression_type1'-Function [20250519_005546.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)c(6.03337011705089, 14.7663097441095, 23.1900365713551, 33.8197153352119, 46.5156833662696, 60.2074431300908, 71.6105733239506, 80.2235915426204)c(6.03337011705089, 2.2663097441095, 1.8099634286449, 3.6802846647881, 3.4843166337304, 2.2925568699092, 3.3894266760494, 7.2764084573796)c(NA, 18.130477952876, 7.2398537145796, 9.81409243943492, 6.9686332674608, 3.66809099185472, 4.51923556806586, 8.3158953798624) [20250519_005546.]: Logging df_agg: CpG#1 [20250519_005546.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)[20250519_005546.]: c(6.03337011705089, 14.7663097441095, 23.1900365713551, 33.8197153352119, 46.5156833662696, 60.2074431300908, 71.6105733239506, 80.2235915426204)[20250519_005546.]: c(6.03337011705089, 2.2663097441095, 1.8099634286449, 3.6802846647881, 3.4843166337304, 2.2925568699092, 3.3894266760494, 7.2764084573796)[20250519_005546.]: c(NA, 18.130477952876, 7.2398537145796, 9.81409243943492, 6.9686332674608, 3.66809099185472, 4.51923556806586, 8.3158953798624) [20250519_005546.]: Entered 'hyperbolic_regression'-Function [20250519_005546.]: 'hyperbolic_regression': minmax = FALSE [20250519_005546.]: Entered 'cubic_regression'-Function [20250519_005546.]: 'cubic_regression': minmax = FALSE [20250519_005546.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.85513576175294, 13.7133339963707, 24.5830349243156, 31.8277435330554, 43.0277185945539, 63.1097818281222, 73.3036858899805, 84.4808447799272, 100)c(6.85513576175294, 1.2133339963707, 0.416965075684399, 5.6722564669446, 6.9722814054461, 0.609781828122202, 1.6963141100195, 3.0191552200728, 0)c(NA, 9.7066719709656, 1.66786030273759, 15.1260172451856, 13.9445628108922, 0.975650924995523, 2.26175214669267, 3.45046310865463, 0) [20250519_005546.]: Logging df_agg: CpG#2 [20250519_005546.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005546.]: c(6.85513576175294, 13.7133339963707, 24.5830349243156, 31.8277435330554, 43.0277185945539, 63.1097818281222, 73.3036858899805, 84.4808447799272, 100)[20250519_005546.]: c(6.85513576175294, 1.2133339963707, 0.416965075684399, 5.6722564669446, 6.9722814054461, 0.609781828122202, 1.6963141100195, 3.0191552200728, 0)[20250519_005546.]: c(NA, 9.7066719709656, 1.66786030273759, 15.1260172451856, 13.9445628108922, 0.975650924995523, 2.26175214669267, 3.45046310865463, 0) [20250519_005546.]: Entered 'hyperbolic_regression'-Function [20250519_005546.]: 'hyperbolic_regression': minmax = FALSE [20250519_005546.]: Entered 'cubic_regression'-Function [20250519_005546.]: 'cubic_regression': minmax = FALSE [20250519_005546.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.27473119209009, 12.7004989900866, 24.2988465005425, 33.7074476249853, 47.0168800224423, 61.5718102366584, 76.091389954119, 79.6023799465703, 100)c(5.27473119209009, 0.200498990086601, 0.701153499457501, 3.7925523750147, 2.9831199775577, 0.928189763341599, 1.09138995411899, 7.8976200534297, 0)c(NA, 1.60399192069281, 2.80461399783, 10.1134730000392, 5.9662399551154, 1.48510362134656, 1.45518660549199, 9.02585148963395, 0) [20250519_005546.]: Logging df_agg: CpG#3 [20250519_005546.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005546.]: c(5.27473119209009, 12.7004989900866, 24.2988465005425, 33.7074476249853, 47.0168800224423, 61.5718102366584, 76.091389954119, 79.6023799465703, 100)[20250519_005546.]: c(5.27473119209009, 0.200498990086601, 0.701153499457501, 3.7925523750147, 2.9831199775577, 0.928189763341599, 1.09138995411899, 7.8976200534297, 0)[20250519_005546.]: c(NA, 1.60399192069281, 2.80461399783, 10.1134730000392, 5.9662399551154, 1.48510362134656, 1.45518660549199, 9.02585148963395, 0) [20250519_005546.]: Entered 'hyperbolic_regression'-Function [20250519_005546.]: 'hyperbolic_regression': minmax = FALSE [20250519_005546.]: Entered 'cubic_regression'-Function [20250519_005546.]: 'cubic_regression': minmax = FALSE [20250519_005546.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.07584235183922, 14.2021272601476, 23.203637284743, 34.7960863609446, 44.4638162018573, 64.2755507391393, 71.7670214785718, 82.5599568822802, 100)c(5.07584235183922, 1.7021272601476, 1.796362715257, 2.7039136390554, 5.5361837981427, 1.77555073913931, 3.23297852142819, 4.9400431177198, 0)c(NA, 13.6170180811808, 7.185450861028, 7.2104363708144, 11.0723675962854, 2.84088118262289, 4.31063802857093, 5.64576356310834, 0) [20250519_005546.]: Logging df_agg: CpG#4 [20250519_005546.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005546.]: c(5.07584235183922, 14.2021272601476, 23.203637284743, 34.7960863609446, 44.4638162018573, 64.2755507391393, 71.7670214785718, 82.5599568822802, 100)[20250519_005546.]: c(5.07584235183922, 1.7021272601476, 1.796362715257, 2.7039136390554, 5.5361837981427, 1.77555073913931, 3.23297852142819, 4.9400431177198, 0)[20250519_005546.]: c(NA, 13.6170180811808, 7.185450861028, 7.2104363708144, 11.0723675962854, 2.84088118262289, 4.31063802857093, 5.64576356310834, 0) [20250519_005546.]: Entered 'hyperbolic_regression'-Function [20250519_005546.]: 'hyperbolic_regression': minmax = FALSE [20250519_005546.]: Entered 'cubic_regression'-Function [20250519_005546.]: 'cubic_regression': minmax = FALSE [20250519_005546.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.96411772281142, 12.4462106087166, 22.9738427992134, 34.1311793989123, 46.7456955019114, 58.6021949543013, 74.5100864344263, 86.8549692943716, 100)c(6.96411772281142, 0.0537893912834004, 2.0261572007866, 3.3688206010877, 3.2543044980886, 3.8978050456987, 0.489913565573701, 0.6450307056284, 0)c(NA, 0.430315130267203, 8.1046288031464, 8.98352160290053, 6.5086089961772, 6.23648807311793, 0.653218087431602, 0.7371779492896, 0) [20250519_005546.]: Logging df_agg: CpG#5 [20250519_005546.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005546.]: c(6.96411772281142, 12.4462106087166, 22.9738427992134, 34.1311793989123, 46.7456955019114, 58.6021949543013, 74.5100864344263, 86.8549692943716, 100)[20250519_005546.]: c(6.96411772281142, 0.0537893912834004, 2.0261572007866, 3.3688206010877, 3.2543044980886, 3.8978050456987, 0.489913565573701, 0.6450307056284, 0)[20250519_005546.]: c(NA, 0.430315130267203, 8.1046288031464, 8.98352160290053, 6.5086089961772, 6.23648807311793, 0.653218087431602, 0.7371779492896, 0) [20250519_005546.]: Entered 'hyperbolic_regression'-Function [20250519_005546.]: 'hyperbolic_regression': minmax = FALSE [20250519_005546.]: Entered 'cubic_regression'-Function [20250519_005546.]: 'cubic_regression': minmax = FALSE [20250519_005546.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.82248549455073, 14.2907258003629, 24.7343949718638, 31.7110486196861, 44.7745870553137, 60.1336283719223, 70.3292384995337, 88.2655480529845, 100)c(6.82248549455073, 1.7907258003629, 0.2656050281362, 5.7889513803139, 5.2254129446863, 2.3663716280777, 4.6707615004663, 0.765548052984499, 0)c(NA, 14.3258064029032, 1.0624201125448, 15.4372036808371, 10.4508258893726, 3.78619460492432, 6.22768200062173, 0.874912060553714, 0) [20250519_005546.]: Logging df_agg: CpG#6 [20250519_005546.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005546.]: c(6.82248549455073, 14.2907258003629, 24.7343949718638, 31.7110486196861, 44.7745870553137, 60.1336283719223, 70.3292384995337, 88.2655480529845, 100)[20250519_005546.]: c(6.82248549455073, 1.7907258003629, 0.2656050281362, 5.7889513803139, 5.2254129446863, 2.3663716280777, 4.6707615004663, 0.765548052984499, 0)[20250519_005546.]: c(NA, 14.3258064029032, 1.0624201125448, 15.4372036808371, 10.4508258893726, 3.78619460492432, 6.22768200062173, 0.874912060553714, 0) [20250519_005546.]: Entered 'hyperbolic_regression'-Function [20250519_005546.]: 'hyperbolic_regression': minmax = FALSE [20250519_005546.]: Entered 'cubic_regression'-Function [20250519_005546.]: 'cubic_regression': minmax = FALSE [20250519_005546.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)c(6.75841824784972, 13.6258602138932, 23.2672219789518, 33.4287658578254, 47.5236851621158, 60.5558050598499, 72.3896034879297, 77.2628610417933)c(6.75841824784972, 1.1258602138932, 1.7327780210482, 4.0712341421746, 2.4763148378842, 1.9441949401501, 2.6103965120703, 10.2371389582067)c(NA, 9.0068817111456, 6.9311120841928, 10.8566243791323, 4.95262967576841, 3.11071190424016, 3.48052868276039, 11.6995873808077) [20250519_005546.]: Logging df_agg: CpG#7 [20250519_005546.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)[20250519_005546.]: c(6.75841824784972, 13.6258602138932, 23.2672219789518, 33.4287658578254, 47.5236851621158, 60.5558050598499, 72.3896034879297, 77.2628610417933)[20250519_005546.]: c(6.75841824784972, 1.1258602138932, 1.7327780210482, 4.0712341421746, 2.4763148378842, 1.9441949401501, 2.6103965120703, 10.2371389582067)[20250519_005546.]: c(NA, 9.0068817111456, 6.9311120841928, 10.8566243791323, 4.95262967576841, 3.11071190424016, 3.48052868276039, 11.6995873808077) [20250519_005546.]: Entered 'hyperbolic_regression'-Function [20250519_005546.]: 'hyperbolic_regression': minmax = FALSE [20250519_005546.]: Entered 'cubic_regression'-Function [20250519_005546.]: 'cubic_regression': minmax = FALSE [20250519_005546.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.74253131510683, 10.9055216254216, 24.5329419436721, 32.1516426249373, 49.0919870496865, 62.3843810658735, 77.6235905386846, 80.9968312605936, 100)c(5.74253131510683, 1.5944783745784, 0.4670580563279, 5.3483573750627, 0.908012950313498, 0.115618934126502, 2.6235905386846, 6.50316873940641, 0)c(NA, 12.7558269966272, 1.8682322253116, 14.2622863335005, 1.816025900627, 0.184990294602403, 3.49812071824613, 7.43219284503589, 0) [20250519_005546.]: Logging df_agg: CpG#8 [20250519_005546.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005546.]: c(5.74253131510683, 10.9055216254216, 24.5329419436721, 32.1516426249373, 49.0919870496865, 62.3843810658735, 77.6235905386846, 80.9968312605936, 100)[20250519_005546.]: c(5.74253131510683, 1.5944783745784, 0.4670580563279, 5.3483573750627, 0.908012950313498, 0.115618934126502, 2.6235905386846, 6.50316873940641, 0)[20250519_005546.]: c(NA, 12.7558269966272, 1.8682322253116, 14.2622863335005, 1.816025900627, 0.184990294602403, 3.49812071824613, 7.43219284503589, 0) [20250519_005546.]: Entered 'hyperbolic_regression'-Function [20250519_005546.]: 'hyperbolic_regression': minmax = FALSE [20250519_005546.]: Entered 'cubic_regression'-Function [20250519_005546.]: 'cubic_regression': minmax = FALSE [20250519_005546.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)c(8.66981641228949, 14.9680920996829, 24.6496838227806, 31.5444702961411, 44.0944303975514, 57.2478940106457, 69.1518417879612, 83.8189177560508)c(8.66981641228949, 2.4680920996829, 0.350316177219401, 5.9555297038589, 5.9055696024486, 5.2521059893543, 5.8481582120388, 3.68108224394921)c(NA, 19.7447367974632, 1.4012647088776, 15.8814125436237, 11.8111392048972, 8.40336958296688, 7.7975442827184, 4.20695113594195) [20250519_005546.]: Logging df_agg: CpG#9 [20250519_005546.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)[20250519_005546.]: c(8.66981641228949, 14.9680920996829, 24.6496838227806, 31.5444702961411, 44.0944303975514, 57.2478940106457, 69.1518417879612, 83.8189177560508)[20250519_005546.]: c(8.66981641228949, 2.4680920996829, 0.350316177219401, 5.9555297038589, 5.9055696024486, 5.2521059893543, 5.8481582120388, 3.68108224394921)[20250519_005546.]: c(NA, 19.7447367974632, 1.4012647088776, 15.8814125436237, 11.8111392048972, 8.40336958296688, 7.7975442827184, 4.20695113594195) [20250519_005546.]: Entered 'hyperbolic_regression'-Function [20250519_005546.]: 'hyperbolic_regression': minmax = FALSE [20250519_005546.]: Entered 'cubic_regression'-Function [20250519_005546.]: 'cubic_regression': minmax = FALSE [20250519_005546.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)c(6.47354965864506, 13.5375540918194, 23.976024251167, 32.9614416466039, 45.8185453601259, 60.9295837234236, 72.9250251350637, 82.8721280175692)c(6.47354965864506, 1.0375540918194, 1.023975748833, 4.5385583533961, 4.1814546398741, 1.5704162765764, 2.07497486493629, 4.6278719824308)c(NA, 8.3004327345552, 4.095902995332, 12.1028222757229, 8.36290927974819, 2.51266604252224, 2.76663315324839, 5.28899655134949) [20250519_005546.]: Logging df_agg: row_means [20250519_005546.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)[20250519_005546.]: c(6.47354965864506, 13.5375540918194, 23.976024251167, 32.9614416466039, 45.8185453601259, 60.9295837234236, 72.9250251350637, 82.8721280175692)[20250519_005546.]: c(6.47354965864506, 1.0375540918194, 1.023975748833, 4.5385583533961, 4.1814546398741, 1.5704162765764, 2.07497486493629, 4.6278719824308)[20250519_005546.]: c(NA, 8.3004327345552, 4.095902995332, 12.1028222757229, 8.36290927974819, 2.51266604252224, 2.76663315324839, 5.28899655134949) [20250519_005546.]: Entered 'hyperbolic_regression'-Function [20250519_005546.]: 'hyperbolic_regression': minmax = FALSE [20250519_005546.]: Entered 'cubic_regression'-Function [20250519_005546.]: 'cubic_regression': minmax = FALSE [20250519_005547.]: ### Starting with plotting ### [20250519_005547.]: Creating BiasCorrected (hyperbolic) plot No. 1 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG1_corrected_h.png [20250519_005547.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = 170413234.729972 , a = 44157086.707734 , b = 170413234.729972 , d = 49999790.9017601 [20250519_005547.]: # CpG-site: CpG#1 Cubic: Using a = -8.02902858014277e-05 , b = 0.0119973831380665 , c = 0.410459267079284 , d = 6.65096545514267 [20250519_005549.]: Creating BiasCorrected (hyperbolic) plot No. 2 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG2_corrected_h.png [20250519_005549.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = 63519198.3583519 , a = 42702123.5062977 , b = 63519198.3583519 , d = 44879882.4102916 [20250519_005549.]: # CpG-site: CpG#2 Cubic: Using a = -4.57917988885898e-05 , b = 0.00978975326683099 , c = 0.400365566888902 , d = 7.17765330717532 [20250519_005550.]: Creating BiasCorrected (hyperbolic) plot No. 3 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG3_corrected_h.png [20250519_005550.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = 60249784.2121432 , a = 37717764.1245422 , b = 60249784.2121432 , d = 39783429.2823954 [20250519_005550.]: # CpG-site: CpG#3 Cubic: Using a = -2.8548625146914e-05 , b = 0.00587956303749884 , c = 0.626869802626483 , d = 4.78057876315863 [20250519_005552.]: Creating BiasCorrected (hyperbolic) plot No. 4 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG4_corrected_h.png [20250519_005552.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = 66787089.0359508 , a = 42175722.8129271 , b = 66787089.0359508 , d = 44465523.7860812 [20250519_005552.]: # CpG-site: CpG#4 Cubic: Using a = -1.99135996570308e-05 , b = 0.00496936895290719 , c = 0.637632604471187 , d = 5.04633670060351 [20250519_005554.]: Creating BiasCorrected (hyperbolic) plot No. 5 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG5_corrected_h.png [20250519_005554.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = 57501729.1236441 , a = 52618093.853152 , b = 57501729.1236441 , d = 54589535.3828564 [20250519_005554.]: # CpG-site: CpG#5 Cubic: Using a = -4.67907948499236e-05 , b = 0.00978122250124343 , c = 0.42296364991001 , d = 6.61748738798833 [20250519_005556.]: Creating BiasCorrected (hyperbolic) plot No. 6 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG6_corrected_h.png [20250519_005556.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = 62067256.5784193 , a = 42512767.1502548 , b = 62067256.5784193 , d = 44640742.1604705 [20250519_005556.]: # CpG-site: CpG#6 Cubic: Using a = -2.66665530276184e-05 , b = 0.00740474965950072 , c = 0.461019694351388 , d = 7.23067904029629 [20250519_005558.]: Creating BiasCorrected (hyperbolic) plot No. 7 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG7_corrected_h.png [20250519_005558.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = 162158036.04427 , a = 37148305.7394653 , b = 162158036.04427 , d = 42707972.2066412 [20250519_005558.]: # CpG-site: CpG#7 Cubic: Using a = -0.000133937721266082 , b = 0.0182624909642956 , c = 0.234528921247686 , d = 7.29090555727988 [20250519_005600.]: Creating BiasCorrected (hyperbolic) plot No. 8 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG8_corrected_h.png [20250519_005600.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = 54899408.7962443 , a = 51832403.6152701 , b = 54899408.7962443 , d = 53714624.7099072 [20250519_005600.]: # CpG-site: CpG#8 Cubic: Using a = -5.74524696829791e-05 , b = 0.00998862677056794 , c = 0.502915955578275 , d = 4.90625922077111 [20250519_005601.]: Creating BiasCorrected (hyperbolic) plot No. 9 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG9_corrected_h.png [20250519_005601.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = 170227220.836014 , a = 37110575.5394583 , b = 170227220.836014 , d = 42946902.7624502 [20250519_005601.]: # CpG-site: CpG#9 Cubic: Using a = -1.38503636743099e-05 , b = 0.00591895875024497 , c = 0.445742549227169 , d = 8.73330013353405 [20250519_005603.]: Creating BiasCorrected (hyperbolic) plot No. 10 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_rowmeans_corrected_h.png [20250519_005603.]: # CpG-site: row_means Hyperbolic: Using bias_weight = 131846991.813373 , a = 45678337.0783695 , b = 131846991.813373 , d = 50198768.4229237 [20250519_005603.]: # CpG-site: row_means Cubic: Using a = -7.67916401132796e-05 , b = 0.0124890194887903 , c = 0.369098504101218 , d = 6.83740913189534 [20250519_005605.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG1_corrected_h.png [20250519_005607.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG2_corrected_h.png [20250519_005609.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG3_corrected_h.png [20250519_005611.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG4_corrected_h.png [20250519_005612.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG5_corrected_h.png [20250519_005614.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG6_corrected_h.png [20250519_005616.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG7_corrected_h.png [20250519_005618.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG8_corrected_h.png [20250519_005619.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG9_corrected_h.png [20250519_005621.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_rowmeans_corrected_h.png [20250519_005623.]: Entered 'solving_equations'-Function [20250519_005623.]: Solving cubic regression for CpG#1 Coefficients: -1.03617340067344Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_005623.]: Samplename: 0 Root: 1.334 --> Root in between the borders! Added to results. Coefficients: -8.34150673400678Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_005623.]: Samplename: 12.5 Root: 11.446 --> Root in between the borders! Added to results. Coefficients: -15.3881734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_005623.]: Samplename: 25 Root: 22.228 --> Root in between the borders! Added to results. Coefficients: -24.2801734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_005623.]: Samplename: 37.5 Root: 36.374 --> Root in between the borders! Added to results. Coefficients: -34.9006734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_005623.]: Samplename: 50 Root: 52.044 --> Root in between the borders! Added to results. Coefficients: -46.3541734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_005623.]: Samplename: 62.5 Root: 66.144 --> Root in between the borders! Added to results. Coefficients: -55.8931734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_005623.]: Samplename: 75 Root: 75.864 --> Root in between the borders! Added to results. Coefficients: -63.0981734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_005623.]: Samplename: 87.5 Root: 82.254 --> Root in between the borders! Added to results. Coefficients: -91.0461734006735Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_005623.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.877 --> '100 < root < 110' --> substitute 100 [20250519_005623.]: Solving cubic regression for CpG#2 Coefficients: -0.283329966329966Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_005623.]: Samplename: 0 Root: 0.549 --> Root in between the borders! Added to results. Coefficients: -6.33999663299663Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_005623.]: Samplename: 12.5 Root: 11.533 --> Root in between the borders! Added to results. Coefficients: -15.93932996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_005623.]: Samplename: 25 Root: 26.628 --> Root in between the borders! Added to results. Coefficients: -22.33732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_005623.]: Samplename: 37.5 Root: 35.509 --> Root in between the borders! Added to results. Coefficients: -32.22832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_005623.]: Samplename: 50 Root: 47.851 --> Root in between the borders! Added to results. Coefficients: -49.96332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_005623.]: Samplename: 62.5 Root: 66.893 --> Root in between the borders! Added to results. Coefficients: -58.96582996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_005623.]: Samplename: 75 Root: 75.431 --> Root in between the borders! Added to results. Coefficients: -68.8366632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_005623.]: Samplename: 87.5 Root: 84.118 --> Root in between the borders! Added to results. Coefficients: -90.57732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_005623.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.287 --> '100 < root < 110' --> substitute 100 [20250519_005623.]: Solving cubic regression for CpG#3 Coefficients: -0.90294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_005623.]: Samplename: 0 Root: 1.441 --> Root in between the borders! Added to results. Coefficients: -6.57294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_005623.]: Samplename: 12.5 Root: 10.568 --> Root in between the borders! Added to results. Coefficients: -15.4289478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_005623.]: Samplename: 25 Root: 24.796 --> Root in between the borders! Added to results. Coefficients: -22.6129478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_005623.]: Samplename: 37.5 Root: 35.952 --> Root in between the borders! Added to results. Coefficients: -32.7754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_005623.]: Samplename: 50 Root: 50.684 --> Root in between the borders! Added to results. Coefficients: -43.8889478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_005623.]: Samplename: 62.5 Root: 65.142 --> Root in between the borders! Added to results. Coefficients: -54.9754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_005623.]: Samplename: 75 Root: 77.905 --> Root in between the borders! Added to results. Coefficients: -57.6562811447812Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_005623.]: Samplename: 87.5 Root: 80.767 --> Root in between the borders! Added to results. Coefficients: -80.6649478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_005623.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.38 --> '100 < root < 110' --> substitute 100 [20250519_005623.]: Solving cubic regression for CpG#4 Coefficients: -0.597449494949524Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_005623.]: Samplename: 0 Root: 0.858 --> Root in between the borders! Added to results. Coefficients: -8.25278282828286Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_005623.]: Samplename: 12.5 Root: 12.086 --> Root in between the borders! Added to results. Coefficients: -15.8034494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_005623.]: Samplename: 25 Root: 23.316 --> Root in between the borders! Added to results. Coefficients: -25.5274494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_005623.]: Samplename: 37.5 Root: 37.383 --> Root in between the borders! Added to results. Coefficients: -33.6369494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_005623.]: Samplename: 50 Root: 48.353 --> Root in between the borders! Added to results. Coefficients: -50.2554494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_005623.]: Samplename: 62.5 Root: 68.082 --> Root in between the borders! Added to results. Coefficients: -56.5394494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_005623.]: Samplename: 75 Root: 74.615 --> Root in between the borders! Added to results. Coefficients: -65.5927828282829Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_005623.]: Samplename: 87.5 Root: 83.254 --> Root in between the borders! Added to results. Coefficients: -88.3214494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_005623.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.715 --> '100 < root < 110' --> substitute 100 [20250519_005623.]: Solving cubic regression for CpG#5 Coefficients: -0.623961279461278Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_005623.]: Samplename: 0 Root: 1.458 --> Root in between the borders! Added to results. Coefficients: -4.76796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_005623.]: Samplename: 12.5 Root: 10.347 --> Root in between the borders! Added to results. Coefficients: -12.7259612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_005623.]: Samplename: 25 Root: 24.815 --> Root in between the borders! Added to results. Coefficients: -21.1599612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_005623.]: Samplename: 37.5 Root: 37.902 --> Root in between the borders! Added to results. Coefficients: -30.6954612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_005623.]: Samplename: 50 Root: 50.977 --> Root in between the borders! Added to results. Coefficients: -39.6579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_005623.]: Samplename: 62.5 Root: 62.126 --> Root in between the borders! Added to results. Coefficients: -51.6829612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_005623.]: Samplename: 75 Root: 75.852 --> Root in between the borders! Added to results. Coefficients: -61.0146279461279Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_005623.]: Samplename: 87.5 Root: 85.767 --> Root in between the borders! Added to results. Coefficients: -76.0699612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_005623.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.743 --> '100 < root < 110' --> substitute 100 [20250519_005623.]: Solving cubic regression for CpG#6 Coefficients: -0.196072390572403Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_005623.]: Samplename: 0 Root: 0.349 --> Root in between the borders! Added to results. Coefficients: -6.73873905723907Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_005623.]: Samplename: 12.5 Root: 11.718 --> Root in between the borders! Added to results. Coefficients: -15.8880723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_005623.]: Samplename: 25 Root: 26.396 --> Root in between the borders! Added to results. Coefficients: -22.0000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_005623.]: Samplename: 37.5 Root: 35.301 --> Root in between the borders! Added to results. Coefficients: -33.4445723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_005623.]: Samplename: 50 Root: 50.134 --> Root in between the borders! Added to results. Coefficients: -46.9000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_005623.]: Samplename: 62.5 Root: 64.993 --> Root in between the borders! Added to results. Coefficients: -55.8320723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_005623.]: Samplename: 75 Root: 73.639 --> Root in between the borders! Added to results. Coefficients: -71.5454057239057Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_005623.]: Samplename: 87.5 Root: 87.043 --> Root in between the borders! Added to results. Coefficients: -89.6560723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_005623.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.329 --> '100 < root < 110' --> substitute 100 [20250519_005623.]: Solving cubic regression for CpG#7 Coefficients: -1.21495454545456Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_005623.]: Samplename: 0 Root: 2.13 --> Root in between the borders! Added to results. Coefficients: -5.39562121212123Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_005623.]: Samplename: 12.5 Root: 9.973 --> Root in between the borders! Added to results. Coefficients: -11.2649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_005623.]: Samplename: 25 Root: 22.206 --> Root in between the borders! Added to results. Coefficients: -17.4509545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_005623.]: Samplename: 37.5 Root: 35.814 --> Root in between the borders! Added to results. Coefficients: -26.0314545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_005623.]: Samplename: 50 Root: 53.28 --> Root in between the borders! Added to results. Coefficients: -33.9649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_005623.]: Samplename: 62.5 Root: 66.598 --> Root in between the borders! Added to results. Coefficients: -41.1689545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_005623.]: Samplename: 75 Root: 76.575 --> Root in between the borders! Added to results. Coefficients: -44.1356212121212Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_005623.]: Samplename: 87.5 Root: 80.219 --> Root in between the borders! Added to results. Coefficients: -67.2229545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_005623.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.506 --> '100 < root < 110' --> substitute 100 [20250519_005623.]: Solving cubic regression for CpG#8 Coefficients: -1.09618518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_005623.]: Samplename: 0 Root: 2.016 --> Root in between the borders! Added to results. Coefficients: -5.44685185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_005623.]: Samplename: 12.5 Root: 9.458 --> Root in between the borders! Added to results. Coefficients: -16.9301851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_005623.]: Samplename: 25 Root: 26.35 --> Root in between the borders! Added to results. Coefficients: -23.3501851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_005623.]: Samplename: 37.5 Root: 34.728 --> Root in between the borders! Added to results. Coefficients: -37.6251851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_005623.]: Samplename: 50 Root: 51.781 --> Root in between the borders! Added to results. Coefficients: -48.8261851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_005623.]: Samplename: 62.5 Root: 64.192 --> Root in between the borders! Added to results. Coefficients: -61.6676851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_005623.]: Samplename: 75 Root: 77.804 --> Root in between the borders! Added to results. Coefficients: -64.5101851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_005623.]: Samplename: 87.5 Root: 80.758 --> Root in between the borders! Added to results. Coefficients: -86.0601851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_005623.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.834 --> '100 < root < 110' --> substitute 100 [20250519_005623.]: Solving cubic regression for CpG#9 Coefficients: -0.989865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_005623.]: Samplename: 0 Root: 1.475 --> Root in between the borders! Added to results. Coefficients: -6.39586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_005623.]: Samplename: 12.5 Root: 10.12 --> Root in between the borders! Added to results. Coefficients: -14.7058653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_005623.]: Samplename: 25 Root: 24.844 --> Root in between the borders! Added to results. Coefficients: -20.6238653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_005623.]: Samplename: 37.5 Root: 35.327 --> Root in between the borders! Added to results. Coefficients: -31.3958653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_005623.]: Samplename: 50 Root: 51.855 --> Root in between the borders! Added to results. Coefficients: -42.6858653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_005623.]: Samplename: 62.5 Root: 65.265 --> Root in between the borders! Added to results. Coefficients: -52.9033653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_005623.]: Samplename: 75 Root: 74.915 --> Root in between the borders! Added to results. Coefficients: -65.492531986532Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_005623.]: Samplename: 87.5 Root: 84.67 --> Root in between the borders! Added to results. Coefficients: -92.9898653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_005623.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.082 --> '100 < root < 110' --> substitute 100 [20250519_005623.]: Solving cubic regression for row_means Coefficients: -0.771215488215478Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_005623.]: Samplename: 0 Root: 1.287 --> Root in between the borders! Added to results. Coefficients: -6.47247474747474Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_005623.]: Samplename: 12.5 Root: 10.847 --> Root in between the borders! Added to results. Coefficients: -14.8972154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_005623.]: Samplename: 25 Root: 24.737 --> Root in between the borders! Added to results. Coefficients: -22.1492154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_005623.]: Samplename: 37.5 Root: 36.02 --> Root in between the borders! Added to results. Coefficients: -32.5259932659933Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_005623.]: Samplename: 50 Root: 50.639 --> Root in between the borders! Added to results. Coefficients: -44.7218821548821Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_005623.]: Samplename: 62.5 Root: 65.497 --> Root in between the borders! Added to results. Coefficients: -54.4032154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_005623.]: Samplename: 75 Root: 75.751 --> Root in between the borders! Added to results. Coefficients: -62.4313636363636Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_005623.]: Samplename: 87.5 Root: 83.403 --> Root in between the borders! Added to results. Coefficients: -84.7343265993266Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_005623.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.573 --> '100 < root < 110' --> substitute 100 [20250519_005623.]: ### Starting with regression calculations ### [20250519_005623.]: Entered 'regression_type1'-Function [20250519_005623.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) [20250519_005624.]: Logging df_agg: CpG#1 [20250519_005624.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005624.]: c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)[20250519_005624.]: c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)[20250519_005624.]: c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) [20250519_005624.]: Entered 'hyperbolic_regression'-Function [20250519_005624.]: 'hyperbolic_regression': minmax = FALSE [20250519_005624.]: Entered 'cubic_regression'-Function [20250519_005624.]: 'cubic_regression': minmax = FALSE [20250519_005624.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) [20250519_005624.]: Logging df_agg: CpG#2 [20250519_005624.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005624.]: c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)[20250519_005624.]: c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)[20250519_005624.]: c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) [20250519_005624.]: Entered 'hyperbolic_regression'-Function [20250519_005624.]: 'hyperbolic_regression': minmax = FALSE [20250519_005624.]: Entered 'cubic_regression'-Function [20250519_005624.]: 'cubic_regression': minmax = FALSE [20250519_005624.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) [20250519_005624.]: Logging df_agg: CpG#3 [20250519_005624.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005624.]: c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)[20250519_005624.]: c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)[20250519_005624.]: c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) [20250519_005624.]: Entered 'hyperbolic_regression'-Function [20250519_005624.]: 'hyperbolic_regression': minmax = FALSE [20250519_005624.]: Entered 'cubic_regression'-Function [20250519_005624.]: 'cubic_regression': minmax = FALSE [20250519_005624.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) [20250519_005624.]: Logging df_agg: CpG#4 [20250519_005624.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005624.]: c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)[20250519_005624.]: c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)[20250519_005624.]: c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) [20250519_005624.]: Entered 'hyperbolic_regression'-Function [20250519_005624.]: 'hyperbolic_regression': minmax = FALSE [20250519_005624.]: Entered 'cubic_regression'-Function [20250519_005624.]: 'cubic_regression': minmax = FALSE [20250519_005624.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) [20250519_005624.]: Logging df_agg: CpG#5 [20250519_005624.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005624.]: c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)[20250519_005624.]: c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)[20250519_005624.]: c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) [20250519_005624.]: Entered 'hyperbolic_regression'-Function [20250519_005624.]: 'hyperbolic_regression': minmax = FALSE [20250519_005624.]: Entered 'cubic_regression'-Function [20250519_005624.]: 'cubic_regression': minmax = FALSE [20250519_005624.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) [20250519_005624.]: Logging df_agg: CpG#6 [20250519_005624.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005624.]: c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)[20250519_005624.]: c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)[20250519_005624.]: c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) [20250519_005624.]: Entered 'hyperbolic_regression'-Function [20250519_005624.]: 'hyperbolic_regression': minmax = FALSE [20250519_005624.]: Entered 'cubic_regression'-Function [20250519_005624.]: 'cubic_regression': minmax = FALSE [20250519_005624.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) [20250519_005624.]: Logging df_agg: CpG#7 [20250519_005624.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005624.]: c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)[20250519_005624.]: c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)[20250519_005624.]: c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) [20250519_005624.]: Entered 'hyperbolic_regression'-Function [20250519_005624.]: 'hyperbolic_regression': minmax = FALSE [20250519_005624.]: Entered 'cubic_regression'-Function [20250519_005624.]: 'cubic_regression': minmax = FALSE [20250519_005624.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) [20250519_005624.]: Logging df_agg: CpG#8 [20250519_005624.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005624.]: c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)[20250519_005624.]: c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)[20250519_005624.]: c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) [20250519_005624.]: Entered 'hyperbolic_regression'-Function [20250519_005624.]: 'hyperbolic_regression': minmax = FALSE [20250519_005624.]: Entered 'cubic_regression'-Function [20250519_005624.]: 'cubic_regression': minmax = FALSE [20250519_005624.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) [20250519_005624.]: Logging df_agg: CpG#9 [20250519_005624.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005624.]: c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)[20250519_005624.]: c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)[20250519_005624.]: c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) [20250519_005624.]: Entered 'hyperbolic_regression'-Function [20250519_005624.]: 'hyperbolic_regression': minmax = FALSE [20250519_005624.]: Entered 'cubic_regression'-Function [20250519_005624.]: 'cubic_regression': minmax = FALSE [20250519_005624.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) [20250519_005624.]: Logging df_agg: row_means [20250519_005624.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005624.]: c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)[20250519_005624.]: c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)[20250519_005624.]: c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) [20250519_005624.]: Entered 'hyperbolic_regression'-Function [20250519_005624.]: 'hyperbolic_regression': minmax = FALSE [20250519_005624.]: Entered 'cubic_regression'-Function [20250519_005624.]: 'cubic_regression': minmax = FALSE [20250519_005624.]: ### Starting with plotting ### [20250519_005624.]: Creating BiasCorrected (cubic) plot No. 1 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG1_corrected_c.png [20250519_005624.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -1164.7742156272 , a = 2039.5018241568 , b = -1164.7742156272 , d = 1955.83895682798 [20250519_005624.]: # CpG-site: CpG#1 Cubic: Using a = -3.42821196240366e-05 , b = 0.00462173155013798 , c = 0.850326258865826 , d = 0.500073874211125 [20250519_005626.]: Creating BiasCorrected (cubic) plot No. 2 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG2_corrected_c.png [20250519_005626.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = -464.155645361511 , a = 4988.81163391504 , b = -464.155645361511 , d = 4925.28408403839 [20250519_005626.]: # CpG-site: CpG#2 Cubic: Using a = -1.40811796906314e-05 , b = 0.00190849431754485 , c = 0.933455870941473 , d = 0.363096210099674 [20250519_005628.]: Creating BiasCorrected (cubic) plot No. 3 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG3_corrected_c.png [20250519_005628.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = -745.602352161431 , a = 2026.54103071808 , b = -745.602352161431 , d = 1953.2909386646 [20250519_005628.]: # CpG-site: CpG#3 Cubic: Using a = -2.78835051857186e-05 , b = 0.00366863707889089 , c = 0.880665171220893 , d = 0.510779308833653 [20250519_005630.]: Creating BiasCorrected (cubic) plot No. 4 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG4_corrected_c.png [20250519_005630.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = -833.06710731958 , a = 2871.12609903594 , b = -833.06710731958 , d = 2796.80085807845 [20250519_005630.]: # CpG-site: CpG#4 Cubic: Using a = -2.30028865643e-05 , b = 0.00309017851940692 , c = 0.897073107777645 , d = 0.443455761841866 [20250519_005632.]: Creating BiasCorrected (cubic) plot No. 5 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG5_corrected_c.png [20250519_005632.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = 221.879400146024 , a = 12476.5710927987 , b = 221.879400146024 , d = 12431.2329555778 [20250519_005632.]: # CpG-site: CpG#5 Cubic: Using a = -1.28632696989134e-05 , b = 0.00184822285299014 , c = 0.931001046631176 , d = 0.437963095185599 [20250519_005634.]: Creating BiasCorrected (cubic) plot No. 6 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG6_corrected_c.png [20250519_005634.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = -700.537394936109 , a = 21298.052707184 , b = -700.537394936109 , d = 21233.4563350159 [20250519_005634.]: # CpG-site: CpG#6 Cubic: Using a = -5.838335151375e-06 , b = 0.000828447163218526 , c = 0.970047764475166 , d = 0.158074701412251 [20250519_005636.]: Creating BiasCorrected (cubic) plot No. 7 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG7_corrected_c.png [20250519_005636.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -1144.91075475684 , a = 1477.92088633595 , b = -1144.91075475684 , d = 1394.70940380214 [20250519_005636.]: # CpG-site: CpG#7 Cubic: Using a = -4.54220535614047e-05 , b = 0.00607993130565131 , c = 0.805411493452389 , d = 0.615125382790254 [20250519_005637.]: Creating BiasCorrected (cubic) plot No. 8 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG8_corrected_c.png [20250519_005637.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = -349.322022819744 , a = 2377.42555416743 , b = -349.322022819744 , d = 2315.25402084122 [20250519_005637.]: # CpG-site: CpG#8 Cubic: Using a = -2.64622306669639e-05 , b = 0.00353621593528015 , c = 0.877916844709074 , d = 0.698684606493051 [20250519_005639.]: Creating BiasCorrected (cubic) plot No. 9 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG9_corrected_c.png [20250519_005639.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -1536.91807795025 , a = 3745.12601816225 , b = -1536.91807795025 , d = 3656.21766932252 [20250519_005639.]: # CpG-site: CpG#9 Cubic: Using a = -2.70976130611725e-05 , b = 0.00378478247182342 , c = 0.871825263051793 , d = 0.455475740974113 [20250519_005641.]: Creating BiasCorrected (cubic) plot No. 10 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_rowmeans_corrected_c.png [20250519_005641.]: # CpG-site: row_means Hyperbolic: Using bias_weight = -820.655299479511 , a = 3198.47118291101 , b = -820.655299479511 , d = 3124.21569118411 [20250519_005641.]: # CpG-site: row_means Cubic: Using a = -2.34328362100009e-05 , b = 0.00319139073176266 , c = 0.891842168032129 , d = 0.493517689787135 [20250519_005643.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG1_corrected_c.png [20250519_005645.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG2_corrected_c.png [20250519_005646.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG3_corrected_c.png [20250519_005648.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG4_corrected_c.png [20250519_005650.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG5_corrected_c.png [20250519_005652.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG6_corrected_c.png [20250519_005653.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG7_corrected_c.png [20250519_005655.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG8_corrected_c.png [20250519_005657.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG9_corrected_c.png [20250519_005659.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_rowmeans_corrected_c.png [20250519_005700.]: Entered 'solving_equations'-Function [20250519_005700.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 77.9602582684049 [20250519_005700.]: Samplename: Sample#1 Root: 77.96 --> Root in between the borders! Added to results. Hyperbolic solved: 27.1420961794087 [20250519_005700.]: Samplename: Sample#10 Root: 27.142 --> Root in between the borders! Added to results. Hyperbolic solved: 36.7640366116659 [20250519_005700.]: Samplename: Sample#2 Root: 36.764 --> Root in between the borders! Added to results. Hyperbolic solved: 51.6231208412737 [20250519_005700.]: Samplename: Sample#3 Root: 51.623 --> Root in between the borders! Added to results. Hyperbolic solved: 12.873561614208 [20250519_005700.]: Samplename: Sample#4 Root: 12.874 --> Root in between the borders! Added to results. Hyperbolic solved: 21.2773642339947 [20250519_005700.]: Samplename: Sample#5 Root: 21.277 --> Root in between the borders! Added to results. Hyperbolic solved: 22.1858835788516 [20250519_005700.]: Samplename: Sample#6 Root: 22.186 --> Root in between the borders! Added to results. Hyperbolic solved: 40.1506639226714 [20250519_005700.]: Samplename: Sample#7 Root: 40.151 --> Root in between the borders! Added to results. Hyperbolic solved: 86.1385429083643 [20250519_005700.]: Samplename: Sample#8 Root: 86.139 --> Root in between the borders! Added to results. Hyperbolic solved: 5.22287566816154 [20250519_005700.]: Samplename: Sample#9 Root: 5.223 --> Root in between the borders! Added to results. [20250519_005700.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 74.4935883316308 [20250519_005700.]: Samplename: Sample#1 Root: 74.494 --> Root in between the borders! Added to results. Hyperbolic solved: 28.4239304939459 [20250519_005700.]: Samplename: Sample#10 Root: 28.424 --> Root in between the borders! Added to results. Hyperbolic solved: 38.422868577211 [20250519_005700.]: Samplename: Sample#2 Root: 38.423 --> Root in between the borders! Added to results. Hyperbolic solved: 54.1812729208768 [20250519_005700.]: Samplename: Sample#3 Root: 54.181 --> Root in between the borders! Added to results. Hyperbolic solved: 9.35760412501846 [20250519_005700.]: Samplename: Sample#4 Root: 9.358 --> Root in between the borders! Added to results. Hyperbolic solved: 20.0638687855045 [20250519_005700.]: Samplename: Sample#5 Root: 20.064 --> Root in between the borders! Added to results. Hyperbolic solved: 18.2691112345675 [20250519_005700.]: Samplename: Sample#6 Root: 18.269 --> Root in between the borders! Added to results. Hyperbolic solved: 36.8485384997942 [20250519_005700.]: Samplename: Sample#7 Root: 36.849 --> Root in between the borders! Added to results. Hyperbolic solved: 86.7213727244426 [20250519_005700.]: Samplename: Sample#8 Root: 86.721 --> Root in between the borders! Added to results. Hyperbolic solved: 8.13467837636389 [20250519_005700.]: Samplename: Sample#9 Root: 8.135 --> Root in between the borders! Added to results. [20250519_005701.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 72.9754823399013 [20250519_005701.]: Samplename: Sample#1 Root: 72.975 --> Root in between the borders! Added to results. Hyperbolic solved: 26.09308106721 [20250519_005701.]: Samplename: Sample#10 Root: 26.093 --> Root in between the borders! Added to results. Hyperbolic solved: 38.5204516929959 [20250519_005701.]: Samplename: Sample#2 Root: 38.52 --> Root in between the borders! Added to results. Hyperbolic solved: 52.0558039343263 [20250519_005701.]: Samplename: Sample#3 Root: 52.056 --> Root in between the borders! Added to results. Hyperbolic solved: 10.6836235174275 [20250519_005701.]: Samplename: Sample#4 Root: 10.684 --> Root in between the borders! Added to results. Hyperbolic solved: 23.1384875877206 [20250519_005701.]: Samplename: Sample#5 Root: 23.138 --> Root in between the borders! Added to results. Hyperbolic solved: 24.8934322445107 [20250519_005701.]: Samplename: Sample#6 Root: 24.893 --> Root in between the borders! Added to results. Hyperbolic solved: 40.6015033285401 [20250519_005701.]: Samplename: Sample#7 Root: 40.602 --> Root in between the borders! Added to results. Hyperbolic solved: 86.3873125270529 [20250519_005701.]: Samplename: Sample#8 Root: 86.387 --> Root in between the borders! Added to results. Hyperbolic solved: 4.44964604860886 [20250519_005701.]: Samplename: Sample#9 Root: 4.45 --> Root in between the borders! Added to results. [20250519_005701.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 74.4016735507272 [20250519_005701.]: Samplename: Sample#1 Root: 74.402 --> Root in between the borders! Added to results. Hyperbolic solved: 26.4295897789912 [20250519_005701.]: Samplename: Sample#10 Root: 26.43 --> Root in between the borders! Added to results. Hyperbolic solved: 39.6008425259518 [20250519_005701.]: Samplename: Sample#2 Root: 39.601 --> Root in between the borders! Added to results. Hyperbolic solved: 54.4093205134833 [20250519_005701.]: Samplename: Sample#3 Root: 54.409 --> Root in between the borders! Added to results. Hyperbolic solved: 12.6125973016001 [20250519_005701.]: Samplename: Sample#4 Root: 12.613 --> Root in between the borders! Added to results. Hyperbolic solved: 21.5537046072918 [20250519_005701.]: Samplename: Sample#5 Root: 21.554 --> Root in between the borders! Added to results. Hyperbolic solved: 25.5116357756709 [20250519_005701.]: Samplename: Sample#6 Root: 25.512 --> Root in between the borders! Added to results. Hyperbolic solved: 38.1579452647512 [20250519_005701.]: Samplename: Sample#7 Root: 38.158 --> Root in between the borders! Added to results. Hyperbolic solved: 86.2182702652395 [20250519_005701.]: Samplename: Sample#8 Root: 86.218 --> Root in between the borders! Added to results. Hyperbolic solved: 7.06911234650237 [20250519_005701.]: Samplename: Sample#9 Root: 7.069 --> Root in between the borders! Added to results. [20250519_005701.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 70.2481376198381 [20250519_005701.]: Samplename: Sample#1 Root: 70.248 --> Root in between the borders! Added to results. Hyperbolic solved: 24.9396715205194 [20250519_005701.]: Samplename: Sample#10 Root: 24.94 --> Root in between the borders! Added to results. Hyperbolic solved: 40.4614457485807 [20250519_005701.]: Samplename: Sample#2 Root: 40.461 --> Root in between the borders! Added to results. Hyperbolic solved: 49.3815751365795 [20250519_005701.]: Samplename: Sample#3 Root: 49.382 --> Root in between the borders! Added to results. Hyperbolic solved: 12.353607679335 [20250519_005701.]: Samplename: Sample#4 Root: 12.354 --> Root in between the borders! Added to results. Hyperbolic solved: 22.0901459497739 [20250519_005701.]: Samplename: Sample#5 Root: 22.09 --> Root in between the borders! Added to results. Hyperbolic solved: 24.5507390437226 [20250519_005701.]: Samplename: Sample#6 Root: 24.551 --> Root in between the borders! Added to results. Hyperbolic solved: 39.0947007538418 [20250519_005701.]: Samplename: Sample#7 Root: 39.095 --> Root in between the borders! Added to results. Hyperbolic solved: 90.4841321892294 [20250519_005701.]: Samplename: Sample#8 Root: 90.484 --> Root in between the borders! Added to results. Hyperbolic solved: 6.91649336851909 [20250519_005701.]: Samplename: Sample#9 Root: 6.916 --> Root in between the borders! Added to results. [20250519_005701.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 77.4558189028663 [20250519_005701.]: Samplename: Sample#1 Root: 77.456 --> Root in between the borders! Added to results. Hyperbolic solved: 27.5880652239037 [20250519_005701.]: Samplename: Sample#10 Root: 27.588 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2657605119474 [20250519_005701.]: Samplename: Sample#2 Root: 37.266 --> Root in between the borders! Added to results. Hyperbolic solved: 51.469303759821 [20250519_005701.]: Samplename: Sample#3 Root: 51.469 --> Root in between the borders! Added to results. Hyperbolic solved: 12.3197919642261 [20250519_005701.]: Samplename: Sample#4 Root: 12.32 --> Root in between the borders! Added to results. Hyperbolic solved: 18.9916700132306 [20250519_005701.]: Samplename: Sample#5 Root: 18.992 --> Root in between the borders! Added to results. Hyperbolic solved: 27.3757521451495 [20250519_005701.]: Samplename: Sample#6 Root: 27.376 --> Root in between the borders! Added to results. Hyperbolic solved: 38.1192529388954 [20250519_005701.]: Samplename: Sample#7 Root: 38.119 --> Root in between the borders! Added to results. Hyperbolic solved: 87.7229687796537 [20250519_005701.]: Samplename: Sample#8 Root: 87.723 --> Root in between the borders! Added to results. Hyperbolic solved: 7.66260533640959 [20250519_005701.]: Samplename: Sample#9 Root: 7.663 --> Root in between the borders! Added to results. [20250519_005701.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 75.6858966925732 [20250519_005701.]: Samplename: Sample#1 Root: 75.686 --> Root in between the borders! Added to results. Hyperbolic solved: 23.9111476853231 [20250519_005701.]: Samplename: Sample#10 Root: 23.911 --> Root in between the borders! Added to results. Hyperbolic solved: 41.6834100561109 [20250519_005701.]: Samplename: Sample#2 Root: 41.683 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3038531152796 [20250519_005701.]: Samplename: Sample#3 Root: 52.304 --> Root in between the borders! Added to results. Hyperbolic solved: 14.584082616843 [20250519_005701.]: Samplename: Sample#4 Root: 14.584 --> Root in between the borders! Added to results. Hyperbolic solved: 23.8322996387272 [20250519_005701.]: Samplename: Sample#5 Root: 23.832 --> Root in between the borders! Added to results. Hyperbolic solved: 26.8055282397054 [20250519_005701.]: Samplename: Sample#6 Root: 26.806 --> Root in between the borders! Added to results. Hyperbolic solved: 39.1748201958722 [20250519_005701.]: Samplename: Sample#7 Root: 39.175 --> Root in between the borders! Added to results. Hyperbolic solved: 86.4968654540458 [20250519_005701.]: Samplename: Sample#8 Root: 86.497 --> Root in between the borders! Added to results. Hyperbolic solved: 5.20445580813888 [20250519_005701.]: Samplename: Sample#9 Root: 5.204 --> Root in between the borders! Added to results. [20250519_005701.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 71.4358559327679 [20250519_005701.]: Samplename: Sample#1 Root: 71.436 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5978231834828 [20250519_005701.]: Samplename: Sample#10 Root: 26.598 --> Root in between the borders! Added to results. Hyperbolic solved: 32.9687797883659 [20250519_005701.]: Samplename: Sample#2 Root: 32.969 --> Root in between the borders! Added to results. Hyperbolic solved: 56.5920244960613 [20250519_005701.]: Samplename: Sample#3 Root: 56.592 --> Root in between the borders! Added to results. Hyperbolic solved: 11.2061554830111 [20250519_005701.]: Samplename: Sample#4 Root: 11.206 --> Root in between the borders! Added to results. Hyperbolic solved: 19.2580019302788 [20250519_005701.]: Samplename: Sample#5 Root: 19.258 --> Root in between the borders! Added to results. Hyperbolic solved: 36.3407423784248 [20250519_005701.]: Samplename: Sample#6 Root: 36.341 --> Root in between the borders! Added to results. Hyperbolic solved: 42.2292176199449 [20250519_005701.]: Samplename: Sample#7 Root: 42.229 --> Root in between the borders! Added to results. Hyperbolic solved: 85.1479651236815 [20250519_005701.]: Samplename: Sample#8 Root: 85.148 --> Root in between the borders! Added to results. Hyperbolic solved: 1.97353330914679 [20250519_005701.]: Samplename: Sample#9 Root: 1.974 --> Root in between the borders! Added to results. [20250519_005701.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 78.362574368733 [20250519_005701.]: Samplename: Sample#1 Root: 78.363 --> Root in between the borders! Added to results. Hyperbolic solved: 24.4725957151698 [20250519_005701.]: Samplename: Sample#10 Root: 24.473 --> Root in between the borders! Added to results. Hyperbolic solved: 38.5471090507298 [20250519_005701.]: Samplename: Sample#2 Root: 38.547 --> Root in between the borders! Added to results. Hyperbolic solved: 48.850764118569 [20250519_005701.]: Samplename: Sample#3 Root: 48.851 --> Root in between the borders! Added to results. Hyperbolic solved: 13.0341042345297 [20250519_005701.]: Samplename: Sample#4 Root: 13.034 --> Root in between the borders! Added to results. Hyperbolic solved: 22.1156929084775 [20250519_005701.]: Samplename: Sample#5 Root: 22.116 --> Root in between the borders! Added to results. Hyperbolic solved: 33.0730206710577 [20250519_005701.]: Samplename: Sample#6 Root: 33.073 --> Root in between the borders! Added to results. Hyperbolic solved: 36.8757560868803 [20250519_005701.]: Samplename: Sample#7 Root: 36.876 --> Root in between the borders! Added to results. Hyperbolic solved: 89.6667208986789 [20250519_005701.]: Samplename: Sample#8 Root: 89.667 --> Root in between the borders! Added to results. Hyperbolic solved: 8.10593089892949 [20250519_005701.]: Samplename: Sample#9 Root: 8.106 --> Root in between the borders! Added to results. [20250519_005701.]: Solving hyperbolic regression for row_means Hyperbolic solved: 74.8404708557895 [20250519_005701.]: Samplename: Sample#1 Root: 74.84 --> Root in between the borders! Added to results. Hyperbolic solved: 26.2806125509682 [20250519_005701.]: Samplename: Sample#10 Root: 26.281 --> Root in between the borders! Added to results. Hyperbolic solved: 38.1683813928189 [20250519_005701.]: Samplename: Sample#2 Root: 38.168 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3554454867083 [20250519_005701.]: Samplename: Sample#3 Root: 52.355 --> Root in between the borders! Added to results. Hyperbolic solved: 12.0337443526331 [20250519_005701.]: Samplename: Sample#4 Root: 12.034 --> Root in between the borders! Added to results. Hyperbolic solved: 21.2452112654646 [20250519_005701.]: Samplename: Sample#5 Root: 21.245 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5661392150144 [20250519_005701.]: Samplename: Sample#6 Root: 26.566 --> Root in between the borders! Added to results. Hyperbolic solved: 38.9839362125804 [20250519_005701.]: Samplename: Sample#7 Root: 38.984 --> Root in between the borders! Added to results. Hyperbolic solved: 87.2201089452416 [20250519_005701.]: Samplename: Sample#8 Root: 87.22 --> Root in between the borders! Added to results. Hyperbolic solved: 6.14149092380673 [20250519_005701.]: Samplename: Sample#9 Root: 6.141 --> Root in between the borders! Added to results. [20250519_005701.]: Entered 'clean_dt'-Function [20250519_005701.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_005701.]: got experimental data [20250519_005701.]: Entered 'clean_dt'-Function [20250519_005701.]: Importing data of type 2: Many loci in one sample (e.g., next-gen seq or microarray data) [20250519_005701.]: got experimental data [20250519_005701.]: Entered 'clean_dt'-Function [20250519_005701.]: Entered 'clean_dt'-Function [20250519_005701.]: Entered 'clean_dt'-Function [20250519_005701.]: Entered 'clean_dt'-Function [20250519_005701.]: Entered 'clean_dt'-Function [20250519_005701.]: Entered 'clean_dt'-Function [20250519_005701.]: Entered 'clean_dt'-Function [20250519_005701.]: Entered 'clean_dt'-Function [20250519_005701.]: Entered 'clean_dt'-Function [20250519_005701.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_005701.]: got calibration data ### ERROR 54: first column cannot be parsed to numeric ### [20250519_005701.]: Entered 'clean_dt'-Function [20250519_005701.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_005701.]: got calibration data ### ERROR 54: first column cannot be parsed to numeric ### [20250519_005701.]: Entered 'clean_dt'-Function [20250519_005701.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_005701.]: got calibration data [20250519_005701.]: ### ERROR ### The data provided contains less than four calibration steps. At least four distinct calibration steps are required to perform bias correction. [20250519_005701.]: Entered 'clean_dt'-Function [20250519_005701.]: Importing data of type 2: Many loci in one sample (e.g., next-gen seq or microarray data) [20250519_005701.]: got calibration data [20250519_005701.]: ### ERROR ### The data provided contains locus ids with heterogeneous counts of CpG-sites. [20250519_005701.]: Entered 'clean_dt'-Function [20250519_005701.]: Importing data of type 2: Many loci in one sample (e.g., next-gen seq or microarray data) [20250519_005701.]: got experimental data [20250519_005701.]: ### ERROR ### The data provided contains locus ids with heterogeneous counts of CpG-sites. [20250519_005701.]: Entered 'clean_dt'-Function [20250519_005701.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_005701.]: got calibration data [20250519_005702.]: Entered 'clean_dt'-Function [20250519_005702.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_005702.]: got calibration data [20250519_005702.]: Entered 'cubic_regression'-Function [20250519_005702.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005702.]: Entered 'cubic_regression'-Function [20250519_005702.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005702.]: Entered 'clean_dt'-Function [20250519_005702.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_005702.]: got calibration data [20250519_005702.]: Entered 'hyperbolic_regression'-Function [20250519_005702.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005702.]: Entered 'hyperbolic_regression'-Function [20250519_005702.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005702.]: on_start: using future::plan("sequential") [20250519_005702.]: Entered 'clean_dt'-Function [20250519_005702.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_005702.]: got experimental data [20250519_005702.]: Entered 'clean_dt'-Function [20250519_005702.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_005702.]: got calibration data [20250519_005702.]: ### Starting with regression calculations ### [20250519_005702.]: Entered 'regression_type1'-Function [20250519_005702.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005702.]: Logging df_agg: CpG#1 [20250519_005702.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005702.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005702.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005702.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005702.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005702.]: Entered 'hyperbolic_regression'-Function [20250519_005702.]: 'hyperbolic_regression': minmax = FALSE [20250519_005702.]: Entered 'cubic_regression'-Function [20250519_005702.]: 'cubic_regression': minmax = FALSE [20250519_005702.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005702.]: Logging df_agg: CpG#2 [20250519_005702.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005702.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005702.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005702.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005702.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005702.]: Entered 'hyperbolic_regression'-Function [20250519_005702.]: 'hyperbolic_regression': minmax = FALSE [20250519_005702.]: Entered 'cubic_regression'-Function [20250519_005702.]: 'cubic_regression': minmax = FALSE [20250519_005702.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005702.]: Logging df_agg: CpG#3 [20250519_005702.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005702.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005702.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005702.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005702.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005702.]: Entered 'hyperbolic_regression'-Function [20250519_005702.]: 'hyperbolic_regression': minmax = FALSE [20250519_005702.]: Entered 'cubic_regression'-Function [20250519_005702.]: 'cubic_regression': minmax = FALSE [20250519_005702.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005702.]: Logging df_agg: CpG#4 [20250519_005702.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005702.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005702.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005702.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005702.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005702.]: Entered 'hyperbolic_regression'-Function [20250519_005702.]: 'hyperbolic_regression': minmax = FALSE [20250519_005702.]: Entered 'cubic_regression'-Function [20250519_005702.]: 'cubic_regression': minmax = FALSE [20250519_005702.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005702.]: Logging df_agg: CpG#5 [20250519_005702.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005702.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005702.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005702.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005702.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005702.]: Entered 'hyperbolic_regression'-Function [20250519_005702.]: 'hyperbolic_regression': minmax = FALSE [20250519_005702.]: Entered 'cubic_regression'-Function [20250519_005702.]: 'cubic_regression': minmax = FALSE [20250519_005702.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005702.]: Logging df_agg: CpG#6 [20250519_005702.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005702.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005702.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005702.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005702.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005702.]: Entered 'hyperbolic_regression'-Function [20250519_005702.]: 'hyperbolic_regression': minmax = FALSE [20250519_005702.]: Entered 'cubic_regression'-Function [20250519_005702.]: 'cubic_regression': minmax = FALSE [20250519_005702.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005702.]: Logging df_agg: CpG#7 [20250519_005702.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005702.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005702.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005702.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005702.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005702.]: Entered 'hyperbolic_regression'-Function [20250519_005702.]: 'hyperbolic_regression': minmax = FALSE [20250519_005702.]: Entered 'cubic_regression'-Function [20250519_005702.]: 'cubic_regression': minmax = FALSE [20250519_005702.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005702.]: Logging df_agg: CpG#8 [20250519_005702.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005702.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005702.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005702.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005702.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005702.]: Entered 'hyperbolic_regression'-Function [20250519_005702.]: 'hyperbolic_regression': minmax = FALSE [20250519_005702.]: Entered 'cubic_regression'-Function [20250519_005702.]: 'cubic_regression': minmax = FALSE [20250519_005702.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005702.]: Logging df_agg: CpG#9 [20250519_005702.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005702.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005702.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005702.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005702.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005702.]: Entered 'hyperbolic_regression'-Function [20250519_005702.]: 'hyperbolic_regression': minmax = FALSE [20250519_005703.]: Entered 'cubic_regression'-Function [20250519_005703.]: 'cubic_regression': minmax = FALSE [20250519_005703.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005703.]: Logging df_agg: row_means [20250519_005703.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005703.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005703.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_005703.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005703.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005703.]: Entered 'hyperbolic_regression'-Function [20250519_005703.]: 'hyperbolic_regression': minmax = FALSE [20250519_005703.]: Entered 'cubic_regression'-Function [20250519_005703.]: 'cubic_regression': minmax = FALSE [20250519_005703.]: ### Starting with plotting ### [20250519_005703.]: Creating plot No. 1 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Test_CpG1.png [20250519_005703.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -123249135.40483 , a = 49589952.0989949 , b = -123249135.40483 , d = 59280789.0121927 [20250519_005703.]: # CpG-site: CpG#1 Cubic: Using a = 6.53413423120091e-05 , b = -0.0055806968734969 , c = 0.784061853455188 , d = 1.93182659932656 [20250519_005705.]: Creating plot No. 2 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Test_CpG2.png [20250519_005705.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = 167929575.171327 , a = 42299444.0962795 , b = 167929575.171327 , d = 47897274.2220611 [20250519_005705.]: # CpG-site: CpG#2 Cubic: Using a = 8.04237934904601e-06 , b = 0.00293158941798942 , c = 0.514821742825076 , d = 9.27667003367003 [20250519_005708.]: Creating plot No. 3 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Test_CpG3.png [20250519_005708.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = 31004745.3756238 , a = 31050253.3779659 , b = 31004745.3756238 , d = 40665231.758814 [20250519_005708.]: # CpG-site: CpG#3 Cubic: Using a = 2.30555869809204e-05 , b = -0.000797009331409346 , c = 0.62783129228796 , d = 3.88705218855218 [20250519_005710.]: Creating plot No. 4 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Test_CpG4.png [20250519_005710.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = 128022258.276155 , a = 42779497.5538086 , b = 128022258.276155 , d = 50999439.9227065 [20250519_005710.]: # CpG-site: CpG#4 Cubic: Using a = 3.2166356902357e-05 , b = -0.0015913142857143 , c = 0.697398364598366 , d = 6.17055050505048 [20250519_005712.]: Creating plot No. 5 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Test_CpG5.png [20250519_005712.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = 29447513.6184781 , a = 41864399.8673718 , b = 29447513.6184781 , d = 55382346.8470806 [20250519_005712.]: # CpG-site: CpG#5 Cubic: Using a = -4.48459708193036e-06 , b = 0.00375371236171235 , c = 0.422446384479718 , d = 5.17203872053872 [20250519_005714.]: Creating plot No. 6 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Test_CpG6.png [20250519_005714.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = 17150080.5960938 , a = 40932834.1078748 , b = 17150080.5960938 , d = 46723471.6686748 [20250519_005714.]: # CpG-site: CpG#6 Cubic: Using a = 2.38852884399552e-05 , b = 0.000918637037037021 , c = 0.561022132435467 , d = 6.1479276094276 [20250519_005716.]: Creating plot No. 7 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Test_CpG7.png [20250519_005716.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -57204506.2862076 , a = 29554652.6579632 , b = -57204506.2862076 , d = 48548425.6483363 [20250519_005716.]: # CpG-site: CpG#7 Cubic: Using a = 4.98010505050505e-05 , b = -0.00436152150072151 , c = 0.579588917748918 , d = 1.72104545454544 [20250519_005719.]: Creating plot No. 8 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Test_CpG8.png [20250519_005719.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = 263871870.982016 , a = 38159683.1948542 , b = 263871870.982016 , d = 45284547.6704654 [20250519_005719.]: # CpG-site: CpG#8 Cubic: Using a = -1.50060965207632e-05 , b = 0.00447978143338143 , c = 0.534897737694404 , d = 9.56981481481482 [20250519_005721.]: Creating plot No. 9 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Test_CpG9.png [20250519_005721.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -224404013.660658 , a = 51672526.9851193 , b = -224404013.660658 , d = 60201205.3472543 [20250519_005721.]: # CpG-site: CpG#9 Cubic: Using a = 7.72300426487093e-05 , b = -0.00542281173641174 , c = 0.67899229597563 , d = 2.72413468013467 [20250519_005723.]: Creating plot No. 10 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Test_rowmeans.png [20250519_005723.]: # CpG-site: row_means Hyperbolic: Using bias_weight = 29838275.3876239 , a = 33244770.7608578 , b = 29838275.3876239 , d = 41191067.4974404 [20250519_005723.]: # CpG-site: row_means Cubic: Using a = 2.88923726150392e-05 , b = -0.000629959275292592 , c = 0.600117857944524 , d = 5.17789562289563 [20250519_005725.]: on_start: using future::plan("sequential") [20250519_005725.]: Entered 'clean_dt'-Function [20250519_005725.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_005725.]: got experimental data [20250519_005725.]: Entered 'clean_dt'-Function [20250519_005725.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_005725.]: got calibration data [20250519_005725.]: ### Starting with regression calculations ### [20250519_005725.]: Entered 'regression_type1'-Function [20250519_005725.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005725.]: Logging df_agg: CpG#1 [20250519_005725.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005725.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005725.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005725.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005725.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005725.]: Entered 'hyperbolic_regression'-Function [20250519_005725.]: 'hyperbolic_regression': minmax = FALSE [20250519_005725.]: Entered 'cubic_regression'-Function [20250519_005725.]: 'cubic_regression': minmax = FALSE [20250519_005725.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005725.]: Logging df_agg: CpG#2 [20250519_005725.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005725.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005725.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005725.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005725.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005725.]: Entered 'hyperbolic_regression'-Function [20250519_005725.]: 'hyperbolic_regression': minmax = FALSE [20250519_005725.]: Entered 'cubic_regression'-Function [20250519_005725.]: 'cubic_regression': minmax = FALSE [20250519_005725.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005725.]: Logging df_agg: CpG#3 [20250519_005725.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005725.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005725.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005725.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005725.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005725.]: Entered 'hyperbolic_regression'-Function [20250519_005725.]: 'hyperbolic_regression': minmax = FALSE [20250519_005725.]: Entered 'cubic_regression'-Function [20250519_005725.]: 'cubic_regression': minmax = FALSE [20250519_005725.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005725.]: Logging df_agg: CpG#4 [20250519_005725.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005725.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005725.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005725.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005725.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005725.]: Entered 'hyperbolic_regression'-Function [20250519_005725.]: 'hyperbolic_regression': minmax = FALSE [20250519_005726.]: Entered 'cubic_regression'-Function [20250519_005726.]: 'cubic_regression': minmax = FALSE [20250519_005726.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005726.]: Logging df_agg: CpG#5 [20250519_005726.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005726.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005726.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005726.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005726.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005726.]: Entered 'hyperbolic_regression'-Function [20250519_005726.]: 'hyperbolic_regression': minmax = FALSE [20250519_005726.]: Entered 'cubic_regression'-Function [20250519_005726.]: 'cubic_regression': minmax = FALSE [20250519_005726.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005726.]: Logging df_agg: CpG#6 [20250519_005726.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005726.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005726.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005726.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005726.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005726.]: Entered 'hyperbolic_regression'-Function [20250519_005726.]: 'hyperbolic_regression': minmax = FALSE [20250519_005726.]: Entered 'cubic_regression'-Function [20250519_005726.]: 'cubic_regression': minmax = FALSE [20250519_005726.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005726.]: Logging df_agg: CpG#7 [20250519_005726.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005726.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005726.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005726.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005726.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005726.]: Entered 'hyperbolic_regression'-Function [20250519_005726.]: 'hyperbolic_regression': minmax = FALSE [20250519_005726.]: Entered 'cubic_regression'-Function [20250519_005726.]: 'cubic_regression': minmax = FALSE [20250519_005726.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005726.]: Logging df_agg: CpG#8 [20250519_005726.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005726.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005726.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005726.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005726.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005726.]: Entered 'hyperbolic_regression'-Function [20250519_005726.]: 'hyperbolic_regression': minmax = FALSE [20250519_005726.]: Entered 'cubic_regression'-Function [20250519_005726.]: 'cubic_regression': minmax = FALSE [20250519_005726.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005726.]: Logging df_agg: CpG#9 [20250519_005726.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005726.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005726.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005726.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005726.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005726.]: Entered 'hyperbolic_regression'-Function [20250519_005726.]: 'hyperbolic_regression': minmax = FALSE [20250519_005726.]: Entered 'cubic_regression'-Function [20250519_005726.]: 'cubic_regression': minmax = FALSE [20250519_005726.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005726.]: Logging df_agg: row_means [20250519_005726.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005726.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005726.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_005726.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005726.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005726.]: Entered 'hyperbolic_regression'-Function [20250519_005726.]: 'hyperbolic_regression': minmax = FALSE [20250519_005726.]: Entered 'cubic_regression'-Function [20250519_005726.]: 'cubic_regression': minmax = FALSE [20250519_005726.]: Entered 'solving_equations'-Function [20250519_005726.]: Solving cubic regression for CpG#1 Coefficients: -1.03617340067344Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_005726.]: Samplename: 0 Root: 1.334 --> Root in between the borders! Added to results. Coefficients: -8.34150673400678Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_005726.]: Samplename: 12.5 Root: 11.446 --> Root in between the borders! Added to results. Coefficients: -15.3881734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_005726.]: Samplename: 25 Root: 22.228 --> Root in between the borders! Added to results. Coefficients: -24.2801734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_005726.]: Samplename: 37.5 Root: 36.374 --> Root in between the borders! Added to results. Coefficients: -34.9006734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_005726.]: Samplename: 50 Root: 52.044 --> Root in between the borders! Added to results. Coefficients: -46.3541734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_005726.]: Samplename: 62.5 Root: 66.144 --> Root in between the borders! Added to results. Coefficients: -55.8931734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_005726.]: Samplename: 75 Root: 75.864 --> Root in between the borders! Added to results. Coefficients: -63.0981734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_005726.]: Samplename: 87.5 Root: 82.254 --> Root in between the borders! Added to results. Coefficients: -91.0461734006735Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_005726.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.877 --> '100 < root < 110' --> substitute 100 [20250519_005726.]: Solving cubic regression for CpG#2 Coefficients: -0.283329966329966Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_005726.]: Samplename: 0 Root: 0.549 --> Root in between the borders! Added to results. Coefficients: -6.33999663299663Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_005726.]: Samplename: 12.5 Root: 11.533 --> Root in between the borders! Added to results. Coefficients: -15.93932996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_005726.]: Samplename: 25 Root: 26.628 --> Root in between the borders! Added to results. Coefficients: -22.33732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_005726.]: Samplename: 37.5 Root: 35.509 --> Root in between the borders! Added to results. Coefficients: -32.22832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_005726.]: Samplename: 50 Root: 47.851 --> Root in between the borders! Added to results. Coefficients: -49.96332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_005726.]: Samplename: 62.5 Root: 66.893 --> Root in between the borders! Added to results. Coefficients: -58.96582996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_005726.]: Samplename: 75 Root: 75.431 --> Root in between the borders! Added to results. Coefficients: -68.8366632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_005726.]: Samplename: 87.5 Root: 84.118 --> Root in between the borders! Added to results. Coefficients: -90.57732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_005726.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.287 --> '100 < root < 110' --> substitute 100 [20250519_005726.]: Solving cubic regression for CpG#3 Coefficients: -0.90294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_005726.]: Samplename: 0 Root: 1.441 --> Root in between the borders! Added to results. Coefficients: -6.57294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_005726.]: Samplename: 12.5 Root: 10.568 --> Root in between the borders! Added to results. Coefficients: -15.4289478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_005726.]: Samplename: 25 Root: 24.796 --> Root in between the borders! Added to results. Coefficients: -22.6129478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_005726.]: Samplename: 37.5 Root: 35.952 --> Root in between the borders! Added to results. Coefficients: -32.7754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_005726.]: Samplename: 50 Root: 50.684 --> Root in between the borders! Added to results. Coefficients: -43.8889478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_005727.]: Samplename: 62.5 Root: 65.142 --> Root in between the borders! Added to results. Coefficients: -54.9754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_005727.]: Samplename: 75 Root: 77.905 --> Root in between the borders! Added to results. Coefficients: -57.6562811447812Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_005727.]: Samplename: 87.5 Root: 80.767 --> Root in between the borders! Added to results. Coefficients: -80.6649478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_005727.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.38 --> '100 < root < 110' --> substitute 100 [20250519_005727.]: Solving cubic regression for CpG#4 Coefficients: -0.597449494949524Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_005727.]: Samplename: 0 Root: 0.858 --> Root in between the borders! Added to results. Coefficients: -8.25278282828286Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_005727.]: Samplename: 12.5 Root: 12.086 --> Root in between the borders! Added to results. Coefficients: -15.8034494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_005727.]: Samplename: 25 Root: 23.316 --> Root in between the borders! Added to results. Coefficients: -25.5274494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_005727.]: Samplename: 37.5 Root: 37.383 --> Root in between the borders! Added to results. Coefficients: -33.6369494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_005727.]: Samplename: 50 Root: 48.353 --> Root in between the borders! Added to results. Coefficients: -50.2554494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_005727.]: Samplename: 62.5 Root: 68.082 --> Root in between the borders! Added to results. Coefficients: -56.5394494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_005727.]: Samplename: 75 Root: 74.615 --> Root in between the borders! Added to results. Coefficients: -65.5927828282829Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_005727.]: Samplename: 87.5 Root: 83.254 --> Root in between the borders! Added to results. Coefficients: -88.3214494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_005727.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.715 --> '100 < root < 110' --> substitute 100 [20250519_005727.]: Solving cubic regression for CpG#5 Coefficients: -0.623961279461278Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_005727.]: Samplename: 0 Root: 1.458 --> Root in between the borders! Added to results. Coefficients: -4.76796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_005727.]: Samplename: 12.5 Root: 10.347 --> Root in between the borders! Added to results. Coefficients: -12.7259612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_005727.]: Samplename: 25 Root: 24.815 --> Root in between the borders! Added to results. Coefficients: -21.1599612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_005727.]: Samplename: 37.5 Root: 37.902 --> Root in between the borders! Added to results. Coefficients: -30.6954612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_005727.]: Samplename: 50 Root: 50.977 --> Root in between the borders! Added to results. Coefficients: -39.6579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_005727.]: Samplename: 62.5 Root: 62.126 --> Root in between the borders! Added to results. Coefficients: -51.6829612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_005727.]: Samplename: 75 Root: 75.852 --> Root in between the borders! Added to results. Coefficients: -61.0146279461279Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_005727.]: Samplename: 87.5 Root: 85.767 --> Root in between the borders! Added to results. Coefficients: -76.0699612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_005727.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.743 --> '100 < root < 110' --> substitute 100 [20250519_005727.]: Solving cubic regression for CpG#6 Coefficients: -0.196072390572403Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_005727.]: Samplename: 0 Root: 0.349 --> Root in between the borders! Added to results. Coefficients: -6.73873905723907Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_005727.]: Samplename: 12.5 Root: 11.718 --> Root in between the borders! Added to results. Coefficients: -15.8880723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_005727.]: Samplename: 25 Root: 26.396 --> Root in between the borders! Added to results. Coefficients: -22.0000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_005727.]: Samplename: 37.5 Root: 35.301 --> Root in between the borders! Added to results. Coefficients: -33.4445723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_005727.]: Samplename: 50 Root: 50.134 --> Root in between the borders! Added to results. Coefficients: -46.9000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_005727.]: Samplename: 62.5 Root: 64.993 --> Root in between the borders! Added to results. Coefficients: -55.8320723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_005727.]: Samplename: 75 Root: 73.639 --> Root in between the borders! Added to results. Coefficients: -71.5454057239057Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_005727.]: Samplename: 87.5 Root: 87.043 --> Root in between the borders! Added to results. Coefficients: -89.6560723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_005727.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.329 --> '100 < root < 110' --> substitute 100 [20250519_005727.]: Solving cubic regression for CpG#7 Coefficients: -1.21495454545456Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_005727.]: Samplename: 0 Root: 2.13 --> Root in between the borders! Added to results. Coefficients: -5.39562121212123Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_005727.]: Samplename: 12.5 Root: 9.973 --> Root in between the borders! Added to results. Coefficients: -11.2649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_005727.]: Samplename: 25 Root: 22.206 --> Root in between the borders! Added to results. Coefficients: -17.4509545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_005727.]: Samplename: 37.5 Root: 35.814 --> Root in between the borders! Added to results. Coefficients: -26.0314545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_005727.]: Samplename: 50 Root: 53.28 --> Root in between the borders! Added to results. Coefficients: -33.9649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_005727.]: Samplename: 62.5 Root: 66.598 --> Root in between the borders! Added to results. Coefficients: -41.1689545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_005727.]: Samplename: 75 Root: 76.575 --> Root in between the borders! Added to results. Coefficients: -44.1356212121212Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_005727.]: Samplename: 87.5 Root: 80.219 --> Root in between the borders! Added to results. Coefficients: -67.2229545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_005727.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.506 --> '100 < root < 110' --> substitute 100 [20250519_005727.]: Solving cubic regression for CpG#8 Coefficients: -1.09618518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_005727.]: Samplename: 0 Root: 2.016 --> Root in between the borders! Added to results. Coefficients: -5.44685185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_005727.]: Samplename: 12.5 Root: 9.458 --> Root in between the borders! Added to results. Coefficients: -16.9301851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_005727.]: Samplename: 25 Root: 26.35 --> Root in between the borders! Added to results. Coefficients: -23.3501851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_005727.]: Samplename: 37.5 Root: 34.728 --> Root in between the borders! Added to results. Coefficients: -37.6251851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_005727.]: Samplename: 50 Root: 51.781 --> Root in between the borders! Added to results. Coefficients: -48.8261851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_005727.]: Samplename: 62.5 Root: 64.192 --> Root in between the borders! Added to results. Coefficients: -61.6676851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_005727.]: Samplename: 75 Root: 77.804 --> Root in between the borders! Added to results. Coefficients: -64.5101851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_005727.]: Samplename: 87.5 Root: 80.758 --> Root in between the borders! Added to results. Coefficients: -86.0601851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_005727.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.834 --> '100 < root < 110' --> substitute 100 [20250519_005727.]: Solving cubic regression for CpG#9 Coefficients: -0.989865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_005727.]: Samplename: 0 Root: 1.475 --> Root in between the borders! Added to results. Coefficients: -6.39586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_005727.]: Samplename: 12.5 Root: 10.12 --> Root in between the borders! Added to results. Coefficients: -14.7058653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_005727.]: Samplename: 25 Root: 24.844 --> Root in between the borders! Added to results. Coefficients: -20.6238653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_005727.]: Samplename: 37.5 Root: 35.327 --> Root in between the borders! Added to results. Coefficients: -31.3958653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_005727.]: Samplename: 50 Root: 51.855 --> Root in between the borders! Added to results. Coefficients: -42.6858653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_005727.]: Samplename: 62.5 Root: 65.265 --> Root in between the borders! Added to results. Coefficients: -52.9033653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_005727.]: Samplename: 75 Root: 74.915 --> Root in between the borders! Added to results. Coefficients: -65.492531986532Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_005727.]: Samplename: 87.5 Root: 84.67 --> Root in between the borders! Added to results. Coefficients: -92.9898653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_005727.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.082 --> '100 < root < 110' --> substitute 100 [20250519_005727.]: Solving cubic regression for row_means Coefficients: -0.771215488215478Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 [20250519_005727.]: Samplename: 0 Root: 1.287 --> Root in between the borders! Added to results. Coefficients: -6.47247474747474Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 [20250519_005727.]: Samplename: 12.5 Root: 10.847 --> Root in between the borders! Added to results. Coefficients: -14.8972154882155Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 [20250519_005727.]: Samplename: 25 Root: 24.737 --> Root in between the borders! Added to results. Coefficients: -22.1492154882155Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 [20250519_005727.]: Samplename: 37.5 Root: 36.02 --> Root in between the borders! Added to results. Coefficients: -32.5259932659933Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 [20250519_005727.]: Samplename: 50 Root: 50.639 --> Root in between the borders! Added to results. Coefficients: -44.7218821548821Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 [20250519_005727.]: Samplename: 62.5 Root: 65.497 --> Root in between the borders! Added to results. Coefficients: -54.4032154882155Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 [20250519_005727.]: Samplename: 75 Root: 75.751 --> Root in between the borders! Added to results. Coefficients: -62.4313636363636Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 [20250519_005727.]: Samplename: 87.5 Root: 83.403 --> Root in between the borders! Added to results. Coefficients: -84.7343265993266Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 [20250519_005727.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.573 --> '100 < root < 110' --> substitute 100 [20250519_005727.]: ### Starting with regression calculations ### [20250519_005727.]: Entered 'regression_type1'-Function [20250519_005727.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) [20250519_005727.]: Logging df_agg: CpG#1 [20250519_005727.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005727.]: c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)[20250519_005727.]: c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)[20250519_005727.]: c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) [20250519_005727.]: Entered 'hyperbolic_regression'-Function [20250519_005727.]: 'hyperbolic_regression': minmax = FALSE [20250519_005727.]: Entered 'cubic_regression'-Function [20250519_005727.]: 'cubic_regression': minmax = FALSE [20250519_005727.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) [20250519_005727.]: Logging df_agg: CpG#2 [20250519_005727.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005727.]: c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)[20250519_005727.]: c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)[20250519_005727.]: c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) [20250519_005727.]: Entered 'hyperbolic_regression'-Function [20250519_005727.]: 'hyperbolic_regression': minmax = FALSE [20250519_005727.]: Entered 'cubic_regression'-Function [20250519_005727.]: 'cubic_regression': minmax = FALSE [20250519_005727.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) [20250519_005727.]: Logging df_agg: CpG#3 [20250519_005727.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005727.]: c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)[20250519_005727.]: c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)[20250519_005727.]: c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) [20250519_005727.]: Entered 'hyperbolic_regression'-Function [20250519_005727.]: 'hyperbolic_regression': minmax = FALSE [20250519_005727.]: Entered 'cubic_regression'-Function [20250519_005727.]: 'cubic_regression': minmax = FALSE [20250519_005727.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) [20250519_005727.]: Logging df_agg: CpG#4 [20250519_005727.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005727.]: c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)[20250519_005727.]: c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)[20250519_005727.]: c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) [20250519_005727.]: Entered 'hyperbolic_regression'-Function [20250519_005727.]: 'hyperbolic_regression': minmax = FALSE [20250519_005727.]: Entered 'cubic_regression'-Function [20250519_005727.]: 'cubic_regression': minmax = FALSE [20250519_005727.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) [20250519_005727.]: Logging df_agg: CpG#5 [20250519_005727.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005727.]: c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)[20250519_005727.]: c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)[20250519_005727.]: c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) [20250519_005727.]: Entered 'hyperbolic_regression'-Function [20250519_005727.]: 'hyperbolic_regression': minmax = FALSE [20250519_005727.]: Entered 'cubic_regression'-Function [20250519_005727.]: 'cubic_regression': minmax = FALSE [20250519_005727.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) [20250519_005727.]: Logging df_agg: CpG#6 [20250519_005727.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005727.]: c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)[20250519_005727.]: c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)[20250519_005727.]: c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) [20250519_005727.]: Entered 'hyperbolic_regression'-Function [20250519_005727.]: 'hyperbolic_regression': minmax = FALSE [20250519_005727.]: Entered 'cubic_regression'-Function [20250519_005727.]: 'cubic_regression': minmax = FALSE [20250519_005727.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) [20250519_005727.]: Logging df_agg: CpG#7 [20250519_005727.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005727.]: c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)[20250519_005727.]: c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)[20250519_005727.]: c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) [20250519_005727.]: Entered 'hyperbolic_regression'-Function [20250519_005727.]: 'hyperbolic_regression': minmax = FALSE [20250519_005727.]: Entered 'cubic_regression'-Function [20250519_005727.]: 'cubic_regression': minmax = FALSE [20250519_005727.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) [20250519_005727.]: Logging df_agg: CpG#8 [20250519_005727.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005727.]: c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)[20250519_005727.]: c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)[20250519_005727.]: c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) [20250519_005727.]: Entered 'hyperbolic_regression'-Function [20250519_005727.]: 'hyperbolic_regression': minmax = FALSE [20250519_005727.]: Entered 'cubic_regression'-Function [20250519_005727.]: 'cubic_regression': minmax = FALSE [20250519_005727.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) [20250519_005727.]: Logging df_agg: CpG#9 [20250519_005727.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005727.]: c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)[20250519_005727.]: c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)[20250519_005727.]: c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) [20250519_005727.]: Entered 'hyperbolic_regression'-Function [20250519_005727.]: 'hyperbolic_regression': minmax = FALSE [20250519_005727.]: Entered 'cubic_regression'-Function [20250519_005727.]: 'cubic_regression': minmax = FALSE [20250519_005727.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) [20250519_005727.]: Logging df_agg: row_means [20250519_005727.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005727.]: c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)[20250519_005727.]: c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)[20250519_005727.]: c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) [20250519_005727.]: Entered 'hyperbolic_regression'-Function [20250519_005727.]: 'hyperbolic_regression': minmax = FALSE [20250519_005727.]: Entered 'cubic_regression'-Function [20250519_005727.]: 'cubic_regression': minmax = FALSE [20250519_005728.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Test_error_CpG1_corrected_h.png [20250519_005730.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Test_error_CpG2_corrected_h.png [20250519_005731.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Test_error_CpG3_corrected_h.png [20250519_005733.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Test_error_CpG4_corrected_h.png [20250519_005735.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Test_error_CpG5_corrected_h.png [20250519_005737.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Test_error_CpG6_corrected_h.png [20250519_005738.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Test_error_CpG7_corrected_h.png [20250519_005740.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Test_error_CpG8_corrected_h.png [20250519_005742.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Test_error_CpG9_corrected_h.png [20250519_005744.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Test_error_rowmeans_corrected_h.png [20250519_005746.]: on_start: using future::plan("sequential") [20250519_005747.]: on_start: using future::plan("sequential") [ FAIL 4 | WARN 209 | SKIP 12 | PASS 42 ] == Skipped tests (12) ========================================================== * On CRAN (12): 'test-algorithm_minmax_FALSE.R:113:5', 'test-algorithm_minmax_FALSE_re.R:173:5', 'test-algorithm_minmax_TRUE.R:114:5', 'test-algorithm_minmax_TRUE_re.R:175:5', 'test-clean_dt.R:19:5', 'test-clean_dt.R:61:5', 'test-create_aggregated.R:21:5', 'test-cubic.R:31:5', 'test-cubic.R:60:5', 'test-hyperbolic.R:32:5', 'test-hyperbolic.R:61:5', 'test-lints.R:12:5' == Failed tests ================================================================ -- Failure ('test-algorithm_minmax_FALSE.R:71:5'): algorithm test, type 1, minmax = FALSE -- regression_results$result_list (`actual`) not equal to regression_results_fast$result_list (`expected`). `actual$CpG#1$Coef_hyper$a`: -108.5680 `expected$CpG#1$Coef_hyper$a`: -108.5682 `actual$CpG#1$Coef_hyper$b`: -937.719 `expected$CpG#1$Coef_hyper$b`: -937.718 `actual$CpG#1$Coef_hyper$d`: -232.0571 `expected$CpG#1$Coef_hyper$d`: -232.0574 `actual$CpG#1$Coef_hyper$b1`: 0.5690716 `expected$CpG#1$Coef_hyper$b1`: 0.5690721 `actual$CpG#1$Coef_hyper$s`: 4.075791 `expected$CpG#1$Coef_hyper$s`: 4.075782 `actual$CpG#2$Coef_hyper$a`: -141.862264 `expected$CpG#2$Coef_hyper$a`: -141.862268 `actual$CpG#3$Coef_hyper$a`: -170.4245 `expected$CpG#3$Coef_hyper$a`: -170.4243 `actual$CpG#3$Coef_hyper$b`: -1477.4686 `expected$CpG#3$Coef_hyper$b`: -1477.4687 `actual$CpG#3$Coef_hyper$d`: -327.1902 `expected$CpG#3$Coef_hyper$d`: -327.1899 `actual$CpG#3$Coef_hyper$b1`: 0.6943673 `expected$CpG#3$Coef_hyper$b1`: 0.6943671 `actual$CpG#3$Coef_hyper$s`: 4.540975 `expected$CpG#3$Coef_hyper$s`: 4.540980 `actual$CpG#4$Coef_hyper$a`: -162.1698 `expected$CpG#4$Coef_hyper$a`: -162.1699 `actual$CpG#4$Coef_hyper$b`: -2115.8779 `expected$CpG#4$Coef_hyper$b`: -2115.8781 `actual$CpG#4$Coef_hyper$d`: -299.6623 `expected$CpG#4$Coef_hyper$d`: -299.6625 `actual$CpG#4$Coef_hyper$b1`: 0.6662910 `expected$CpG#4$Coef_hyper$b1`: 0.6662912 `actual$CpG#4$Coef_hyper$s`: 7.075768 `expected$CpG#4$Coef_hyper$s`: 7.075764 `actual$CpG#5$Coef_hyper$a`: -151.44007 `expected$CpG#5$Coef_hyper$a`: -151.44009 `actual$CpG#5$Coef_hyper$b`: -1383.32739 `expected$CpG#5$Coef_hyper$b`: -1383.32735 `actual$CpG#5$Coef_hyper$d`: -304.20025 `expected$CpG#5$Coef_hyper$d`: -304.20028 `actual$CpG#5$Coef_hyper$b1`: 0.67126918 `expected$CpG#5$Coef_hyper$b1`: 0.67126921 `actual$CpG#5$Coef_hyper$s`: 4.5750679 `expected$CpG#5$Coef_hyper$s`: 4.5750673 `actual$CpG#6$Coef_hyper$a`: -125.016146 `expected$CpG#6$Coef_hyper$a`: -125.016150 `actual$CpG#6$Coef_hyper$b`: -1550.59308 `expected$CpG#6$Coef_hyper$b`: -1550.59305 `actual$CpG#6$Coef_hyper$d`: -247.279679 `expected$CpG#6$Coef_hyper$d`: -247.279684 `actual$CpG#6$Coef_hyper$s`: 6.2900686 `expected$CpG#6$Coef_hyper$s`: 6.2900684 `actual$CpG#7$Coef_hyper$a`: -75.2725 `expected$CpG#7$Coef_hyper$a`: -75.2727 `actual$CpG#7$Coef_hyper$b`: -772.8442 `expected$CpG#7$Coef_hyper$b`: -772.8433 `actual$CpG#7$Coef_hyper$d`: -226.6731 `expected$CpG#7$Coef_hyper$d`: -226.6734 `actual$CpG#7$Coef_hyper$b1`: 0.5588361 `expected$CpG#7$Coef_hyper$b1`: 0.5588367 `actual$CpG#7$Coef_hyper$s`: 3.474320 `expected$CpG#7$Coef_hyper$s`: 3.474311 `actual$CpG#8$Coef_hyper$a`: -278.0173 `expected$CpG#8$Coef_hyper$a`: -278.0172 `actual$CpG#8$Coef_hyper$b`: -3872.7362 `expected$CpG#8$Coef_hyper$b`: -3872.7353 `actual$CpG#8$Coef_hyper$d`: -438.8943 `expected$CpG#8$Coef_hyper$d`: -438.8941 `actual$CpG#8$Coef_hyper$b1`: 0.77215469 `expected$CpG#8$Coef_hyper$b1`: 0.77215462 `actual$CpG#8$Coef_hyper$s`: 8.831458 `expected$CpG#8$Coef_hyper$s`: 8.831459 `actual$CpG#9$Coef_hyper$a`: -70.67961 `expected$CpG#9$Coef_hyper$a`: -70.67963 `actual$CpG#9$Coef_hyper$b`: -843.9255 `expected$CpG#9$Coef_hyper$b`: -843.9253 `actual$CpG#9$Coef_hyper$d`: -184.48215 `expected$CpG#9$Coef_hyper$d`: -184.48218 `actual$CpG#9$Coef_hyper$b1`: 0.45794215 `expected$CpG#9$Coef_hyper$b1`: 0.45794222 `actual$CpG#9$Coef_hyper$s`: 4.615973 `expected$CpG#9$Coef_hyper$s`: 4.615971 `actual$row_means$Coef_hyper$a`: -127.67503 `expected$row_means$Coef_hyper$a`: -127.67508 `actual$row_means$Coef_hyper$b`: -1525.7576 `expected$row_means$Coef_hyper$b`: -1525.7575 `actual$row_means$Coef_hyper$d`: -263.00305 `expected$row_means$Coef_hyper$d`: -263.00310 `actual$row_means$Coef_hyper$b1`: 0.61977627 `expected$row_means$Coef_hyper$b1`: 0.61977635 `actual$row_means$Coef_hyper$s`: 5.824068 `expected$row_means$Coef_hyper$s`: 5.824067 -- Failure ('test-algorithm_minmax_FALSE.R:88:5'): algorithm test, type 1, minmax = FALSE -- regression_results$result_list (`actual`) not equal to regression_results_minpack$result_list (`expected`). `actual$CpG#1$SSE_hyper`: 77 `expected$CpG#1$SSE_hyper`: 228 `actual$CpG#1$Coef_hyper$a`: -109 `expected$CpG#1$Coef_hyper$a`: 49589952 `actual$CpG#1$Coef_hyper$b`: -938 `expected$CpG#1$Coef_hyper$b`: -123249135 `actual$CpG#1$Coef_hyper$d`: -232 `expected$CpG#1$Coef_hyper$d`: 59280789 `actual$CpG#1$Coef_hyper$R2`: 0.99 `expected$CpG#1$Coef_hyper$R2`: 0.97 `actual$CpG#1$Coef_hyper$b1`: 0.6 `expected$CpG#1$Coef_hyper$b1`: 1.0 `actual$CpG#1$Coef_hyper$s`: 4 `expected$CpG#1$Coef_hyper$s`: 2 `actual$CpG#2$SSE_hyper`: 46 `expected$CpG#2$SSE_hyper`: 175 `actual$CpG#2$Coef_hyper$a`: -142 `expected$CpG#2$Coef_hyper$a`: 42299444 `actual$CpG#2$Coef_hyper$b`: -2393 `expected$CpG#2$Coef_hyper$b`: 167929575 `actual$CpG#2$Coef_hyper$d`: -268 `expected$CpG#2$Coef_hyper$d`: 47897274 `actual$CpG#2$Coef_hyper$R2`: 0.99 `expected$CpG#2$Coef_hyper$R2`: 0.98 `actual$CpG#2$Coef_hyper$b1`: 0.6 `expected$CpG#2$Coef_hyper$b1`: 1.0 `actual$CpG#2$Coef_hyper$s`: 9 `expected$CpG#2$Coef_hyper$s`: 4 `actual$CpG#3$SSE_hyper`: 67 `expected$CpG#3$SSE_hyper`: 123 `actual$CpG#3$Coef_hyper$a`: -170 `expected$CpG#3$Coef_hyper$a`: 31050253 `actual$CpG#3$Coef_hyper$b`: -1477 `expected$CpG#3$Coef_hyper$b`: 31004745 `actual$CpG#3$Coef_hyper$d`: -327 `expected$CpG#3$Coef_hyper$d`: 40665232 `actual$CpG#3$Coef_hyper$R2`: 0.99 `expected$CpG#3$Coef_hyper$R2`: 0.98 `actual$CpG#3$Coef_hyper$b1`: 0.7 `expected$CpG#3$Coef_hyper$b1`: 1.0 `actual$CpG#3$Coef_hyper$s`: 5 `expected$CpG#3$Coef_hyper$s`: 1 `actual$CpG#4$SSE_hyper`: 58 `expected$CpG#4$SSE_hyper`: 141 `actual$CpG#4$Coef_hyper$a`: -162 `expected$CpG#4$Coef_hyper$a`: 42779498 `actual$CpG#4$Coef_hyper$b`: -2116 `expected$CpG#4$Coef_hyper$b`: 128022258 `actual$CpG#4$Coef_hyper$d`: -300 `expected$CpG#4$Coef_hyper$d`: 50999440 `actual$CpG#4$Coef_hyper$R2`: 0.99 `expected$CpG#4$Coef_hyper$R2`: 0.98 `actual$CpG#4$Coef_hyper$b1`: 0.7 `expected$CpG#4$Coef_hyper$b1`: 1.0 `actual$CpG#4$Coef_hyper$s`: 7 `expected$CpG#4$Coef_hyper$s`: 3 `actual$CpG#5$SSE_hyper`: 8 `expected$CpG#5$SSE_hyper`: 78 `actual$CpG#5$Coef_hyper$a`: -151 `expected$CpG#5$Coef_hyper$a`: 41864400 `actual$CpG#5$Coef_hyper$b`: -1383 `expected$CpG#5$Coef_hyper$b`: 29447514 `actual$CpG#5$Coef_hyper$d`: -304 `expected$CpG#5$Coef_hyper$d`: 55382347 `actual$CpG#5$Coef_hyper$R2`: 1.00 `expected$CpG#5$Coef_hyper$R2`: 0.99 `actual$CpG#5$Coef_hyper$b1`: 0.7 `expected$CpG#5$Coef_hyper$b1`: 1.0 `actual$CpG#5$Coef_hyper$s`: 5 `expected$CpG#5$Coef_hyper$s`: 1 `actual$CpG#6$SSE_hyper`: 12 `expected$CpG#6$SSE_hyper`: 168 `actual$CpG#6$Coef_hyper$a`: -125 `expected$CpG#6$Coef_hyper$a`: 40932834 `actual$CpG#6$Coef_hyper$b`: -1551 `expected$CpG#6$Coef_hyper$b`: 17150081 `actual$CpG#6$Coef_hyper$d`: -247 `expected$CpG#6$Coef_hyper$d`: 46723472 `actual$CpG#6$Coef_hyper$R2`: 1.00 `expected$CpG#6$Coef_hyper$R2`: 0.98 `actual$CpG#6$Coef_hyper$b1`: 0.6 `expected$CpG#6$Coef_hyper$b1`: 1.0 `actual$CpG#6$Coef_hyper$s`: 6 `expected$CpG#6$Coef_hyper$s`: 0 `actual$CpG#7$SSE_hyper`: 72 `expected$CpG#7$SSE_hyper`: 155 `actual$CpG#7$Coef_hyper$a`: -75 `expected$CpG#7$Coef_hyper$a`: 29554653 `actual$CpG#7$Coef_hyper$b`: -773 `expected$CpG#7$Coef_hyper$b`: -57204506 `actual$CpG#7$Coef_hyper$d`: -227 `expected$CpG#7$Coef_hyper$d`: 48548426 `actual$CpG#7$Coef_hyper$R2`: 0.98 `expected$CpG#7$Coef_hyper$R2`: 0.96 `actual$CpG#7$Coef_hyper$b1`: 0.6 `expected$CpG#7$Coef_hyper$b1`: 1.0 `actual$CpG#7$Coef_hyper$s`: 3 `expected$CpG#7$Coef_hyper$s`: 1 `actual$CpG#8$SSE_hyper`: 75 `expected$CpG#8$SSE_hyper`: 112 `actual$CpG#8$Coef_hyper$a`: -278 `expected$CpG#8$Coef_hyper$a`: 38159683 `actual$CpG#8$Coef_hyper$b`: -3873 `expected$CpG#8$Coef_hyper$b`: 263871871 `actual$CpG#8$Coef_hyper$d`: -439 `expected$CpG#8$Coef_hyper$d`: 45284548 `actual$CpG#8$Coef_hyper$R2`: 0.989 `expected$CpG#8$Coef_hyper$R2`: 0.983 `actual$CpG#8$Coef_hyper$b1`: 0.8 `expected$CpG#8$Coef_hyper$b1`: 1.0 `actual$CpG#8$Coef_hyper$s`: 9 `expected$CpG#8$Coef_hyper$s`: 6 `actual$CpG#9$SSE_hyper`: 33 `expected$CpG#9$SSE_hyper`: 352 `actual$CpG#9$Coef_hyper$a`: -71 `expected$CpG#9$Coef_hyper$a`: 51672527 `actual$CpG#9$Coef_hyper$b`: -844 `expected$CpG#9$Coef_hyper$b`: -224404014 `actual$CpG#9$Coef_hyper$d`: -184 `expected$CpG#9$Coef_hyper$d`: 60201205 `actual$CpG#9$Coef_hyper$R2`: 1.00 `expected$CpG#9$Coef_hyper$R2`: 0.95 `actual$CpG#9$Coef_hyper$b1`: 0.5 `expected$CpG#9$Coef_hyper$b1`: 1.0 `actual$CpG#9$Coef_hyper$s`: 4.6 `expected$CpG#9$Coef_hyper$s`: 3.7 `actual$row_means$SSE_hyper`: 35 `expected$row_means$SSE_hyper`: 144 `actual$row_means$Coef_hyper$a`: -128 `expected$row_means$Coef_hyper$a`: 33244771 `actual$row_means$Coef_hyper$b`: -1526 `expected$row_means$Coef_hyper$b`: 29838275 `actual$row_means$Coef_hyper$d`: -263 `expected$row_means$Coef_hyper$d`: 41191067 `actual$row_means$Coef_hyper$R2`: 0.99 `expected$row_means$Coef_hyper$R2`: 0.98 `actual$row_means$Coef_hyper$b1`: 0.6 `expected$row_means$Coef_hyper$b1`: 1.0 `actual$row_means$Coef_hyper$s`: 6 `expected$row_means$Coef_hyper$s`: 1 -- Failure ('test-algorithm_minmax_TRUE.R:71:5'): algorithm test, type 1, minmax = TRUE -- regression_results$result_list (`actual`) not equal to regression_results_fast$result_list (`expected`). `actual$CpG#1$Coef_hyper$b`: 0.5364372 `expected$CpG#1$Coef_hyper$b`: 0.5364373 actual$CpG#1$Coef_cubic$a != expected$CpG#1$Coef_cubic$a but don't know how to show the difference actual$CpG#1$Coef_cubic$b != expected$CpG#1$Coef_cubic$b but don't know how to show the difference actual$CpG#2$Coef_cubic$a != expected$CpG#2$Coef_cubic$a but don't know how to show the difference actual$CpG#2$Coef_cubic$b != expected$CpG#2$Coef_cubic$b but don't know how to show the difference actual$CpG#3$Coef_cubic$a != expected$CpG#3$Coef_cubic$a but don't know how to show the difference `actual$CpG#3$Coef_cubic$b`: -0.00173040 `expected$CpG#3$Coef_cubic$b`: -0.00173041 `actual$CpG#4$Coef_hyper$b`: 0.6263293 `expected$CpG#4$Coef_hyper$b`: 0.6263302 actual$CpG#4$Coef_cubic$a != expected$CpG#4$Coef_cubic$a but don't know how to show the difference actual$CpG#4$Coef_cubic$b != expected$CpG#4$Coef_cubic$b but don't know how to show the difference actual$CpG#5$Coef_cubic$a != expected$CpG#5$Coef_cubic$a but don't know how to show the difference actual$CpG#5$Coef_cubic$b != expected$CpG#5$Coef_cubic$b but don't know how to show the difference actual$CpG#6$Coef_cubic$a != expected$CpG#6$Coef_cubic$a but don't know how to show the difference actual$CpG#6$Coef_cubic$b != expected$CpG#6$Coef_cubic$b but don't know how to show the difference `actual$CpG#7$Coef_hyper$b`: 0.50578129 `expected$CpG#7$Coef_hyper$b`: 0.50578130 actual$CpG#7$Coef_cubic$a != expected$CpG#7$Coef_cubic$a but don't know how to show the difference actual$CpG#7$Coef_cubic$b != expected$CpG#7$Coef_cubic$b but don't know how to show the difference `actual$CpG#8$Coef_hyper$b`: 0.68763945 `expected$CpG#8$Coef_hyper$b`: 0.68763952 actual$CpG#8$Coef_cubic$a != expected$CpG#8$Coef_cubic$a but don't know how to show the difference actual$CpG#8$Coef_cubic$b != expected$CpG#8$Coef_cubic$b but don't know how to show the difference `actual$CpG#9$Coef_hyper$b`: 0.44888828 `expected$CpG#9$Coef_hyper$b`: 0.44888829 actual$CpG#9$Coef_cubic$a != expected$CpG#9$Coef_cubic$a but don't know how to show the difference actual$CpG#9$Coef_cubic$b != expected$CpG#9$Coef_cubic$b but don't know how to show the difference actual$row_means$Coef_cubic$a != expected$row_means$Coef_cubic$a but don't know how to show the difference actual$row_means$Coef_cubic$b != expected$row_means$Coef_cubic$b but don't know how to show the difference -- Failure ('test-algorithm_minmax_TRUE.R:88:5'): algorithm test, type 1, minmax = TRUE -- regression_results$result_list (`actual`) not equal to regression_results_minpack$result_list (`expected`). `actual$CpG#1$Coef_hyper$b`: 0.5364372 `expected$CpG#1$Coef_hyper$b`: 0.5364373 actual$CpG#1$Coef_cubic$a != expected$CpG#1$Coef_cubic$a but don't know how to show the difference actual$CpG#1$Coef_cubic$b != expected$CpG#1$Coef_cubic$b but don't know how to show the difference actual$CpG#2$Coef_cubic$a != expected$CpG#2$Coef_cubic$a but don't know how to show the difference actual$CpG#2$Coef_cubic$b != expected$CpG#2$Coef_cubic$b but don't know how to show the difference `actual$CpG#3$Coef_hyper$b`: 0.6287998 `expected$CpG#3$Coef_hyper$b`: 0.6287994 actual$CpG#3$Coef_cubic$a != expected$CpG#3$Coef_cubic$a but don't know how to show the difference actual$CpG#3$Coef_cubic$b != expected$CpG#3$Coef_cubic$b but don't know how to show the difference `actual$CpG#4$Coef_hyper$b`: 0.6263293 `expected$CpG#4$Coef_hyper$b`: 0.6263302 `actual$CpG#4$Coef_cubic$a`: 0.00004340 `expected$CpG#4$Coef_cubic$a`: 0.00004341 actual$CpG#4$Coef_cubic$b != expected$CpG#4$Coef_cubic$b but don't know how to show the difference `actual$CpG#5$Coef_hyper$b`: 0.63653152 `expected$CpG#5$Coef_hyper$b`: 0.63653153 actual$CpG#5$Coef_cubic$a != expected$CpG#5$Coef_cubic$a but don't know how to show the difference actual$CpG#5$Coef_cubic$b != expected$CpG#5$Coef_cubic$b but don't know how to show the difference actual$CpG#6$Coef_cubic$a != expected$CpG#6$Coef_cubic$a but don't know how to show the difference actual$CpG#6$Coef_cubic$b != expected$CpG#6$Coef_cubic$b but don't know how to show the difference `actual$CpG#7$Coef_hyper$b`: 0.5057813 `expected$CpG#7$Coef_hyper$b`: 0.5057809 actual$CpG#7$Coef_cubic$a != expected$CpG#7$Coef_cubic$a but don't know how to show the difference `actual$CpG#7$Coef_cubic$b`: -0.00511940 `expected$CpG#7$Coef_cubic$b`: -0.00511941 `actual$CpG#8$Coef_hyper$b`: 0.68763945 `expected$CpG#8$Coef_hyper$b`: 0.68763952 actual$CpG#8$Coef_cubic$a != expected$CpG#8$Coef_cubic$a but don't know how to show the difference actual$CpG#8$Coef_cubic$b != expected$CpG#8$Coef_cubic$b but don't know how to show the difference `actual$CpG#9$Coef_hyper$b`: 0.44888828 `expected$CpG#9$Coef_hyper$b`: 0.44888824 actual$CpG#9$Coef_cubic$b != expected$CpG#9$Coef_cubic$b but don't know how to show the difference actual$row_means$Coef_cubic$a != expected$row_means$Coef_cubic$a but don't know how to show the difference `actual$row_means$Coef_cubic$b`: -0.00123898 `expected$row_means$Coef_cubic$b`: -0.00123897 [ FAIL 4 | WARN 209 | SKIP 12 | PASS 42 ] Error: Test failures Execution halted Error in deferred_run(env) : could not find function "deferred_run" Calls: * checking for unstated dependencies in vignettes ... OK * checking package vignettes ... OK * checking re-building of vignette outputs ... OK * checking PDF version of manual ... OK * DONE Status: 1 ERROR See '/tmp/th798/17369309/R-release/1149/rBiasCorrection.Rcheck/00check.log' for details. [1] "2025-05-19 00:57:58 MST" [1] "2025-05-19 00:57:58 MST" Installing package into '/tmp/th798/17369309/R-release/1149/library' (as 'lib' is unspecified) * installing *source* package 'data.table' ... ** this is package 'data.table' version '1.17.99' ** using staged installation zlib 1.2.13 is available ok * checking if R installation supports OpenMP without any extra hints... yes ** libs using C compiler: 'gcc (Spack GCC) 12.2.0' /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c assign.c -o assign.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c between.c -o between.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c bmerge.c -o bmerge.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c chmatch.c -o chmatch.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c cj.c -o cj.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c coalesce.c -o coalesce.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c dogroups.c -o dogroups.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c fastmean.c -o fastmean.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c fcast.c -o fcast.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c fifelse.c -o fifelse.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c fmelt.c -o fmelt.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c forder.c -o forder.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c frank.c -o frank.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c fread.c -o fread.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c freadR.c -o freadR.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c froll.c -o froll.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c frollR.c -o frollR.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c frolladaptive.c -o frolladaptive.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c fsort.c -o fsort.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c fwrite.c -o fwrite.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c fwriteR.c -o fwriteR.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c gsumm.c -o gsumm.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c idatetime.c -o idatetime.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c ijoin.c -o ijoin.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c init.c -o init.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c inrange.c -o inrange.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c nafill.c -o nafill.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c negate.c -o negate.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c nqrecreateindices.c -o nqrecreateindices.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c openmp-utils.c -o openmp-utils.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c programming.c -o programming.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c quickselect.c -o quickselect.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c rbindlist.c -o rbindlist.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c reorder.c -o reorder.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c shift.c -o shift.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c snprintf.c -o snprintf.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c subset.c -o subset.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c transpose.c -o transpose.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c types.c -o types.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c uniqlist.c -o uniqlist.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c utils.c -o utils.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c vecseq.c -o vecseq.o /packages/gcc/12.2.0-nnbserq/bin/gcc -I"/home/th798/R/R-release/include" -DNDEBUG -I/home/th798/.conda/envs/emacs1/include -I/home/th798/include -fopenmp -I/home/th798/.conda/envs/emacs1/include -fpic -I/packages/gcc/12.2.0-nnbserq/include -I/packages/zstd/1.5.2-cs5ansz/include -I/packages/zlib/1.2.13-po6bkge/include -I/packages/mpc/1.2.1-nnfoiwh/include -I/packages/mpfr/4.1.0-yfsqqka/include -I/packages/gmp/6.2.1-7ydtie6/include -c wrappers.c -o wrappers.o /packages/gcc/12.2.0-nnbserq/bin/gcc -shared -L/home/th798/.conda/envs/emacs1/lib -Wl,-rpath=/home/th798/.conda/envs/emacs1/lib -L/home/th798/lib -Wl,-rpath=/home/th798/lib -L/home/th798/lib64 -Wl,-rpath=/home/th798/lib64 -o data.table.so assign.o between.o bmerge.o chmatch.o cj.o coalesce.o dogroups.o fastmean.o fcast.o fifelse.o fmelt.o forder.o frank.o fread.o freadR.o froll.o frollR.o frolladaptive.o fsort.o fwrite.o fwriteR.o gsumm.o idatetime.o ijoin.o init.o inrange.o nafill.o negate.o nqrecreateindices.o openmp-utils.o programming.o quickselect.o rbindlist.o reorder.o shift.o snprintf.o subset.o transpose.o types.o uniqlist.o utils.o vecseq.o wrappers.o -fopenmp -L/home/th798/.conda/envs/emacs1/lib -lz PKG_CFLAGS = -fopenmp -I/home/th798/.conda/envs/emacs1/include PKG_LIBS = -fopenmp -L/home/th798/.conda/envs/emacs1/lib -lz if [ "data.table.so" != "data_table.so" ]; then mv data.table.so data_table.so; fi if [ "" != "Windows_NT" ] && [ `uname -s` = 'Darwin' ]; then install_name_tool -id data_table.so data_table.so; fi installing to /tmp/th798/17369309/R-release/1149/library/00LOCK-data.table/00new/data.table/libs ** R ** inst ** byte-compile and prepare package for lazy loading ** help *** installing help indices ** building package indices ** installing vignettes ** testing if installed package can be loaded from temporary location ** checking absolute paths in shared objects and dynamic libraries ** testing if installed package can be loaded from final location ** testing if installed package keeps a record of temporary installation path * DONE (data.table) [1] "2025-05-19 00:58:20 MST" * using log directory '/tmp/th798/17369309/R-release/1149/rBiasCorrection.Rcheck' * using R version 4.5.0 (2025-04-11) * using platform: x86_64-pc-linux-gnu * R was compiled by gcc (Spack GCC) 12.2.0 GNU Fortran (Spack GCC) 12.2.0 * running under: Red Hat Enterprise Linux 8.10 (Ootpa) * using session charset: ASCII * checking for file 'rBiasCorrection/DESCRIPTION' ... OK * this is package 'rBiasCorrection' version '0.3.5' * package encoding: UTF-8 * checking package namespace information ... OK * checking package dependencies ... OK * checking if this is a source package ... OK * checking if there is a namespace ... OK * checking for executable files ... OK * checking for hidden files and directories ... OK * checking for portable file names ... OK * checking for sufficient/correct file permissions ... OK * checking whether package 'rBiasCorrection' can be installed ... OK * checking installed package size ... OK * checking package directory ... OK * checking 'build' directory ... OK * checking DESCRIPTION meta-information ... OK * checking top-level files ... OK * checking for left-over files ... OK * checking index information ... OK * checking package subdirectories ... OK * checking code files for non-ASCII characters ... OK * checking R files for syntax errors ... OK * checking whether the package can be loaded ... OK * checking whether the package can be loaded with stated dependencies ... OK * checking whether the package can be unloaded cleanly ... OK * checking whether the namespace can be loaded with stated dependencies ... OK * checking whether the namespace can be unloaded cleanly ... OK * checking loading without being on the library search path ... OK * checking whether startup messages can be suppressed ... OK * checking dependencies in R code ... OK * checking S3 generic/method consistency ... OK * checking replacement functions ... OK * checking foreign function calls ... OK * checking R code for possible problems ... OK * checking Rd files ... OK * checking Rd metadata ... OK * checking Rd cross-references ... OK * checking for missing documentation entries ... OK * checking for code/documentation mismatches ... OK * checking Rd \usage sections ... OK * checking Rd contents ... OK * checking for unstated dependencies in examples ... OK * checking contents of 'data' directory ... OK * checking data for non-ASCII characters ... OK * checking LazyData ... OK * checking data for ASCII and uncompressed saves ... OK * checking installed files from 'inst/doc' ... OK * checking files in 'vignettes' ... OK * checking examples ... OK * checking for unstated dependencies in 'tests' ... OK * checking tests ... Running 'testthat.R' ERROR Running the tests in 'tests/testthat.R' failed. Complete output: > # https://github.com/Rdatatable/data.table/issues/5658 > Sys.setenv("OMP_THREAD_LIMIT" = 2) > Sys.setenv("Ncpu" = 2) > > library(testthat) > library(rBiasCorrection) > > local_edition(3) > > test_check("rBiasCorrection") [20250519_005923.]: Entered 'clean_dt'-Function [20250519_005923.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_005923.]: got experimental data [20250519_005923.]: Entered 'clean_dt'-Function [20250519_005923.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_005923.]: got calibration data [20250519_005923.]: ### Starting with regression calculations ### [20250519_005923.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_005923.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005923.]: Logging df_agg: CpG#1 [20250519_005923.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005923.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005923.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005923.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005923.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005923.]: Entered 'hyperbolic_regression'-Function [20250519_005923.]: 'hyperbolic_regression': minmax = FALSE [20250519_005924.]: Entered 'cubic_regression'-Function [20250519_005924.]: 'cubic_regression': minmax = FALSE [20250519_005924.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005924.]: Logging df_agg: CpG#2 [20250519_005924.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005924.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005924.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005924.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005924.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005924.]: Entered 'hyperbolic_regression'-Function [20250519_005924.]: 'hyperbolic_regression': minmax = FALSE [20250519_005924.]: Entered 'cubic_regression'-Function [20250519_005924.]: 'cubic_regression': minmax = FALSE [20250519_005924.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005924.]: Logging df_agg: CpG#3 [20250519_005924.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005924.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005924.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005924.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005924.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005924.]: Entered 'hyperbolic_regression'-Function [20250519_005924.]: 'hyperbolic_regression': minmax = FALSE [20250519_005924.]: Entered 'cubic_regression'-Function [20250519_005924.]: 'cubic_regression': minmax = FALSE [20250519_005924.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005924.]: Logging df_agg: CpG#4 [20250519_005924.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005924.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005924.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005924.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005924.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005924.]: Entered 'hyperbolic_regression'-Function [20250519_005924.]: 'hyperbolic_regression': minmax = FALSE [20250519_005924.]: Entered 'cubic_regression'-Function [20250519_005924.]: 'cubic_regression': minmax = FALSE [20250519_005924.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005924.]: Logging df_agg: CpG#5 [20250519_005924.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005924.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005924.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005924.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005924.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005924.]: Entered 'hyperbolic_regression'-Function [20250519_005924.]: 'hyperbolic_regression': minmax = FALSE [20250519_005925.]: Entered 'cubic_regression'-Function [20250519_005925.]: 'cubic_regression': minmax = FALSE [20250519_005925.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005925.]: Logging df_agg: CpG#6 [20250519_005925.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005925.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005925.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005925.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005925.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005925.]: Entered 'hyperbolic_regression'-Function [20250519_005925.]: 'hyperbolic_regression': minmax = FALSE [20250519_005925.]: Entered 'cubic_regression'-Function [20250519_005925.]: 'cubic_regression': minmax = FALSE [20250519_005925.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005925.]: Logging df_agg: CpG#7 [20250519_005925.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005925.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005925.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005925.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005925.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005925.]: Entered 'hyperbolic_regression'-Function [20250519_005925.]: 'hyperbolic_regression': minmax = FALSE [20250519_005925.]: Entered 'cubic_regression'-Function [20250519_005925.]: 'cubic_regression': minmax = FALSE [20250519_005925.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005925.]: Logging df_agg: CpG#8 [20250519_005925.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005925.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005925.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005925.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005925.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005925.]: Entered 'hyperbolic_regression'-Function [20250519_005925.]: 'hyperbolic_regression': minmax = FALSE [20250519_005925.]: Entered 'cubic_regression'-Function [20250519_005925.]: 'cubic_regression': minmax = FALSE [20250519_005925.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005925.]: Logging df_agg: CpG#9 [20250519_005925.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005925.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005925.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005925.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005925.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005925.]: Entered 'hyperbolic_regression'-Function [20250519_005925.]: 'hyperbolic_regression': minmax = FALSE [20250519_005926.]: Entered 'cubic_regression'-Function [20250519_005926.]: 'cubic_regression': minmax = FALSE [20250519_005926.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005926.]: Logging df_agg: row_means [20250519_005926.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005926.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005926.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_005926.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005926.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005926.]: Entered 'hyperbolic_regression'-Function [20250519_005926.]: 'hyperbolic_regression': minmax = FALSE [20250519_005926.]: Entered 'cubic_regression'-Function [20250519_005926.]: 'cubic_regression': minmax = FALSE [20250519_005927.]: ### Starting with regression calculations ### [20250519_005927.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_005927.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005927.]: Logging df_agg: CpG#1 [20250519_005927.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005927.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005927.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005927.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005927.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005927.]: Entered 'hyperbolic_regression'-Function [20250519_005927.]: 'hyperbolic_regression': minmax = FALSE [20250519_005927.]: Entered 'cubic_regression'-Function [20250519_005927.]: 'cubic_regression': minmax = FALSE [20250519_005927.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005927.]: Logging df_agg: CpG#2 [20250519_005927.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005927.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005927.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005927.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005927.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005927.]: Entered 'hyperbolic_regression'-Function [20250519_005927.]: 'hyperbolic_regression': minmax = FALSE [20250519_005927.]: Entered 'cubic_regression'-Function [20250519_005927.]: 'cubic_regression': minmax = FALSE [20250519_005927.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005927.]: Logging df_agg: CpG#3 [20250519_005927.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005927.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005927.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005927.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005927.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005927.]: Entered 'hyperbolic_regression'-Function [20250519_005927.]: 'hyperbolic_regression': minmax = FALSE [20250519_005927.]: Entered 'cubic_regression'-Function [20250519_005927.]: 'cubic_regression': minmax = FALSE [20250519_005927.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005927.]: Logging df_agg: CpG#4 [20250519_005927.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005927.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005927.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005927.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005927.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005927.]: Entered 'hyperbolic_regression'-Function [20250519_005927.]: 'hyperbolic_regression': minmax = FALSE [20250519_005928.]: Entered 'cubic_regression'-Function [20250519_005928.]: 'cubic_regression': minmax = FALSE [20250519_005928.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005928.]: Logging df_agg: CpG#5 [20250519_005928.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005928.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005928.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005928.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005928.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005928.]: Entered 'hyperbolic_regression'-Function [20250519_005928.]: 'hyperbolic_regression': minmax = FALSE [20250519_005928.]: Entered 'cubic_regression'-Function [20250519_005928.]: 'cubic_regression': minmax = FALSE [20250519_005928.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005928.]: Logging df_agg: CpG#6 [20250519_005928.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005928.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005928.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005928.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005928.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005928.]: Entered 'hyperbolic_regression'-Function [20250519_005928.]: 'hyperbolic_regression': minmax = FALSE [20250519_005928.]: Entered 'cubic_regression'-Function [20250519_005928.]: 'cubic_regression': minmax = FALSE [20250519_005928.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005928.]: Logging df_agg: CpG#7 [20250519_005928.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005928.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005928.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005928.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005928.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005928.]: Entered 'hyperbolic_regression'-Function [20250519_005928.]: 'hyperbolic_regression': minmax = FALSE [20250519_005928.]: Entered 'cubic_regression'-Function [20250519_005928.]: 'cubic_regression': minmax = FALSE [20250519_005928.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005928.]: Logging df_agg: CpG#8 [20250519_005928.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005928.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005928.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005928.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005928.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005928.]: Entered 'hyperbolic_regression'-Function [20250519_005928.]: 'hyperbolic_regression': minmax = FALSE [20250519_005929.]: Entered 'cubic_regression'-Function [20250519_005929.]: 'cubic_regression': minmax = FALSE [20250519_005929.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005929.]: Logging df_agg: CpG#9 [20250519_005929.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005929.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005929.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005929.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005929.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005929.]: Entered 'hyperbolic_regression'-Function [20250519_005929.]: 'hyperbolic_regression': minmax = FALSE [20250519_005929.]: Entered 'cubic_regression'-Function [20250519_005929.]: 'cubic_regression': minmax = FALSE [20250519_005929.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005929.]: Logging df_agg: row_means [20250519_005929.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005929.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005929.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_005929.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005929.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005929.]: Entered 'hyperbolic_regression'-Function [20250519_005929.]: 'hyperbolic_regression': minmax = FALSE [20250519_005929.]: Entered 'cubic_regression'-Function [20250519_005929.]: 'cubic_regression': minmax = FALSE [20250519_005931.]: ### Starting with regression calculations ### [20250519_005931.]: Entered 'regression_type1'-Function [20250519_005931.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005931.]: Logging df_agg: CpG#1 [20250519_005931.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005931.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005931.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005931.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005931.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005931.]: Entered 'hyperbolic_regression'-Function [20250519_005931.]: 'hyperbolic_regression': minmax = FALSE [20250519_005931.]: Entered 'cubic_regression'-Function [20250519_005931.]: 'cubic_regression': minmax = FALSE [20250519_005931.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005931.]: Logging df_agg: CpG#2 [20250519_005931.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005931.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005931.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005931.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005931.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005931.]: Entered 'hyperbolic_regression'-Function [20250519_005931.]: 'hyperbolic_regression': minmax = FALSE [20250519_005931.]: Entered 'cubic_regression'-Function [20250519_005931.]: 'cubic_regression': minmax = FALSE [20250519_005931.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005931.]: Logging df_agg: CpG#3 [20250519_005931.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005931.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005931.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005931.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005931.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005931.]: Entered 'hyperbolic_regression'-Function [20250519_005931.]: 'hyperbolic_regression': minmax = FALSE [20250519_005931.]: Entered 'cubic_regression'-Function [20250519_005931.]: 'cubic_regression': minmax = FALSE [20250519_005931.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005931.]: Logging df_agg: CpG#4 [20250519_005931.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005931.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005931.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005931.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005931.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005931.]: Entered 'hyperbolic_regression'-Function [20250519_005931.]: 'hyperbolic_regression': minmax = FALSE [20250519_005931.]: Entered 'cubic_regression'-Function [20250519_005931.]: 'cubic_regression': minmax = FALSE [20250519_005931.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005931.]: Logging df_agg: CpG#5 [20250519_005931.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005931.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005931.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005931.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005931.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005931.]: Entered 'hyperbolic_regression'-Function [20250519_005931.]: 'hyperbolic_regression': minmax = FALSE [20250519_005931.]: Entered 'cubic_regression'-Function [20250519_005931.]: 'cubic_regression': minmax = FALSE [20250519_005931.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005931.]: Logging df_agg: CpG#6 [20250519_005931.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005931.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005931.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005931.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005931.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005931.]: Entered 'hyperbolic_regression'-Function [20250519_005931.]: 'hyperbolic_regression': minmax = FALSE [20250519_005931.]: Entered 'cubic_regression'-Function [20250519_005931.]: 'cubic_regression': minmax = FALSE [20250519_005931.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005931.]: Logging df_agg: CpG#7 [20250519_005931.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005931.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005931.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005931.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005931.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005931.]: Entered 'hyperbolic_regression'-Function [20250519_005931.]: 'hyperbolic_regression': minmax = FALSE [20250519_005931.]: Entered 'cubic_regression'-Function [20250519_005931.]: 'cubic_regression': minmax = FALSE [20250519_005931.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005931.]: Logging df_agg: CpG#8 [20250519_005931.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005931.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005931.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005931.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005931.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005931.]: Entered 'hyperbolic_regression'-Function [20250519_005931.]: 'hyperbolic_regression': minmax = FALSE [20250519_005931.]: Entered 'cubic_regression'-Function [20250519_005931.]: 'cubic_regression': minmax = FALSE [20250519_005931.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005931.]: Logging df_agg: CpG#9 [20250519_005931.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005931.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005931.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005931.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005931.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005931.]: Entered 'hyperbolic_regression'-Function [20250519_005931.]: 'hyperbolic_regression': minmax = FALSE [20250519_005931.]: Entered 'cubic_regression'-Function [20250519_005931.]: 'cubic_regression': minmax = FALSE [20250519_005931.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005931.]: Logging df_agg: row_means [20250519_005931.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005931.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005931.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_005931.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005931.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005931.]: Entered 'hyperbolic_regression'-Function [20250519_005931.]: 'hyperbolic_regression': minmax = FALSE [20250519_005931.]: Entered 'cubic_regression'-Function [20250519_005931.]: 'cubic_regression': minmax = FALSE [20250519_005933.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_005933.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005933.]: Logging df_agg: CpG#1 [20250519_005933.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005933.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005933.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005933.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005933.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005933.]: Entered 'hyperbolic_regression'-Function [20250519_005933.]: 'hyperbolic_regression': minmax = FALSE [20250519_005933.]: Entered 'cubic_regression'-Function [20250519_005933.]: 'cubic_regression': minmax = FALSE [20250519_005933.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005933.]: Logging df_agg: CpG#2 [20250519_005933.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005933.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005933.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005933.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005933.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005933.]: Entered 'hyperbolic_regression'-Function [20250519_005933.]: 'hyperbolic_regression': minmax = FALSE [20250519_005934.]: Entered 'cubic_regression'-Function [20250519_005934.]: 'cubic_regression': minmax = FALSE [20250519_005934.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005934.]: Logging df_agg: CpG#3 [20250519_005934.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005934.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005934.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005934.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005934.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005934.]: Entered 'hyperbolic_regression'-Function [20250519_005934.]: 'hyperbolic_regression': minmax = FALSE [20250519_005934.]: Entered 'cubic_regression'-Function [20250519_005934.]: 'cubic_regression': minmax = FALSE [20250519_005934.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005934.]: Logging df_agg: CpG#4 [20250519_005934.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005934.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005934.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005934.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005934.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005934.]: Entered 'hyperbolic_regression'-Function [20250519_005934.]: 'hyperbolic_regression': minmax = FALSE [20250519_005934.]: Entered 'cubic_regression'-Function [20250519_005934.]: 'cubic_regression': minmax = FALSE [20250519_005934.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005934.]: Logging df_agg: CpG#5 [20250519_005934.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005934.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005934.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005934.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005934.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005934.]: Entered 'hyperbolic_regression'-Function [20250519_005934.]: 'hyperbolic_regression': minmax = FALSE [20250519_005934.]: Entered 'cubic_regression'-Function [20250519_005934.]: 'cubic_regression': minmax = FALSE [20250519_005934.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005934.]: Logging df_agg: CpG#6 [20250519_005934.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005934.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005934.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005934.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005934.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005934.]: Entered 'hyperbolic_regression'-Function [20250519_005934.]: 'hyperbolic_regression': minmax = FALSE [20250519_005934.]: Entered 'cubic_regression'-Function [20250519_005934.]: 'cubic_regression': minmax = FALSE [20250519_005935.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005935.]: Logging df_agg: CpG#7 [20250519_005935.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005935.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005935.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005935.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005935.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005935.]: Entered 'hyperbolic_regression'-Function [20250519_005935.]: 'hyperbolic_regression': minmax = FALSE [20250519_005935.]: Entered 'cubic_regression'-Function [20250519_005935.]: 'cubic_regression': minmax = FALSE [20250519_005935.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005935.]: Logging df_agg: CpG#8 [20250519_005935.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005935.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005935.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005935.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005935.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005935.]: Entered 'hyperbolic_regression'-Function [20250519_005935.]: 'hyperbolic_regression': minmax = FALSE [20250519_005935.]: Entered 'cubic_regression'-Function [20250519_005935.]: 'cubic_regression': minmax = FALSE [20250519_005935.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005935.]: Logging df_agg: CpG#9 [20250519_005935.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005935.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005935.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005935.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005935.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005935.]: Entered 'hyperbolic_regression'-Function [20250519_005935.]: 'hyperbolic_regression': minmax = FALSE [20250519_005935.]: Entered 'cubic_regression'-Function [20250519_005935.]: 'cubic_regression': minmax = FALSE [20250519_005935.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005935.]: Logging df_agg: row_means [20250519_005935.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005935.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005935.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_005935.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005935.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005935.]: Entered 'hyperbolic_regression'-Function [20250519_005935.]: 'hyperbolic_regression': minmax = FALSE [20250519_005935.]: Entered 'cubic_regression'-Function [20250519_005935.]: 'cubic_regression': minmax = FALSE [20250519_005936.]: Entered 'clean_dt'-Function [20250519_005936.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_005936.]: got experimental data [20250519_005936.]: Entered 'clean_dt'-Function [20250519_005936.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_005936.]: got calibration data [20250519_005936.]: ### Starting with regression calculations ### [20250519_005936.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_005936.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005937.]: Logging df_agg: CpG#1 [20250519_005937.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005937.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005937.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005937.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005937.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005937.]: Entered 'hyperbolic_regression'-Function [20250519_005937.]: 'hyperbolic_regression': minmax = FALSE [20250519_005937.]: Entered 'cubic_regression'-Function [20250519_005937.]: 'cubic_regression': minmax = FALSE [20250519_005937.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005937.]: Logging df_agg: CpG#2 [20250519_005937.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005937.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005937.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005937.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005937.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005937.]: Entered 'hyperbolic_regression'-Function [20250519_005937.]: 'hyperbolic_regression': minmax = FALSE [20250519_005937.]: Entered 'cubic_regression'-Function [20250519_005937.]: 'cubic_regression': minmax = FALSE [20250519_005937.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005937.]: Logging df_agg: CpG#3 [20250519_005937.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005937.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005937.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005937.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005937.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005937.]: Entered 'hyperbolic_regression'-Function [20250519_005937.]: 'hyperbolic_regression': minmax = FALSE [20250519_005937.]: Entered 'cubic_regression'-Function [20250519_005937.]: 'cubic_regression': minmax = FALSE [20250519_005937.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005937.]: Logging df_agg: CpG#4 [20250519_005937.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005937.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005937.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005937.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005937.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005937.]: Entered 'hyperbolic_regression'-Function [20250519_005937.]: 'hyperbolic_regression': minmax = FALSE [20250519_005937.]: Entered 'cubic_regression'-Function [20250519_005937.]: 'cubic_regression': minmax = FALSE [20250519_005937.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005937.]: Logging df_agg: CpG#5 [20250519_005937.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005937.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005937.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005937.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005937.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005937.]: Entered 'hyperbolic_regression'-Function [20250519_005937.]: 'hyperbolic_regression': minmax = FALSE [20250519_005938.]: Entered 'cubic_regression'-Function [20250519_005938.]: 'cubic_regression': minmax = FALSE [20250519_005938.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005938.]: Logging df_agg: CpG#6 [20250519_005938.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005938.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005938.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005938.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005938.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005938.]: Entered 'hyperbolic_regression'-Function [20250519_005938.]: 'hyperbolic_regression': minmax = FALSE [20250519_005938.]: Entered 'cubic_regression'-Function [20250519_005938.]: 'cubic_regression': minmax = FALSE [20250519_005938.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005938.]: Logging df_agg: CpG#7 [20250519_005938.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005938.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005938.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005938.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005938.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005938.]: Entered 'hyperbolic_regression'-Function [20250519_005938.]: 'hyperbolic_regression': minmax = FALSE [20250519_005938.]: Entered 'cubic_regression'-Function [20250519_005938.]: 'cubic_regression': minmax = FALSE [20250519_005938.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005938.]: Logging df_agg: CpG#8 [20250519_005938.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005938.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005938.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005938.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005938.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005938.]: Entered 'hyperbolic_regression'-Function [20250519_005938.]: 'hyperbolic_regression': minmax = FALSE [20250519_005938.]: Entered 'cubic_regression'-Function [20250519_005938.]: 'cubic_regression': minmax = FALSE [20250519_005938.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005938.]: Logging df_agg: CpG#9 [20250519_005938.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005938.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005938.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005938.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005938.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005938.]: Entered 'hyperbolic_regression'-Function [20250519_005938.]: 'hyperbolic_regression': minmax = FALSE [20250519_005939.]: Entered 'cubic_regression'-Function [20250519_005939.]: 'cubic_regression': minmax = FALSE [20250519_005939.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005939.]: Logging df_agg: row_means [20250519_005939.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005939.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005939.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_005939.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005939.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005939.]: Entered 'hyperbolic_regression'-Function [20250519_005939.]: 'hyperbolic_regression': minmax = FALSE [20250519_005939.]: Entered 'cubic_regression'-Function [20250519_005939.]: 'cubic_regression': minmax = FALSE [20250519_005939.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_005939.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005939.]: Logging df_agg: CpG#1 [20250519_005939.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005939.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005939.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005939.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005939.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005939.]: Entered 'hyperbolic_regression'-Function [20250519_005939.]: 'hyperbolic_regression': minmax = FALSE [20250519_005940.]: Entered 'cubic_regression'-Function [20250519_005940.]: 'cubic_regression': minmax = FALSE [20250519_005940.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005940.]: Logging df_agg: CpG#2 [20250519_005940.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005940.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005940.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005940.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005940.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005940.]: Entered 'hyperbolic_regression'-Function [20250519_005940.]: 'hyperbolic_regression': minmax = FALSE [20250519_005940.]: Entered 'cubic_regression'-Function [20250519_005940.]: 'cubic_regression': minmax = FALSE [20250519_005940.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005940.]: Logging df_agg: CpG#3 [20250519_005940.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005940.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005940.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005940.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005940.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005940.]: Entered 'hyperbolic_regression'-Function [20250519_005940.]: 'hyperbolic_regression': minmax = FALSE [20250519_005940.]: Entered 'cubic_regression'-Function [20250519_005940.]: 'cubic_regression': minmax = FALSE [20250519_005940.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005940.]: Logging df_agg: CpG#4 [20250519_005940.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005940.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005940.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005940.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005940.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005940.]: Entered 'hyperbolic_regression'-Function [20250519_005940.]: 'hyperbolic_regression': minmax = FALSE [20250519_005940.]: Entered 'cubic_regression'-Function [20250519_005940.]: 'cubic_regression': minmax = FALSE [20250519_005940.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005940.]: Logging df_agg: CpG#5 [20250519_005940.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005940.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005940.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005940.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005940.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005940.]: Entered 'hyperbolic_regression'-Function [20250519_005940.]: 'hyperbolic_regression': minmax = FALSE [20250519_005941.]: Entered 'cubic_regression'-Function [20250519_005941.]: 'cubic_regression': minmax = FALSE [20250519_005941.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005941.]: Logging df_agg: CpG#6 [20250519_005941.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005941.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005941.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005941.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005941.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005941.]: Entered 'hyperbolic_regression'-Function [20250519_005941.]: 'hyperbolic_regression': minmax = FALSE [20250519_005941.]: Entered 'cubic_regression'-Function [20250519_005941.]: 'cubic_regression': minmax = FALSE [20250519_005941.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005941.]: Logging df_agg: CpG#7 [20250519_005941.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005941.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005941.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005941.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005941.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005941.]: Entered 'hyperbolic_regression'-Function [20250519_005941.]: 'hyperbolic_regression': minmax = FALSE [20250519_005941.]: Entered 'cubic_regression'-Function [20250519_005941.]: 'cubic_regression': minmax = FALSE [20250519_005941.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005941.]: Logging df_agg: CpG#8 [20250519_005941.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005941.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005941.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005941.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005941.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005941.]: Entered 'hyperbolic_regression'-Function [20250519_005941.]: 'hyperbolic_regression': minmax = FALSE [20250519_005941.]: Entered 'cubic_regression'-Function [20250519_005941.]: 'cubic_regression': minmax = FALSE [20250519_005941.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005941.]: Logging df_agg: CpG#9 [20250519_005941.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005941.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005941.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005941.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005941.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005941.]: Entered 'hyperbolic_regression'-Function [20250519_005941.]: 'hyperbolic_regression': minmax = FALSE [20250519_005942.]: Entered 'cubic_regression'-Function [20250519_005942.]: 'cubic_regression': minmax = FALSE [20250519_005942.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005942.]: Logging df_agg: row_means [20250519_005942.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005942.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005942.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_005942.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005942.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005942.]: Entered 'hyperbolic_regression'-Function [20250519_005942.]: 'hyperbolic_regression': minmax = FALSE [20250519_005942.]: Entered 'cubic_regression'-Function [20250519_005942.]: 'cubic_regression': minmax = FALSE [20250519_005942.]: Entered 'solving_equations'-Function [20250519_005942.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: -2.23222837469517 [20250519_005942.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.232 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.1698391693126 [20250519_005942.]: Samplename: 12.5 Root: 12.17 --> Root in between the borders! Added to results. Hyperbolic solved: 24.4781729791561 [20250519_005942.]: Samplename: 25 Root: 24.478 --> Root in between the borders! Added to results. Hyperbolic solved: 38.1730159074047 [20250519_005942.]: Samplename: 37.5 Root: 38.173 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3348987967717 [20250519_005942.]: Samplename: 50 Root: 52.335 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4582305966058 [20250519_005942.]: Samplename: 62.5 Root: 65.458 --> Root in between the borders! Added to results. Hyperbolic solved: 75.0090269722885 [20250519_005942.]: Samplename: 75 Root: 75.009 --> Root in between the borders! Added to results. Hyperbolic solved: 81.527135738891 [20250519_005942.]: Samplename: 87.5 Root: 81.527 --> Root in between the borders! Added to results. Hyperbolic solved: 102.400825350044 [20250519_005942.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.401 --> '100 < root < 110' --> substitute 100 [20250519_005942.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 1.13663087266051 [20250519_005942.]: Samplename: 0 Root: 1.137 --> Root in between the borders! Added to results. Hyperbolic solved: 11.4130277963576 [20250519_005942.]: Samplename: 12.5 Root: 11.413 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1741025868157 [20250519_005942.]: Samplename: 25 Root: 26.174 --> Root in between the borders! Added to results. Hyperbolic solved: 35.1051721736724 [20250519_005942.]: Samplename: 37.5 Root: 35.105 --> Root in between the borders! Added to results. Hyperbolic solved: 47.6856613240114 [20250519_005942.]: Samplename: 50 Root: 47.686 --> Root in between the borders! Added to results. Hyperbolic solved: 67.1442585354696 [20250519_005942.]: Samplename: 62.5 Root: 67.144 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7646957109439 [20250519_005942.]: Samplename: 75 Root: 75.765 --> Root in between the borders! Added to results. Hyperbolic solved: 84.4056634834815 [20250519_005942.]: Samplename: 87.5 Root: 84.406 --> Root in between the borders! Added to results. Hyperbolic solved: 100.94855337669 [20250519_005942.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.949 --> '100 < root < 110' --> substitute 100 [20250519_005942.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 0.512348257854635 [20250519_005942.]: Samplename: 0 Root: 0.512 --> Root in between the borders! Added to results. Hyperbolic solved: 10.7523476241811 [20250519_005942.]: Samplename: 12.5 Root: 10.752 --> Root in between the borders! Added to results. Hyperbolic solved: 25.5218062516799 [20250519_005942.]: Samplename: 25 Root: 25.522 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5269315729339 [20250519_005942.]: Samplename: 37.5 Root: 36.527 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7907738274884 [20250519_005942.]: Samplename: 50 Root: 50.791 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8684489988589 [20250519_005942.]: Samplename: 62.5 Root: 64.868 --> Root in between the borders! Added to results. Hyperbolic solved: 77.5522101073979 [20250519_005942.]: Samplename: 75 Root: 77.552 --> Root in between the borders! Added to results. Hyperbolic solved: 80.4372474691229 [20250519_005942.]: Samplename: 87.5 Root: 80.437 --> Root in between the borders! Added to results. Hyperbolic solved: 102.703772810881 [20250519_005942.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.704 --> '100 < root < 110' --> substitute 100 [20250519_005942.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: -0.519513573026794 [20250519_005942.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.52 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.4933760051342 [20250519_005943.]: Samplename: 12.5 Root: 12.493 --> Root in between the borders! Added to results. Hyperbolic solved: 24.2684789512555 [20250519_005943.]: Samplename: 25 Root: 24.268 --> Root in between the borders! Added to results. Hyperbolic solved: 38.0816229393195 [20250519_005943.]: Samplename: 37.5 Root: 38.082 --> Root in between the borders! Added to results. Hyperbolic solved: 48.5842089589187 [20250519_005943.]: Samplename: 50 Root: 48.584 --> Root in between the borders! Added to results. Hyperbolic solved: 67.6720983549562 [20250519_005943.]: Samplename: 62.5 Root: 67.672 --> Root in between the borders! Added to results. Hyperbolic solved: 74.1547759694059 [20250519_005943.]: Samplename: 75 Root: 74.155 --> Root in between the borders! Added to results. Hyperbolic solved: 82.8820147905386 [20250519_005943.]: Samplename: 87.5 Root: 82.882 --> Root in between the borders! Added to results. Hyperbolic solved: 102.078935346876 [20250519_005943.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.079 --> '100 < root < 110' --> substitute 100 [20250519_005943.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 2.41558040143479 [20250519_005943.]: Samplename: 0 Root: 2.416 --> Root in between the borders! Added to results. Hyperbolic solved: 10.1649584830834 [20250519_005943.]: Samplename: 12.5 Root: 10.165 --> Root in between the borders! Added to results. Hyperbolic solved: 23.9830670020905 [20250519_005943.]: Samplename: 25 Root: 23.983 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2773406385708 [20250519_005943.]: Samplename: 37.5 Root: 37.277 --> Root in between the borders! Added to results. Hyperbolic solved: 50.8659103346102 [20250519_005943.]: Samplename: 50 Root: 50.866 --> Root in between the borders! Added to results. Hyperbolic solved: 62.4341926937382 [20250519_005943.]: Samplename: 62.5 Root: 62.434 --> Root in between the borders! Added to results. Hyperbolic solved: 76.3914832329149 [20250519_005943.]: Samplename: 75 Root: 76.391 --> Root in between the borders! Added to results. Hyperbolic solved: 86.1597399215782 [20250519_005943.]: Samplename: 87.5 Root: 86.16 --> Root in between the borders! Added to results. Hyperbolic solved: 100.267701841999 [20250519_005943.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.268 --> '100 < root < 110' --> substitute 100 [20250519_005943.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 0.138179963459196 [20250519_005943.]: Samplename: 0 Root: 0.138 --> Root in between the borders! Added to results. Hyperbolic solved: 11.8635989635202 [20250519_005943.]: Samplename: 12.5 Root: 11.864 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5108198460409 [20250519_005943.]: Samplename: 25 Root: 26.511 --> Root in between the borders! Added to results. Hyperbolic solved: 35.3206004021833 [20250519_005943.]: Samplename: 37.5 Root: 35.321 --> Root in between the borders! Added to results. Hyperbolic solved: 50.0571987473308 [20250519_005943.]: Samplename: 50 Root: 50.057 --> Root in between the borders! Added to results. Hyperbolic solved: 64.9604436607063 [20250519_005943.]: Samplename: 62.5 Root: 64.96 --> Root in between the borders! Added to results. Hyperbolic solved: 73.6690699266294 [20250519_005943.]: Samplename: 75 Root: 73.669 --> Root in between the borders! Added to results. Hyperbolic solved: 87.1267946879604 [20250519_005943.]: Samplename: 87.5 Root: 87.127 --> Root in between the borders! Added to results. Hyperbolic solved: 100.261842767865 [20250519_005943.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.262 --> '100 < root < 110' --> substitute 100 [20250519_005943.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: -1.37239249181669 [20250519_005943.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -1.372 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.199366215418 [20250519_005943.]: Samplename: 12.5 Root: 10.199 --> Root in between the borders! Added to results. Hyperbolic solved: 24.595295361327 [20250519_005943.]: Samplename: 25 Root: 24.595 --> Root in between the borders! Added to results. Hyperbolic solved: 37.8312095633391 [20250519_005943.]: Samplename: 37.5 Root: 37.831 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5590896582852 [20250519_005943.]: Samplename: 50 Root: 53.559 --> Root in between the borders! Added to results. Hyperbolic solved: 65.9367389282431 [20250519_005943.]: Samplename: 62.5 Root: 65.937 --> Root in between the borders! Added to results. Hyperbolic solved: 75.736370152841 [20250519_005943.]: Samplename: 75 Root: 75.736 --> Root in between the borders! Added to results. Hyperbolic solved: 79.433089359524 [20250519_005943.]: Samplename: 87.5 Root: 79.433 --> Root in between the borders! Added to results. Hyperbolic solved: 103.004516225662 [20250519_005943.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 103.005 --> '100 < root < 110' --> substitute 100 [20250519_005943.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 2.80067549526643 [20250519_005943.]: Samplename: 0 Root: 2.801 --> Root in between the borders! Added to results. Hyperbolic solved: 9.27534686003087 [20250519_005943.]: Samplename: 12.5 Root: 9.275 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4762624089952 [20250519_005943.]: Samplename: 25 Root: 25.476 --> Root in between the borders! Added to results. Hyperbolic solved: 34.0122098045585 [20250519_005943.]: Samplename: 37.5 Root: 34.012 --> Root in between the borders! Added to results. Hyperbolic solved: 51.784270967861 [20250519_005943.]: Samplename: 50 Root: 51.784 --> Root in between the borders! Added to results. Hyperbolic solved: 64.6732380258789 [20250519_005943.]: Samplename: 62.5 Root: 64.673 --> Root in between the borders! Added to results. Hyperbolic solved: 78.4327054483192 [20250519_005943.]: Samplename: 75 Root: 78.433 --> Root in between the borders! Added to results. Hyperbolic solved: 81.3427308951795 [20250519_005943.]: Samplename: 87.5 Root: 81.343 --> Root in between the borders! Added to results. Hyperbolic solved: 101.964413523995 [20250519_005943.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.964 --> '100 < root < 110' --> substitute 100 [20250519_005943.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: -2.13402763663736 [20250519_005943.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.134 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.5081558897243 [20250519_005943.]: Samplename: 12.5 Root: 10.508 --> Root in between the borders! Added to results. Hyperbolic solved: 26.9163121098627 [20250519_005943.]: Samplename: 25 Root: 26.916 --> Root in between the borders! Added to results. Hyperbolic solved: 36.8332915301825 [20250519_005943.]: Samplename: 37.5 Root: 36.833 --> Root in between the borders! Added to results. Hyperbolic solved: 52.00955 [20250519_005943.]: Samplename: 50 Root: 52.01 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8927781893359 [20250519_005943.]: Samplename: 62.5 Root: 64.893 --> Root in between the borders! Added to results. Hyperbolic solved: 74.566810799042 [20250519_005943.]: Samplename: 75 Root: 74.567 --> Root in between the borders! Added to results. Hyperbolic solved: 84.5291854856128 [20250519_005943.]: Samplename: 87.5 Root: 84.529 --> Root in between the borders! Added to results. Hyperbolic solved: 101.046823491232 [20250519_005943.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.047 --> '100 < root < 110' --> substitute 100 [20250519_005943.]: Solving hyperbolic regression for row_means Hyperbolic solved: 0.290936038655697 [20250519_005943.]: Samplename: 0 Root: 0.291 --> Root in between the borders! Added to results. Hyperbolic solved: 11.0412364555656 [20250519_005943.]: Samplename: 12.5 Root: 11.041 --> Root in between the borders! Added to results. Hyperbolic solved: 25.408146358228 [20250519_005943.]: Samplename: 25 Root: 25.408 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5243683648753 [20250519_005943.]: Samplename: 37.5 Root: 36.524 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7348788337891 [20250519_005943.]: Samplename: 50 Root: 50.735 --> Root in between the borders! Added to results. Hyperbolic solved: 65.3135169308955 [20250519_005943.]: Samplename: 62.5 Root: 65.314 --> Root in between the borders! Added to results. Hyperbolic solved: 75.5342663138126 [20250519_005943.]: Samplename: 75 Root: 75.534 --> Root in between the borders! Added to results. Hyperbolic solved: 83.2411177153911 [20250519_005943.]: Samplename: 87.5 Root: 83.241 --> Root in between the borders! Added to results. Hyperbolic solved: 101.666935931185 [20250519_005943.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.667 --> '100 < root < 110' --> substitute 100 [20250519_005943.]: ### Starting with regression calculations ### [20250519_005943.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_005943.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.1698391693126, 24.4781729791561, 38.1730159074047, 52.3348987967717, 65.4582305966058, 75.0090269722885, 81.527135738891, 100)c(0, 0.330160830687401, 0.521827020843901, 0.673015907404697, 2.3348987967717, 2.9582305966058, 0.00902697228849547, 5.972864261109, 0)c(NA, 2.64128664549921, 2.0873080833756, 1.79470908641252, 4.66979759354339, 4.73316895456928, 0.0120359630513273, 6.82613058412457, 0) [20250519_005943.]: Logging df_agg: CpG#1 [20250519_005943.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005943.]: c(0, 12.1698391693126, 24.4781729791561, 38.1730159074047, 52.3348987967717, 65.4582305966058, 75.0090269722885, 81.527135738891, 100)[20250519_005943.]: c(0, 0.330160830687401, 0.521827020843901, 0.673015907404697, 2.3348987967717, 2.9582305966058, 0.00902697228849547, 5.972864261109, 0)[20250519_005943.]: c(NA, 2.64128664549921, 2.0873080833756, 1.79470908641252, 4.66979759354339, 4.73316895456928, 0.0120359630513273, 6.82613058412457, 0) [20250519_005943.]: Entered 'hyperbolic_regression'-Function [20250519_005943.]: 'hyperbolic_regression': minmax = FALSE [20250519_005943.]: Entered 'cubic_regression'-Function [20250519_005943.]: 'cubic_regression': minmax = FALSE [20250519_005943.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.13663087266051, 11.4130277963576, 26.1741025868157, 35.1051721736724, 47.6856613240114, 67.1442585354696, 75.7646957109439, 84.4056634834815, 100)c(1.13663087266051, 1.0869722036424, 1.1741025868157, 2.3948278263276, 2.3143386759886, 4.6442585354696, 0.764695710943897, 3.0943365165185, 0)c(NA, 8.6957776291392, 4.6964103472628, 6.3862075368736, 4.6286773519772, 7.43081365675137, 1.01959428125853, 3.53638459030685, 0) [20250519_005943.]: Logging df_agg: CpG#2 [20250519_005943.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005943.]: c(1.13663087266051, 11.4130277963576, 26.1741025868157, 35.1051721736724, 47.6856613240114, 67.1442585354696, 75.7646957109439, 84.4056634834815, 100)[20250519_005943.]: c(1.13663087266051, 1.0869722036424, 1.1741025868157, 2.3948278263276, 2.3143386759886, 4.6442585354696, 0.764695710943897, 3.0943365165185, 0)[20250519_005943.]: c(NA, 8.6957776291392, 4.6964103472628, 6.3862075368736, 4.6286773519772, 7.43081365675137, 1.01959428125853, 3.53638459030685, 0) [20250519_005943.]: Entered 'hyperbolic_regression'-Function [20250519_005943.]: 'hyperbolic_regression': minmax = FALSE [20250519_005943.]: Entered 'cubic_regression'-Function [20250519_005943.]: 'cubic_regression': minmax = FALSE [20250519_005943.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.512348257854635, 10.7523476241811, 25.5218062516799, 36.5269315729339, 50.7907738274884, 64.8684489988589, 77.5522101073979, 80.4372474691229, 100)c(0.512348257854635, 1.7476523758189, 0.521806251679902, 0.973068427066103, 0.790773827488401, 2.3684489988589, 2.5522101073979, 7.06275253087711, 0)c(NA, 13.9812190065512, 2.08722500671961, 2.59484913884294, 1.5815476549768, 3.78951839817423, 3.40294680986386, 8.07171717814526, 0) [20250519_005943.]: Logging df_agg: CpG#3 [20250519_005943.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005943.]: c(0.512348257854635, 10.7523476241811, 25.5218062516799, 36.5269315729339, 50.7907738274884, 64.8684489988589, 77.5522101073979, 80.4372474691229, 100)[20250519_005943.]: c(0.512348257854635, 1.7476523758189, 0.521806251679902, 0.973068427066103, 0.790773827488401, 2.3684489988589, 2.5522101073979, 7.06275253087711, 0)[20250519_005943.]: c(NA, 13.9812190065512, 2.08722500671961, 2.59484913884294, 1.5815476549768, 3.78951839817423, 3.40294680986386, 8.07171717814526, 0) [20250519_005943.]: Entered 'hyperbolic_regression'-Function [20250519_005943.]: 'hyperbolic_regression': minmax = FALSE [20250519_005943.]: Entered 'cubic_regression'-Function [20250519_005943.]: 'cubic_regression': minmax = FALSE [20250519_005943.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.4933760051342, 24.2684789512555, 38.0816229393195, 48.5842089589187, 67.6720983549562, 74.1547759694059, 82.8820147905386, 100)c(0, 0.00662399486579979, 0.731521048744501, 0.581622939319502, 1.4157910410813, 5.1720983549562, 0.845224030594096, 4.61798520946139, 0)c(NA, 0.0529919589263983, 2.926084194978, 1.550994504852, 2.8315820821626, 8.27535736792993, 1.12696537412546, 5.27769738224159, 0) [20250519_005943.]: Logging df_agg: CpG#4 [20250519_005943.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005943.]: c(0, 12.4933760051342, 24.2684789512555, 38.0816229393195, 48.5842089589187, 67.6720983549562, 74.1547759694059, 82.8820147905386, 100)[20250519_005943.]: c(0, 0.00662399486579979, 0.731521048744501, 0.581622939319502, 1.4157910410813, 5.1720983549562, 0.845224030594096, 4.61798520946139, 0)[20250519_005943.]: c(NA, 0.0529919589263983, 2.926084194978, 1.550994504852, 2.8315820821626, 8.27535736792993, 1.12696537412546, 5.27769738224159, 0) [20250519_005943.]: Entered 'hyperbolic_regression'-Function [20250519_005944.]: 'hyperbolic_regression': minmax = FALSE [20250519_005944.]: Entered 'cubic_regression'-Function [20250519_005944.]: 'cubic_regression': minmax = FALSE [20250519_005944.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.41558040143479, 10.1649584830834, 23.9830670020905, 37.2773406385708, 50.8659103346102, 62.4341926937382, 76.3914832329149, 86.1597399215782, 100)c(2.41558040143479, 2.3350415169166, 1.0169329979095, 0.222659361429201, 0.865910334610199, 0.065807306261803, 1.39148323291489, 1.34026007842181, 0)c(NA, 18.6803321353328, 4.067731991638, 0.593758297144537, 1.7318206692204, 0.105291690018885, 1.85531097721986, 1.53172580391064, 0) [20250519_005944.]: Logging df_agg: CpG#5 [20250519_005944.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005944.]: c(2.41558040143479, 10.1649584830834, 23.9830670020905, 37.2773406385708, 50.8659103346102, 62.4341926937382, 76.3914832329149, 86.1597399215782, 100)[20250519_005944.]: c(2.41558040143479, 2.3350415169166, 1.0169329979095, 0.222659361429201, 0.865910334610199, 0.065807306261803, 1.39148323291489, 1.34026007842181, 0)[20250519_005944.]: c(NA, 18.6803321353328, 4.067731991638, 0.593758297144537, 1.7318206692204, 0.105291690018885, 1.85531097721986, 1.53172580391064, 0) [20250519_005944.]: Entered 'hyperbolic_regression'-Function [20250519_005944.]: 'hyperbolic_regression': minmax = FALSE [20250519_005944.]: Entered 'cubic_regression'-Function [20250519_005944.]: 'cubic_regression': minmax = FALSE [20250519_005944.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.138179963459196, 11.8635989635202, 26.5108198460409, 35.3206004021833, 50.0571987473308, 64.9604436607063, 73.6690699266294, 87.1267946879604, 100)c(0.138179963459196, 0.6364010364798, 1.5108198460409, 2.1793995978167, 0.0571987473308013, 2.4604436607063, 1.3309300733706, 0.373205312039602, 0)c(NA, 5.0912082918384, 6.0432793841636, 5.81173226084453, 0.114397494661603, 3.93670985713009, 1.77457343116079, 0.426520356616688, 0) [20250519_005944.]: Logging df_agg: CpG#6 [20250519_005944.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005944.]: c(0.138179963459196, 11.8635989635202, 26.5108198460409, 35.3206004021833, 50.0571987473308, 64.9604436607063, 73.6690699266294, 87.1267946879604, 100)[20250519_005944.]: c(0.138179963459196, 0.6364010364798, 1.5108198460409, 2.1793995978167, 0.0571987473308013, 2.4604436607063, 1.3309300733706, 0.373205312039602, 0)[20250519_005944.]: c(NA, 5.0912082918384, 6.0432793841636, 5.81173226084453, 0.114397494661603, 3.93670985713009, 1.77457343116079, 0.426520356616688, 0) [20250519_005944.]: Entered 'hyperbolic_regression'-Function [20250519_005944.]: 'hyperbolic_regression': minmax = FALSE [20250519_005944.]: Entered 'cubic_regression'-Function [20250519_005944.]: 'cubic_regression': minmax = FALSE [20250519_005944.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.199366215418, 24.595295361327, 37.8312095633391, 53.5590896582852, 65.9367389282431, 75.736370152841, 79.433089359524, 100)c(0, 2.300633784582, 0.404704638673, 0.331209563339101, 3.5590896582852, 3.4367389282431, 0.736370152841005, 8.066910640476, 0)c(NA, 18.405070276656, 1.618818554692, 0.883225502237603, 7.11817931657041, 5.49878228518896, 0.981826870454673, 9.21932644625828, 0) [20250519_005944.]: Logging df_agg: CpG#7 [20250519_005944.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005944.]: c(0, 10.199366215418, 24.595295361327, 37.8312095633391, 53.5590896582852, 65.9367389282431, 75.736370152841, 79.433089359524, 100)[20250519_005944.]: c(0, 2.300633784582, 0.404704638673, 0.331209563339101, 3.5590896582852, 3.4367389282431, 0.736370152841005, 8.066910640476, 0)[20250519_005944.]: c(NA, 18.405070276656, 1.618818554692, 0.883225502237603, 7.11817931657041, 5.49878228518896, 0.981826870454673, 9.21932644625828, 0) [20250519_005944.]: Entered 'hyperbolic_regression'-Function [20250519_005944.]: 'hyperbolic_regression': minmax = FALSE [20250519_005944.]: Entered 'cubic_regression'-Function [20250519_005944.]: 'cubic_regression': minmax = FALSE [20250519_005944.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.80067549526643, 9.27534686003087, 25.4762624089952, 34.0122098045585, 51.784270967861, 64.6732380258789, 78.4327054483192, 81.3427308951795, 100)c(2.80067549526643, 3.22465313996913, 0.476262408995201, 3.4877901954415, 1.784270967861, 2.1732380258789, 3.4327054483192, 6.1572691048205, 0)c(NA, 25.797225119753, 1.9050496359808, 9.30077385451066, 3.568541935722, 3.47718084140624, 4.57694059775893, 7.03687897693771, 0) [20250519_005944.]: Logging df_agg: CpG#8 [20250519_005944.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005944.]: c(2.80067549526643, 9.27534686003087, 25.4762624089952, 34.0122098045585, 51.784270967861, 64.6732380258789, 78.4327054483192, 81.3427308951795, 100)[20250519_005944.]: c(2.80067549526643, 3.22465313996913, 0.476262408995201, 3.4877901954415, 1.784270967861, 2.1732380258789, 3.4327054483192, 6.1572691048205, 0)[20250519_005944.]: c(NA, 25.797225119753, 1.9050496359808, 9.30077385451066, 3.568541935722, 3.47718084140624, 4.57694059775893, 7.03687897693771, 0) [20250519_005944.]: Entered 'hyperbolic_regression'-Function [20250519_005944.]: 'hyperbolic_regression': minmax = FALSE [20250519_005945.]: Entered 'cubic_regression'-Function [20250519_005945.]: 'cubic_regression': minmax = FALSE [20250519_005945.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.5081558897243, 26.9163121098627, 36.8332915301825, 52.00955, 64.8927781893359, 74.566810799042, 84.5291854856128, 100)c(0, 1.9918441102757, 1.9163121098627, 0.6667084698175, 2.00955, 2.3927781893359, 0.433189200957997, 2.9708145143872, 0)c(NA, 15.9347528822056, 7.66524843945081, 1.77788925284667, 4.01909999999999, 3.82844510293744, 0.57758560127733, 3.39521658787109, 0) [20250519_005945.]: Logging df_agg: CpG#9 [20250519_005945.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005945.]: c(0, 10.5081558897243, 26.9163121098627, 36.8332915301825, 52.00955, 64.8927781893359, 74.566810799042, 84.5291854856128, 100)[20250519_005945.]: c(0, 1.9918441102757, 1.9163121098627, 0.6667084698175, 2.00955, 2.3927781893359, 0.433189200957997, 2.9708145143872, 0)[20250519_005945.]: c(NA, 15.9347528822056, 7.66524843945081, 1.77788925284667, 4.01909999999999, 3.82844510293744, 0.57758560127733, 3.39521658787109, 0) [20250519_005945.]: Entered 'hyperbolic_regression'-Function [20250519_005945.]: 'hyperbolic_regression': minmax = FALSE [20250519_005945.]: Entered 'cubic_regression'-Function [20250519_005945.]: 'cubic_regression': minmax = FALSE [20250519_005945.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.290936038655697, 11.0412364555656, 25.408146358228, 36.5243683648753, 50.7348788337891, 65.3135169308955, 75.5342663138126, 83.2411177153911, 100)c(0.290936038655697, 1.4587635444344, 0.408146358227999, 0.975631635124699, 0.734878833789097, 2.8135169308955, 0.534266313812594, 4.2588822846089, 0)c(NA, 11.6701083554752, 1.63258543291199, 2.60168436033253, 1.46975766757819, 4.5016270894328, 0.712355085083459, 4.86729403955303, 0) [20250519_005945.]: Logging df_agg: row_means [20250519_005945.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005945.]: c(0.290936038655697, 11.0412364555656, 25.408146358228, 36.5243683648753, 50.7348788337891, 65.3135169308955, 75.5342663138126, 83.2411177153911, 100)[20250519_005945.]: c(0.290936038655697, 1.4587635444344, 0.408146358227999, 0.975631635124699, 0.734878833789097, 2.8135169308955, 0.534266313812594, 4.2588822846089, 0)[20250519_005945.]: c(NA, 11.6701083554752, 1.63258543291199, 2.60168436033253, 1.46975766757819, 4.5016270894328, 0.712355085083459, 4.86729403955303, 0) [20250519_005945.]: Entered 'hyperbolic_regression'-Function [20250519_005945.]: 'hyperbolic_regression': minmax = FALSE [20250519_005945.]: Entered 'cubic_regression'-Function [20250519_005945.]: 'cubic_regression': minmax = FALSE [20250519_005946.]: Entered 'solving_equations'-Function [20250519_005946.]: Solving cubic regression for CpG#1 Coefficients: -1.036Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20250519_005946.]: Samplename: 0 Root: 1.335 --> Root in between the borders! Added to results. Coefficients: -8.34133333333333Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20250519_005946.]: Samplename: 12.5 Root: 11.684 --> Root in between the borders! Added to results. Coefficients: -15.388Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20250519_005946.]: Samplename: 25 Root: 24.056 --> Root in between the borders! Added to results. Coefficients: -24.28Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20250519_005946.]: Samplename: 37.5 Root: 50.441 --> Root in between the borders! Added to results. Coefficients: -34.9005Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20250519_005946.]: Samplename: 50 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -46.354Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20250519_005946.]: Samplename: 62.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -55.893Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20250519_005946.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -63.098Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20250519_005946.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -91.046Coefficients: 0.784Coefficients: -0.006Coefficients: 0 [20250519_005946.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20250519_005946.]: Solving cubic regression for CpG#2 Coefficients: -0.283000000000001Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20250519_005946.]: Samplename: 0 Root: 0.548 --> Root in between the borders! Added to results. Coefficients: -6.33966666666667Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20250519_005946.]: Samplename: 12.5 Root: 11.535 --> Root in between the borders! Added to results. Coefficients: -15.939Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20250519_005946.]: Samplename: 25 Root: 26.774 --> Root in between the borders! Added to results. Coefficients: -22.337Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20250519_005946.]: Samplename: 37.5 Root: 35.875 --> Root in between the borders! Added to results. Coefficients: -32.228Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20250519_005946.]: Samplename: 50 Root: 48.74 --> Root in between the borders! Added to results. Coefficients: -49.963Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20250519_005946.]: Samplename: 62.5 Root: 69.156 --> Root in between the borders! Added to results. Coefficients: -58.9655Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20250519_005946.]: Samplename: 75 Root: 78.552 --> Root in between the borders! Added to results. Coefficients: -68.8363333333333Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20250519_005946.]: Samplename: 87.5 Root: 88.272 --> Root in between the borders! Added to results. Coefficients: -90.577Coefficients: 0.515Coefficients: 0.003Coefficients: 0 [20250519_005946.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 107.97 --> '100 < root < 110' --> substitute 100 [20250519_005946.]: Solving cubic regression for CpG#3 Coefficients: -0.903Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20250519_005946.]: Samplename: 0 Root: 1.441 --> Root in between the borders! Added to results. Coefficients: -6.573Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20250519_005946.]: Samplename: 12.5 Root: 10.647 --> Root in between the borders! Added to results. Coefficients: -15.429Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20250519_005946.]: Samplename: 25 Root: 25.613 --> Root in between the borders! Added to results. Coefficients: -22.613Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20250519_005946.]: Samplename: 37.5 Root: 38.35 --> Root in between the borders! Added to results. Coefficients: -32.7755Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20250519_005946.]: Samplename: 50 Root: 57.445 --> Root in between the borders! Added to results. Coefficients: -43.889Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20250519_005946.]: Samplename: 62.5 Root: 80.105 --> Root in between the borders! Added to results. Coefficients: -54.9755Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20250519_005946.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 105.145 --> '100 < root < 110' --> substitute 100 Coefficients: -57.6563333333333Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20250519_005946.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -80.665Coefficients: 0.628Coefficients: -0.001Coefficients: 0 [20250519_005946.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20250519_005946.]: Solving cubic regression for CpG#4 Coefficients: -0.597Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20250519_005946.]: Samplename: 0 Root: 0.859 --> Root in between the borders! Added to results. Coefficients: -8.25233333333333Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20250519_005946.]: Samplename: 12.5 Root: 12.272 --> Root in between the borders! Added to results. Coefficients: -15.803Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20250519_005946.]: Samplename: 25 Root: 24.378 --> Root in between the borders! Added to results. Coefficients: -25.527Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20250519_005946.]: Samplename: 37.5 Root: 41.587 --> Root in between the borders! Added to results. Coefficients: -33.6365Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20250519_005946.]: Samplename: 50 Root: 57.868 --> Root in between the borders! Added to results. Coefficients: -50.255Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20250519_005946.]: Samplename: 62.5 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.893 --> '100 < root < 110' --> substitute 100 Coefficients: -56.539Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20250519_005946.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -65.5923333333333Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20250519_005946.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -88.321Coefficients: 0.697Coefficients: -0.002Coefficients: 0 [20250519_005946.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20250519_005946.]: Solving cubic regression for CpG#5 Coefficients: -0.624000000000001Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20250519_005946.]: Samplename: 0 Root: 1.459 --> Root in between the borders! Added to results. Coefficients: -4.768Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20250519_005946.]: Samplename: 12.5 Root: 10.294 --> Root in between the borders! Added to results. Coefficients: -12.726Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20250519_005946.]: Samplename: 25 Root: 24.477 --> Root in between the borders! Added to results. Coefficients: -21.16Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20250519_005946.]: Samplename: 37.5 Root: 37.097 --> Root in between the borders! Added to results. Coefficients: -30.6955Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20250519_005946.]: Samplename: 50 Root: 49.507 --> Root in between the borders! Added to results. Coefficients: -39.658Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20250519_005946.]: Samplename: 62.5 Root: 59.931 --> Root in between the borders! Added to results. Coefficients: -51.683Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20250519_005946.]: Samplename: 75 Root: 72.563 --> Root in between the borders! Added to results. Coefficients: -61.0146666666667Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20250519_005946.]: Samplename: 87.5 Root: 81.549 --> Root in between the borders! Added to results. Coefficients: -76.07Coefficients: 0.422Coefficients: 0.004Coefficients: 0 [20250519_005946.]: Samplename: 100 Root: 94.898 --> Root in between the borders! Added to results. [20250519_005946.]: Solving cubic regression for CpG#6 Coefficients: -0.196000000000001Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20250519_005946.]: Samplename: 0 Root: 0.349 --> Root in between the borders! Added to results. Coefficients: -6.73866666666667Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20250519_005946.]: Samplename: 12.5 Root: 11.765 --> Root in between the borders! Added to results. Coefficients: -15.888Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20250519_005946.]: Samplename: 25 Root: 27.02 --> Root in between the borders! Added to results. Coefficients: -22Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20250519_005946.]: Samplename: 37.5 Root: 36.802 --> Root in between the borders! Added to results. Coefficients: -33.4445Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20250519_005946.]: Samplename: 50 Root: 54.35 --> Root in between the borders! Added to results. Coefficients: -46.9Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20250519_005946.]: Samplename: 62.5 Root: 73.873 --> Root in between the borders! Added to results. Coefficients: -55.832Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20250519_005946.]: Samplename: 75 Root: 86.259 --> Root in between the borders! Added to results. Coefficients: -71.5453333333333Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20250519_005946.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 107.089 --> '100 < root < 110' --> substitute 100 Coefficients: -89.656Coefficients: 0.561Coefficients: 0.001Coefficients: 0 [20250519_005946.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20250519_005946.]: Solving cubic regression for CpG#7 Coefficients: -1.215Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20250519_005946.]: Samplename: 0 Root: 2.126 --> Root in between the borders! Added to results. Coefficients: -5.39566666666667Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20250519_005946.]: Samplename: 12.5 Root: 9.991 --> Root in between the borders! Added to results. Coefficients: -11.265Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20250519_005946.]: Samplename: 25 Root: 23.104 --> Root in between the borders! Added to results. Coefficients: -17.451Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20250519_005946.]: Samplename: 37.5 Root: 42.609 --> Root in between the borders! Added to results. Coefficients: -26.0315Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20250519_005946.]: Samplename: 50 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -33.965Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20250519_005946.]: Samplename: 62.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -41.169Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20250519_005946.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -44.1356666666667Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20250519_005946.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -67.223Coefficients: 0.58Coefficients: -0.004Coefficients: 0 [20250519_005946.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20250519_005946.]: Solving cubic regression for CpG#8 Coefficients: -1.096Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20250519_005946.]: Samplename: 0 Root: 2.018 --> Root in between the borders! Added to results. Coefficients: -5.44666666666667Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20250519_005946.]: Samplename: 12.5 Root: 9.505 --> Root in between the borders! Added to results. Coefficients: -16.93Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20250519_005946.]: Samplename: 25 Root: 26.424 --> Root in between the borders! Added to results. Coefficients: -23.35Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20250519_005946.]: Samplename: 37.5 Root: 34.662 --> Root in between the borders! Added to results. Coefficients: -37.625Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20250519_005946.]: Samplename: 50 Root: 50.932 --> Root in between the borders! Added to results. Coefficients: -48.826Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20250519_005946.]: Samplename: 62.5 Root: 62.271 --> Root in between the borders! Added to results. Coefficients: -61.6675Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20250519_005946.]: Samplename: 75 Root: 74.154 --> Root in between the borders! Added to results. Coefficients: -64.51Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20250519_005946.]: Samplename: 87.5 Root: 76.651 --> Root in between the borders! Added to results. Coefficients: -86.06Coefficients: 0.535Coefficients: 0.004Coefficients: 0 [20250519_005946.]: Samplename: 100 Root: 94.331 --> Root in between the borders! Added to results. [20250519_005946.]: Solving cubic regression for CpG#9 Coefficients: -0.99Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20250519_005946.]: Samplename: 0 Root: 1.474 --> Root in between the borders! Added to results. Coefficients: -6.396Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20250519_005946.]: Samplename: 12.5 Root: 10.183 --> Root in between the borders! Added to results. Coefficients: -14.706Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20250519_005946.]: Samplename: 25 Root: 27.044 --> Root in between the borders! Added to results. Coefficients: -20.624Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20250519_005946.]: Samplename: 37.5 Root: 45.863 --> Root in between the borders! Added to results. Coefficients: -31.396Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20250519_005946.]: Samplename: 50 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -42.686Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20250519_005946.]: Samplename: 62.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -52.9035Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20250519_005946.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -65.4926666666667Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20250519_005946.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -92.99Coefficients: 0.679Coefficients: -0.005Coefficients: 0 [20250519_005946.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20250519_005946.]: Solving cubic regression for row_means Coefficients: -0.771111111111111Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20250519_005946.]: Samplename: 0 Root: 1.288 --> Root in between the borders! Added to results. Coefficients: -6.47237037037037Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20250519_005946.]: Samplename: 12.5 Root: 10.989 --> Root in between the borders! Added to results. Coefficients: -14.8971111111111Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20250519_005946.]: Samplename: 25 Root: 25.951 --> Root in between the borders! Added to results. Coefficients: -22.1491111111111Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20250519_005946.]: Samplename: 37.5 Root: 39.518 --> Root in between the borders! Added to results. Coefficients: -32.5258888888889Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20250519_005946.]: Samplename: 50 Root: 60.262 --> Root in between the borders! Added to results. Coefficients: -44.7217777777778Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20250519_005946.]: Samplename: 62.5 Root: 87.213 --> Root in between the borders! Added to results. Coefficients: -54.4031111111111Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20250519_005946.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -62.4312592592593Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20250519_005946.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -84.7342222222222Coefficients: 0.6Coefficients: -0.001Coefficients: 0 [20250519_005946.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20250519_005946.]: ### Starting with regression calculations ### [20250519_005946.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_005946.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5)c(1.3350694679926, 11.6842678967181, 24.056490215457, 50.4411280642076)c(1.3350694679926, 0.815732103281899, 0.943509784543, 12.9411280642076)c(NA, 6.52585682625519, 3.774039138172, 34.5096748378869) [20250519_005946.]: Logging df_agg: CpG#1 [20250519_005946.]: c(0, 12.5, 25, 37.5)[20250519_005946.]: c(1.3350694679926, 11.6842678967181, 24.056490215457, 50.4411280642076)[20250519_005946.]: c(1.3350694679926, 0.815732103281899, 0.943509784543, 12.9411280642076)[20250519_005946.]: c(NA, 6.52585682625519, 3.774039138172, 34.5096748378869) [20250519_005946.]: Entered 'hyperbolic_regression'-Function [20250519_005946.]: 'hyperbolic_regression': minmax = FALSE [20250519_005946.]: Entered 'cubic_regression'-Function [20250519_005946.]: 'cubic_regression': minmax = FALSE [20250519_005946.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.547766708541827, 11.5349537694669, 26.7737761718445, 35.875449331591, 48.7401699196391, 69.1560032174257, 78.551969470104, 88.2723976336729, 100)c(0.547766708541827, 0.965046230533099, 1.7737761718445, 1.624550668409, 1.2598300803609, 6.65600321742571, 3.551969470104, 0.772397633672895, 0)c(NA, 7.72036984426479, 7.095104687378, 4.33213511575733, 2.5196601607218, 10.6496051478811, 4.735959293472, 0.882740152769023, 0) [20250519_005946.]: Logging df_agg: CpG#2 [20250519_005946.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005946.]: c(0.547766708541827, 11.5349537694669, 26.7737761718445, 35.875449331591, 48.7401699196391, 69.1560032174257, 78.551969470104, 88.2723976336729, 100)[20250519_005946.]: c(0.547766708541827, 0.965046230533099, 1.7737761718445, 1.624550668409, 1.2598300803609, 6.65600321742571, 3.551969470104, 0.772397633672895, 0)[20250519_005946.]: c(NA, 7.72036984426479, 7.095104687378, 4.33213511575733, 2.5196601607218, 10.6496051478811, 4.735959293472, 0.882740152769023, 0) [20250519_005946.]: Entered 'hyperbolic_regression'-Function [20250519_005946.]: 'hyperbolic_regression': minmax = FALSE [20250519_005947.]: Entered 'cubic_regression'-Function [20250519_005947.]: 'cubic_regression': minmax = FALSE [20250519_005947.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75)c(1.44120553086336, 10.6470702300701, 25.6131070939596, 38.3498594232174, 57.444937683935, 80.104724288839, 100)c(1.44120553086336, 1.8529297699299, 0.613107093959599, 0.849859423217403, 7.444937683935, 17.604724288839, 25)c(NA, 14.8234381594392, 2.45242837583839, 2.26629179524641, 14.88987536787, 28.1675588621424, 33.3333333333333) [20250519_005947.]: Logging df_agg: CpG#3 [20250519_005947.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75)[20250519_005947.]: c(1.44120553086336, 10.6470702300701, 25.6131070939596, 38.3498594232174, 57.444937683935, 80.104724288839, 100)[20250519_005947.]: c(1.44120553086336, 1.8529297699299, 0.613107093959599, 0.849859423217403, 7.444937683935, 17.604724288839, 25)[20250519_005947.]: c(NA, 14.8234381594392, 2.45242837583839, 2.26629179524641, 14.88987536787, 28.1675588621424, 33.3333333333333) [20250519_005947.]: Entered 'hyperbolic_regression'-Function [20250519_005947.]: 'hyperbolic_regression': minmax = FALSE [20250519_005947.]: Entered 'cubic_regression'-Function [20250519_005947.]: 'cubic_regression': minmax = FALSE [20250519_005947.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5)c(0.858643525693588, 12.2719279243844, 24.3781797668421, 41.5866572861968, 57.86781708526, 100)c(0.858643525693588, 0.2280720756156, 0.621820233157901, 4.0866572861968, 7.86781708526, 37.5)c(NA, 1.8245766049248, 2.48728093263161, 10.8977527631915, 15.73563417052, 60) [20250519_005947.]: Logging df_agg: CpG#4 [20250519_005947.]: c(0, 12.5, 25, 37.5, 50, 62.5)[20250519_005947.]: c(0.858643525693588, 12.2719279243844, 24.3781797668421, 41.5866572861968, 57.86781708526, 100)[20250519_005947.]: c(0.858643525693588, 0.2280720756156, 0.621820233157901, 4.0866572861968, 7.86781708526, 37.5)[20250519_005947.]: c(NA, 1.8245766049248, 2.48728093263161, 10.8977527631915, 15.73563417052, 60) [20250519_005947.]: Entered 'hyperbolic_regression'-Function [20250519_005947.]: 'hyperbolic_regression': minmax = FALSE [20250519_005947.]: Entered 'cubic_regression'-Function [20250519_005947.]: 'cubic_regression': minmax = FALSE [20250519_005947.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.45850947960108, 10.2941313684311, 24.4773429557174, 37.0974401416089, 49.5067234953282, 59.9312428933937, 72.5628584782902, 81.5490289118528, 94.898442253889)c(1.45850947960108, 2.2058686315689, 0.5226570442826, 0.402559858391101, 0.493276504671798, 2.5687571066063, 2.4371415217098, 5.9509710881472, 5.101557746111)c(NA, 17.6469490525512, 2.0906281771304, 1.0734929557096, 0.986553009343595, 4.11001137057008, 3.24952202894641, 6.80110981502537, 5.101557746111) [20250519_005947.]: Logging df_agg: CpG#5 [20250519_005947.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005947.]: c(1.45850947960108, 10.2941313684311, 24.4773429557174, 37.0974401416089, 49.5067234953282, 59.9312428933937, 72.5628584782902, 81.5490289118528, 94.898442253889)[20250519_005947.]: c(1.45850947960108, 2.2058686315689, 0.5226570442826, 0.402559858391101, 0.493276504671798, 2.5687571066063, 2.4371415217098, 5.9509710881472, 5.101557746111)[20250519_005947.]: c(NA, 17.6469490525512, 2.0906281771304, 1.0734929557096, 0.986553009343595, 4.11001137057008, 3.24952202894641, 6.80110981502537, 5.101557746111) [20250519_005947.]: Entered 'hyperbolic_regression'-Function [20250519_005947.]: 'hyperbolic_regression': minmax = FALSE [20250519_005947.]: Entered 'cubic_regression'-Function [20250519_005947.]: 'cubic_regression': minmax = FALSE [20250519_005947.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)c(0.349158802372062, 11.7651478823068, 27.019511576095, 36.8015127603397, 54.3503397041729, 73.8730379134395, 86.2591171327579, 100)c(0.349158802372062, 0.734852117693199, 2.019511576095, 0.698487239660302, 4.3503397041729, 11.3730379134395, 11.2591171327579, 12.5)c(NA, 5.87881694154559, 8.07804630438, 1.86263263909414, 8.70067940834581, 18.1968606615032, 15.0121561770105, 14.2857142857143) [20250519_005947.]: Logging df_agg: CpG#6 [20250519_005947.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)[20250519_005947.]: c(0.349158802372062, 11.7651478823068, 27.019511576095, 36.8015127603397, 54.3503397041729, 73.8730379134395, 86.2591171327579, 100)[20250519_005947.]: c(0.349158802372062, 0.734852117693199, 2.019511576095, 0.698487239660302, 4.3503397041729, 11.3730379134395, 11.2591171327579, 12.5)[20250519_005947.]: c(NA, 5.87881694154559, 8.07804630438, 1.86263263909414, 8.70067940834581, 18.1968606615032, 15.0121561770105, 14.2857142857143) [20250519_005947.]: Entered 'hyperbolic_regression'-Function [20250519_005947.]: 'hyperbolic_regression': minmax = FALSE [20250519_005948.]: Entered 'cubic_regression'-Function [20250519_005948.]: 'cubic_regression': minmax = FALSE [20250519_005948.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5)c(2.12599911899281, 9.99133393413891, 23.1036438590861, 42.6085296447297)c(2.12599911899281, 2.50866606586109, 1.8963561409139, 5.1085296447297)c(NA, 20.0693285268887, 7.58542456365559, 13.6227457192792) [20250519_005948.]: Logging df_agg: CpG#7 [20250519_005948.]: c(0, 12.5, 25, 37.5)[20250519_005948.]: c(2.12599911899281, 9.99133393413891, 23.1036438590861, 42.6085296447297)[20250519_005948.]: c(2.12599911899281, 2.50866606586109, 1.8963561409139, 5.1085296447297)[20250519_005948.]: c(NA, 20.0693285268887, 7.58542456365559, 13.6227457192792) [20250519_005948.]: Entered 'hyperbolic_regression'-Function [20250519_005948.]: 'hyperbolic_regression': minmax = FALSE [20250519_005948.]: Entered 'cubic_regression'-Function [20250519_005948.]: 'cubic_regression': minmax = FALSE [20250519_005948.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.0181464298155, 9.50518258466437, 26.4243334649289, 34.6620160335629, 50.9321119457565, 62.2712954366094, 74.1538645100711, 76.6511844577497, 94.3306625091067)c(2.0181464298155, 2.99481741533563, 1.4243334649289, 2.8379839664371, 0.932111945756503, 0.2287045633906, 0.846135489928898, 10.8488155422503, 5.6693374908933)c(NA, 23.958539322685, 5.6973338597156, 7.56795724383227, 1.86422389151301, 0.365927301424961, 1.12818065323853, 12.3986463340004, 5.6693374908933) [20250519_005948.]: Logging df_agg: CpG#8 [20250519_005948.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005948.]: c(2.0181464298155, 9.50518258466437, 26.4243334649289, 34.6620160335629, 50.9321119457565, 62.2712954366094, 74.1538645100711, 76.6511844577497, 94.3306625091067)[20250519_005948.]: c(2.0181464298155, 2.99481741533563, 1.4243334649289, 2.8379839664371, 0.932111945756503, 0.2287045633906, 0.846135489928898, 10.8488155422503, 5.6693374908933)[20250519_005948.]: c(NA, 23.958539322685, 5.6973338597156, 7.56795724383227, 1.86422389151301, 0.365927301424961, 1.12818065323853, 12.3986463340004, 5.6693374908933) [20250519_005948.]: Entered 'hyperbolic_regression'-Function [20250519_005948.]: 'hyperbolic_regression': minmax = FALSE [20250519_005948.]: Entered 'cubic_regression'-Function [20250519_005948.]: 'cubic_regression': minmax = FALSE [20250519_005948.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5)c(1.47402616445883, 10.1833646164297, 27.0440334834678, 45.8634394698265)c(1.47402616445883, 2.3166353835703, 2.0440334834678, 8.3634394698265)c(NA, 18.5330830685624, 8.1761339338712, 22.3025052528707) [20250519_005948.]: Logging df_agg: CpG#9 [20250519_005948.]: c(0, 12.5, 25, 37.5)[20250519_005948.]: c(1.47402616445883, 10.1833646164297, 27.0440334834678, 45.8634394698265)[20250519_005948.]: c(1.47402616445883, 2.3166353835703, 2.0440334834678, 8.3634394698265)[20250519_005948.]: c(NA, 18.5330830685624, 8.1761339338712, 22.3025052528707) [20250519_005948.]: Entered 'hyperbolic_regression'-Function [20250519_005948.]: 'hyperbolic_regression': minmax = FALSE [20250519_005948.]: Entered 'cubic_regression'-Function [20250519_005948.]: 'cubic_regression': minmax = FALSE [20250519_005948.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5)c(1.28794987665983, 10.9885302801468, 25.950937077156, 39.5179682033924, 60.2624119769469, 87.2132000752344)c(1.28794987665983, 1.5114697198532, 0.950937077155999, 2.0179682033924, 10.2624119769469, 24.7132000752344)c(NA, 12.0917577588256, 3.80374830862399, 5.38124854237973, 20.5248239538938, 39.541120120375) [20250519_005948.]: Logging df_agg: row_means [20250519_005948.]: c(0, 12.5, 25, 37.5, 50, 62.5)[20250519_005948.]: c(1.28794987665983, 10.9885302801468, 25.950937077156, 39.5179682033924, 60.2624119769469, 87.2132000752344)[20250519_005948.]: c(1.28794987665983, 1.5114697198532, 0.950937077155999, 2.0179682033924, 10.2624119769469, 24.7132000752344)[20250519_005948.]: c(NA, 12.0917577588256, 3.80374830862399, 5.38124854237973, 20.5248239538938, 39.541120120375) [20250519_005948.]: Entered 'hyperbolic_regression'-Function [20250519_005948.]: 'hyperbolic_regression': minmax = FALSE [20250519_005948.]: Entered 'cubic_regression'-Function [20250519_005948.]: 'cubic_regression': minmax = FALSE [20250519_005949.]: Entered 'solving_equations'-Function [20250519_005949.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 79.8672902891399 [20250519_005949.]: Samplename: Sample#1 Root: 79.867 --> Root in between the borders! Added to results. Hyperbolic solved: 29.789995526108 [20250519_005949.]: Samplename: Sample#10 Root: 29.79 --> Root in between the borders! Added to results. Hyperbolic solved: 41.6525103283956 [20250519_005949.]: Samplename: Sample#2 Root: 41.653 --> Root in between the borders! Added to results. Hyperbolic solved: 57.4651672980431 [20250519_005949.]: Samplename: Sample#3 Root: 57.465 --> Root in between the borders! Added to results. Hyperbolic solved: 9.20070553821487 [20250519_005949.]: Samplename: Sample#4 Root: 9.201 --> Root in between the borders! Added to results. Hyperbolic solved: 21.8059429711637 [20250519_005949.]: Samplename: Sample#5 Root: 21.806 --> Root in between the borders! Added to results. Hyperbolic solved: 23.0837787089758 [20250519_005949.]: Samplename: Sample#6 Root: 23.084 --> Root in between the borders! Added to results. Hyperbolic solved: 45.5033907021902 [20250519_005949.]: Samplename: Sample#7 Root: 45.503 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6987316769908 [20250519_005949.]: Samplename: Sample#8 Root: 85.699 --> Root in between the borders! Added to results. Hyperbolic solved: -3.66512538562846 [20250519_005949.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -3.665 --> '-10 < root < 0' --> substitute 0 [20250519_005949.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 76.7230240612756 [20250519_005949.]: Samplename: Sample#1 Root: 76.723 --> Root in between the borders! Added to results. Hyperbolic solved: 30.992543297941 [20250519_005949.]: Samplename: Sample#10 Root: 30.993 --> Root in between the borders! Added to results. Hyperbolic solved: 42.6812361010669 [20250519_005949.]: Samplename: Sample#2 Root: 42.681 --> Root in between the borders! Added to results. Hyperbolic solved: 58.933980239834 [20250519_005949.]: Samplename: Sample#3 Root: 58.934 --> Root in between the borders! Added to results. Hyperbolic solved: 4.98024265777963 [20250519_005949.]: Samplename: Sample#4 Root: 4.98 --> Root in between the borders! Added to results. Hyperbolic solved: 20.2481111308688 [20250519_005949.]: Samplename: Sample#5 Root: 20.248 --> Root in between the borders! Added to results. Hyperbolic solved: 17.813386088098 [20250519_005949.]: Samplename: Sample#6 Root: 17.813 --> Root in between the borders! Added to results. Hyperbolic solved: 40.9178176381316 [20250519_005949.]: Samplename: Sample#7 Root: 40.918 --> Root in between the borders! Added to results. Hyperbolic solved: 86.0453226524415 [20250519_005949.]: Samplename: Sample#8 Root: 86.045 --> Root in between the borders! Added to results. Hyperbolic solved: 3.11582988095862 [20250519_005949.]: Samplename: Sample#9 Root: 3.116 --> Root in between the borders! Added to results. [20250519_005949.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 74.934721803959 [20250519_005949.]: Samplename: Sample#1 Root: 74.935 --> Root in between the borders! Added to results. Hyperbolic solved: 27.6843475257834 [20250519_005949.]: Samplename: Sample#10 Root: 27.684 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8518905782652 [20250519_005949.]: Samplename: Sample#2 Root: 41.852 --> Root in between the borders! Added to results. Hyperbolic solved: 55.8323554649537 [20250519_005949.]: Samplename: Sample#3 Root: 55.832 --> Root in between the borders! Added to results. Hyperbolic solved: 8.03516016616019 [20250519_005949.]: Samplename: Sample#4 Root: 8.035 --> Root in between the borders! Added to results. Hyperbolic solved: 24.1065510576898 [20250519_005949.]: Samplename: Sample#5 Root: 24.107 --> Root in between the borders! Added to results. Hyperbolic solved: 26.2418954231184 [20250519_005949.]: Samplename: Sample#6 Root: 26.242 --> Root in between the borders! Added to results. Hyperbolic solved: 44.0943580511304 [20250519_005949.]: Samplename: Sample#7 Root: 44.094 --> Root in between the borders! Added to results. Hyperbolic solved: 85.8277140531902 [20250519_005949.]: Samplename: Sample#8 Root: 85.828 --> Root in between the borders! Added to results. Hyperbolic solved: -0.666486811581751 [20250519_005949.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.666 --> '-10 < root < 0' --> substitute 0 [20250519_005949.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 76.349372671628 [20250519_005949.]: Samplename: Sample#1 Root: 76.349 --> Root in between the borders! Added to results. Hyperbolic solved: 28.2567843724913 [20250519_005949.]: Samplename: Sample#10 Root: 28.257 --> Root in between the borders! Added to results. Hyperbolic solved: 43.4088841408467 [20250519_005949.]: Samplename: Sample#2 Root: 43.409 --> Root in between the borders! Added to results. Hyperbolic solved: 58.5433972042602 [20250519_005949.]: Samplename: Sample#3 Root: 58.543 --> Root in between the borders! Added to results. Hyperbolic solved: 10.3086704325003 [20250519_005949.]: Samplename: Sample#4 Root: 10.309 --> Root in between the borders! Added to results. Hyperbolic solved: 22.182986320858 [20250519_005949.]: Samplename: Sample#5 Root: 22.183 --> Root in between the borders! Added to results. Hyperbolic solved: 27.1337081900258 [20250519_005949.]: Samplename: Sample#6 Root: 27.134 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8320127104856 [20250519_005949.]: Samplename: Sample#7 Root: 41.832 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6888498156134 [20250519_005949.]: Samplename: Sample#8 Root: 85.689 --> Root in between the borders! Added to results. Hyperbolic solved: 2.42230396811441 [20250519_005949.]: Samplename: Sample#9 Root: 2.422 --> Root in between the borders! Added to results. [20250519_005949.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 72.8126507590781 [20250519_005949.]: Samplename: Sample#1 Root: 72.813 --> Root in between the borders! Added to results. Hyperbolic solved: 26.4206774223762 [20250519_005949.]: Samplename: Sample#10 Root: 26.421 --> Root in between the borders! Added to results. Hyperbolic solved: 44.2738516315831 [20250519_005949.]: Samplename: Sample#2 Root: 44.274 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5324088748019 [20250519_005949.]: Samplename: Sample#3 Root: 53.532 --> Root in between the borders! Added to results. Hyperbolic solved: 10.0373628417333 [20250519_005949.]: Samplename: Sample#4 Root: 10.037 --> Root in between the borders! Added to results. Hyperbolic solved: 22.8732969704156 [20250519_005949.]: Samplename: Sample#5 Root: 22.873 --> Root in between the borders! Added to results. Hyperbolic solved: 25.9417756406497 [20250519_005949.]: Samplename: Sample#6 Root: 25.942 --> Root in between the borders! Added to results. Hyperbolic solved: 42.7944833740993 [20250519_005949.]: Samplename: Sample#7 Root: 42.794 --> Root in between the borders! Added to results. Hyperbolic solved: 88.8740708807914 [20250519_005949.]: Samplename: Sample#8 Root: 88.874 --> Root in between the borders! Added to results. Hyperbolic solved: 2.34646946564885 [20250519_005949.]: Samplename: Sample#9 Root: 2.346 --> Root in between the borders! Added to results. [20250519_005949.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 79.2782328649447 [20250519_005949.]: Samplename: Sample#1 Root: 79.278 --> Root in between the borders! Added to results. Hyperbolic solved: 30.2013285011234 [20250519_005949.]: Samplename: Sample#10 Root: 30.201 --> Root in between the borders! Added to results. Hyperbolic solved: 41.847545497608 [20250519_005949.]: Samplename: Sample#2 Root: 41.848 --> Root in between the borders! Added to results. Hyperbolic solved: 56.8424863688491 [20250519_005949.]: Samplename: Sample#3 Root: 56.842 --> Root in between the borders! Added to results. Hyperbolic solved: 8.87859681588532 [20250519_005949.]: Samplename: Sample#4 Root: 8.879 --> Root in between the borders! Added to results. Hyperbolic solved: 18.6902176438696 [20250519_005949.]: Samplename: Sample#5 Root: 18.69 --> Root in between the borders! Added to results. Hyperbolic solved: 29.9310083954849 [20250519_005949.]: Samplename: Sample#6 Root: 29.931 --> Root in between the borders! Added to results. Hyperbolic solved: 42.814964037839 [20250519_005949.]: Samplename: Sample#7 Root: 42.815 --> Root in between the borders! Added to results. Hyperbolic solved: 86.7503685829287 [20250519_005949.]: Samplename: Sample#8 Root: 86.75 --> Root in between the borders! Added to results. Hyperbolic solved: 1.51518138323643 [20250519_005949.]: Samplename: Sample#9 Root: 1.515 --> Root in between the borders! Added to results. [20250519_005949.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 78.2568233815803 [20250519_005949.]: Samplename: Sample#1 Root: 78.257 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4888594923858 [20250519_005949.]: Samplename: Sample#10 Root: 25.489 --> Root in between the borders! Added to results. Hyperbolic solved: 47.3714242177043 [20250519_005949.]: Samplename: Sample#2 Root: 47.371 --> Root in between the borders! Added to results. Hyperbolic solved: 58.3144949237501 [20250519_005949.]: Samplename: Sample#3 Root: 58.314 --> Root in between the borders! Added to results. Hyperbolic solved: 11.7212806729981 [20250519_005949.]: Samplename: Sample#4 Root: 11.721 --> Root in between the borders! Added to results. Hyperbolic solved: 25.3798682873976 [20250519_005949.]: Samplename: Sample#5 Root: 25.38 --> Root in between the borders! Added to results. Hyperbolic solved: 29.4096493828253 [20250519_005949.]: Samplename: Sample#6 Root: 29.41 --> Root in between the borders! Added to results. Hyperbolic solved: 44.5756969430888 [20250519_005949.]: Samplename: Sample#7 Root: 44.576 --> Root in between the borders! Added to results. Hyperbolic solved: 85.9631455147929 [20250519_005949.]: Samplename: Sample#8 Root: 85.963 --> Root in between the borders! Added to results. Hyperbolic solved: -4.16459229634232 [20250519_005949.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -4.165 --> '-10 < root < 0' --> substitute 0 [20250519_005949.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 72.9696678780879 [20250519_005949.]: Samplename: Sample#1 Root: 72.97 --> Root in between the borders! Added to results. Hyperbolic solved: 27.825096438612 [20250519_005949.]: Samplename: Sample#10 Root: 27.825 --> Root in between the borders! Added to results. Hyperbolic solved: 34.9068412412515 [20250519_005949.]: Samplename: Sample#2 Root: 34.907 --> Root in between the borders! Added to results. Hyperbolic solved: 59.1637501357335 [20250519_005949.]: Samplename: Sample#3 Root: 59.164 --> Root in between the borders! Added to results. Hyperbolic solved: 9.6464397671905 [20250519_005949.]: Samplename: Sample#4 Root: 9.646 --> Root in between the borders! Added to results. Hyperbolic solved: 19.3522593577541 [20250519_005949.]: Samplename: Sample#5 Root: 19.352 --> Root in between the borders! Added to results. Hyperbolic solved: 38.5571468548369 [20250519_005949.]: Samplename: Sample#6 Root: 38.557 --> Root in between the borders! Added to results. Hyperbolic solved: 44.775966890921 [20250519_005949.]: Samplename: Sample#7 Root: 44.776 --> Root in between the borders! Added to results. Hyperbolic solved: 84.8599747802978 [20250519_005949.]: Samplename: Sample#8 Root: 84.86 --> Root in between the borders! Added to results. Hyperbolic solved: -2.05045739684141 [20250519_005949.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.05 --> '-10 < root < 0' --> substitute 0 [20250519_005949.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 81.0413625074508 [20250519_005949.]: Samplename: Sample#1 Root: 81.041 --> Root in between the borders! Added to results. Hyperbolic solved: 26.6440232383638 [20250519_005949.]: Samplename: Sample#10 Root: 26.644 --> Root in between the borders! Added to results. Hyperbolic solved: 45.704400902509 [20250519_005949.]: Samplename: Sample#2 Root: 45.704 --> Root in between the borders! Added to results. Hyperbolic solved: 56.9765444860285 [20250519_005949.]: Samplename: Sample#3 Root: 56.977 --> Root in between the borders! Added to results. Hyperbolic solved: 6.81226926030202 [20250519_005949.]: Samplename: Sample#4 Root: 6.812 --> Root in between the borders! Added to results. Hyperbolic solved: 22.9283517775063 [20250519_005949.]: Samplename: Sample#5 Root: 22.928 --> Root in between the borders! Added to results. Hyperbolic solved: 38.8651260750996 [20250519_005949.]: Samplename: Sample#6 Root: 38.865 --> Root in between the borders! Added to results. Hyperbolic solved: 43.6853511825078 [20250519_005949.]: Samplename: Sample#7 Root: 43.685 --> Root in between the borders! Added to results. Hyperbolic solved: 88.0152224353095 [20250519_005949.]: Samplename: Sample#8 Root: 88.015 --> Root in between the borders! Added to results. Hyperbolic solved: -3.35608361520768 [20250519_005949.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -3.356 --> '-10 < root < 0' --> substitute 0 [20250519_005949.]: Solving hyperbolic regression for row_means Hyperbolic solved: 77.069275047056 [20250519_005949.]: Samplename: Sample#1 Root: 77.069 --> Root in between the borders! Added to results. Hyperbolic solved: 28.3620003757916 [20250519_005949.]: Samplename: Sample#10 Root: 28.362 --> Root in between the borders! Added to results. Hyperbolic solved: 42.5026135474906 [20250519_005949.]: Samplename: Sample#2 Root: 42.503 --> Root in between the borders! Added to results. Hyperbolic solved: 57.2972007822813 [20250519_005949.]: Samplename: Sample#3 Root: 57.297 --> Root in between the borders! Added to results. Hyperbolic solved: 8.82703592503651 [20250519_005949.]: Samplename: Sample#4 Root: 8.827 --> Root in between the borders! Added to results. Hyperbolic solved: 21.8102552608375 [20250519_005949.]: Samplename: Sample#5 Root: 21.81 --> Root in between the borders! Added to results. Hyperbolic solved: 28.7228620570755 [20250519_005949.]: Samplename: Sample#6 Root: 28.723 --> Root in between the borders! Added to results. Hyperbolic solved: 43.4105062814289 [20250519_005949.]: Samplename: Sample#7 Root: 43.411 --> Root in between the borders! Added to results. Hyperbolic solved: 86.4143497902066 [20250519_005949.]: Samplename: Sample#8 Root: 86.414 --> Root in between the borders! Added to results. Hyperbolic solved: -0.237025016559672 [20250519_005949.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.237 --> '-10 < root < 0' --> substitute 0 [20250519_005949.]: Entered 'solving_equations'-Function [20250519_005949.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: -2.23222837469517 [20250519_005949.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.232 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.1698391693126 [20250519_005949.]: Samplename: 12.5 Root: 12.17 --> Root in between the borders! Added to results. Hyperbolic solved: 24.4781729791561 [20250519_005949.]: Samplename: 25 Root: 24.478 --> Root in between the borders! Added to results. Hyperbolic solved: 38.1730159074047 [20250519_005949.]: Samplename: 37.5 Root: 38.173 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3348987967717 [20250519_005949.]: Samplename: 50 Root: 52.335 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4582305966058 [20250519_005949.]: Samplename: 62.5 Root: 65.458 --> Root in between the borders! Added to results. Hyperbolic solved: 75.0090269722885 [20250519_005949.]: Samplename: 75 Root: 75.009 --> Root in between the borders! Added to results. Hyperbolic solved: 81.527135738891 [20250519_005949.]: Samplename: 87.5 Root: 81.527 --> Root in between the borders! Added to results. Hyperbolic solved: 102.400825350044 [20250519_005949.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.401 --> '100 < root < 110' --> substitute 100 [20250519_005949.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 1.13663087266051 [20250519_005949.]: Samplename: 0 Root: 1.137 --> Root in between the borders! Added to results. Hyperbolic solved: 11.4130277963576 [20250519_005949.]: Samplename: 12.5 Root: 11.413 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1741025868157 [20250519_005949.]: Samplename: 25 Root: 26.174 --> Root in between the borders! Added to results. Hyperbolic solved: 35.1051721736724 [20250519_005949.]: Samplename: 37.5 Root: 35.105 --> Root in between the borders! Added to results. Hyperbolic solved: 47.6856613240114 [20250519_005949.]: Samplename: 50 Root: 47.686 --> Root in between the borders! Added to results. Hyperbolic solved: 67.1442585354696 [20250519_005949.]: Samplename: 62.5 Root: 67.144 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7646957109439 [20250519_005949.]: Samplename: 75 Root: 75.765 --> Root in between the borders! Added to results. Hyperbolic solved: 84.4056634834815 [20250519_005949.]: Samplename: 87.5 Root: 84.406 --> Root in between the borders! Added to results. Hyperbolic solved: 100.94855337669 [20250519_005949.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.949 --> '100 < root < 110' --> substitute 100 [20250519_005949.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 0.512348257854635 [20250519_005949.]: Samplename: 0 Root: 0.512 --> Root in between the borders! Added to results. Hyperbolic solved: 10.7523476241811 [20250519_005949.]: Samplename: 12.5 Root: 10.752 --> Root in between the borders! Added to results. Hyperbolic solved: 25.5218062516799 [20250519_005949.]: Samplename: 25 Root: 25.522 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5269315729339 [20250519_005949.]: Samplename: 37.5 Root: 36.527 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7907738274884 [20250519_005949.]: Samplename: 50 Root: 50.791 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8684489988589 [20250519_005949.]: Samplename: 62.5 Root: 64.868 --> Root in between the borders! Added to results. Hyperbolic solved: 77.5522101073979 [20250519_005949.]: Samplename: 75 Root: 77.552 --> Root in between the borders! Added to results. Hyperbolic solved: 80.4372474691229 [20250519_005949.]: Samplename: 87.5 Root: 80.437 --> Root in between the borders! Added to results. Hyperbolic solved: 102.703772810881 [20250519_005949.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.704 --> '100 < root < 110' --> substitute 100 [20250519_005949.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: -0.519513573026794 [20250519_005949.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.52 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.4933760051342 [20250519_005949.]: Samplename: 12.5 Root: 12.493 --> Root in between the borders! Added to results. Hyperbolic solved: 24.2684789512555 [20250519_005949.]: Samplename: 25 Root: 24.268 --> Root in between the borders! Added to results. Hyperbolic solved: 38.0816229393195 [20250519_005949.]: Samplename: 37.5 Root: 38.082 --> Root in between the borders! Added to results. Hyperbolic solved: 48.5842089589187 [20250519_005949.]: Samplename: 50 Root: 48.584 --> Root in between the borders! Added to results. Hyperbolic solved: 67.6720983549562 [20250519_005949.]: Samplename: 62.5 Root: 67.672 --> Root in between the borders! Added to results. Hyperbolic solved: 74.1547759694059 [20250519_005949.]: Samplename: 75 Root: 74.155 --> Root in between the borders! Added to results. Hyperbolic solved: 82.8820147905386 [20250519_005949.]: Samplename: 87.5 Root: 82.882 --> Root in between the borders! Added to results. Hyperbolic solved: 102.078935346876 [20250519_005949.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.079 --> '100 < root < 110' --> substitute 100 [20250519_005949.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 2.41558040143479 [20250519_005949.]: Samplename: 0 Root: 2.416 --> Root in between the borders! Added to results. Hyperbolic solved: 10.1649584830834 [20250519_005949.]: Samplename: 12.5 Root: 10.165 --> Root in between the borders! Added to results. Hyperbolic solved: 23.9830670020905 [20250519_005949.]: Samplename: 25 Root: 23.983 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2773406385708 [20250519_005949.]: Samplename: 37.5 Root: 37.277 --> Root in between the borders! Added to results. Hyperbolic solved: 50.8659103346102 [20250519_005949.]: Samplename: 50 Root: 50.866 --> Root in between the borders! Added to results. Hyperbolic solved: 62.4341926937382 [20250519_005949.]: Samplename: 62.5 Root: 62.434 --> Root in between the borders! Added to results. Hyperbolic solved: 76.3914832329149 [20250519_005949.]: Samplename: 75 Root: 76.391 --> Root in between the borders! Added to results. Hyperbolic solved: 86.1597399215782 [20250519_005949.]: Samplename: 87.5 Root: 86.16 --> Root in between the borders! Added to results. Hyperbolic solved: 100.267701841999 [20250519_005949.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.268 --> '100 < root < 110' --> substitute 100 [20250519_005949.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 0.138179963459196 [20250519_005949.]: Samplename: 0 Root: 0.138 --> Root in between the borders! Added to results. Hyperbolic solved: 11.8635989635202 [20250519_005949.]: Samplename: 12.5 Root: 11.864 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5108198460409 [20250519_005949.]: Samplename: 25 Root: 26.511 --> Root in between the borders! Added to results. Hyperbolic solved: 35.3206004021833 [20250519_005949.]: Samplename: 37.5 Root: 35.321 --> Root in between the borders! Added to results. Hyperbolic solved: 50.0571987473308 [20250519_005949.]: Samplename: 50 Root: 50.057 --> Root in between the borders! Added to results. Hyperbolic solved: 64.9604436607063 [20250519_005949.]: Samplename: 62.5 Root: 64.96 --> Root in between the borders! Added to results. Hyperbolic solved: 73.6690699266294 [20250519_005949.]: Samplename: 75 Root: 73.669 --> Root in between the borders! Added to results. Hyperbolic solved: 87.1267946879604 [20250519_005949.]: Samplename: 87.5 Root: 87.127 --> Root in between the borders! Added to results. Hyperbolic solved: 100.261842767865 [20250519_005949.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.262 --> '100 < root < 110' --> substitute 100 [20250519_005949.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: -1.37239249181669 [20250519_005949.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -1.372 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.199366215418 [20250519_005949.]: Samplename: 12.5 Root: 10.199 --> Root in between the borders! Added to results. Hyperbolic solved: 24.595295361327 [20250519_005949.]: Samplename: 25 Root: 24.595 --> Root in between the borders! Added to results. Hyperbolic solved: 37.8312095633391 [20250519_005949.]: Samplename: 37.5 Root: 37.831 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5590896582852 [20250519_005949.]: Samplename: 50 Root: 53.559 --> Root in between the borders! Added to results. Hyperbolic solved: 65.9367389282431 [20250519_005949.]: Samplename: 62.5 Root: 65.937 --> Root in between the borders! Added to results. Hyperbolic solved: 75.736370152841 [20250519_005949.]: Samplename: 75 Root: 75.736 --> Root in between the borders! Added to results. Hyperbolic solved: 79.433089359524 [20250519_005949.]: Samplename: 87.5 Root: 79.433 --> Root in between the borders! Added to results. Hyperbolic solved: 103.004516225662 [20250519_005949.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 103.005 --> '100 < root < 110' --> substitute 100 [20250519_005949.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 2.80067549526643 [20250519_005949.]: Samplename: 0 Root: 2.801 --> Root in between the borders! Added to results. Hyperbolic solved: 9.27534686003087 [20250519_005949.]: Samplename: 12.5 Root: 9.275 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4762624089952 [20250519_005949.]: Samplename: 25 Root: 25.476 --> Root in between the borders! Added to results. Hyperbolic solved: 34.0122098045585 [20250519_005949.]: Samplename: 37.5 Root: 34.012 --> Root in between the borders! Added to results. Hyperbolic solved: 51.784270967861 [20250519_005950.]: Samplename: 50 Root: 51.784 --> Root in between the borders! Added to results. Hyperbolic solved: 64.6732380258789 [20250519_005950.]: Samplename: 62.5 Root: 64.673 --> Root in between the borders! Added to results. Hyperbolic solved: 78.4327054483192 [20250519_005950.]: Samplename: 75 Root: 78.433 --> Root in between the borders! Added to results. Hyperbolic solved: 81.3427308951795 [20250519_005950.]: Samplename: 87.5 Root: 81.343 --> Root in between the borders! Added to results. Hyperbolic solved: 101.964413523995 [20250519_005950.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.964 --> '100 < root < 110' --> substitute 100 [20250519_005950.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: -2.13402763663736 [20250519_005950.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.134 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.5081558897243 [20250519_005950.]: Samplename: 12.5 Root: 10.508 --> Root in between the borders! Added to results. Hyperbolic solved: 26.9163121098627 [20250519_005950.]: Samplename: 25 Root: 26.916 --> Root in between the borders! Added to results. Hyperbolic solved: 36.8332915301825 [20250519_005950.]: Samplename: 37.5 Root: 36.833 --> Root in between the borders! Added to results. Hyperbolic solved: 52.00955 [20250519_005950.]: Samplename: 50 Root: 52.01 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8927781893359 [20250519_005950.]: Samplename: 62.5 Root: 64.893 --> Root in between the borders! Added to results. Hyperbolic solved: 74.566810799042 [20250519_005950.]: Samplename: 75 Root: 74.567 --> Root in between the borders! Added to results. Hyperbolic solved: 84.5291854856128 [20250519_005950.]: Samplename: 87.5 Root: 84.529 --> Root in between the borders! Added to results. Hyperbolic solved: 101.046823491232 [20250519_005950.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.047 --> '100 < root < 110' --> substitute 100 [20250519_005950.]: Solving hyperbolic regression for row_means Hyperbolic solved: 0.290936038655697 [20250519_005950.]: Samplename: 0 Root: 0.291 --> Root in between the borders! Added to results. Hyperbolic solved: 11.0412364555656 [20250519_005950.]: Samplename: 12.5 Root: 11.041 --> Root in between the borders! Added to results. Hyperbolic solved: 25.408146358228 [20250519_005950.]: Samplename: 25 Root: 25.408 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5243683648753 [20250519_005950.]: Samplename: 37.5 Root: 36.524 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7348788337891 [20250519_005950.]: Samplename: 50 Root: 50.735 --> Root in between the borders! Added to results. Hyperbolic solved: 65.3135169308955 [20250519_005950.]: Samplename: 62.5 Root: 65.314 --> Root in between the borders! Added to results. Hyperbolic solved: 75.5342663138126 [20250519_005950.]: Samplename: 75 Root: 75.534 --> Root in between the borders! Added to results. Hyperbolic solved: 83.2411177153911 [20250519_005950.]: Samplename: 87.5 Root: 83.241 --> Root in between the borders! Added to results. Hyperbolic solved: 101.666935931185 [20250519_005950.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.667 --> '100 < root < 110' --> substitute 100 [20250519_005950.]: Entered 'clean_dt'-Function [20250519_005950.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_005950.]: got experimental data [20250519_005950.]: Entered 'clean_dt'-Function [20250519_005950.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_005950.]: got calibration data [20250519_005950.]: ### Starting with regression calculations ### [20250519_005950.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_005950.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005950.]: Logging df_agg: CpG#1 [20250519_005950.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005950.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005950.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005950.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005950.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005950.]: Entered 'hyperbolic_regression'-Function [20250519_005950.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005950.]: Entered 'cubic_regression'-Function [20250519_005950.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005950.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005950.]: Logging df_agg: CpG#2 [20250519_005950.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005950.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005950.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005950.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005950.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005950.]: Entered 'hyperbolic_regression'-Function [20250519_005950.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005950.]: Entered 'cubic_regression'-Function [20250519_005950.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005951.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005951.]: Logging df_agg: CpG#3 [20250519_005951.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005951.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005951.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005951.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005951.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005951.]: Entered 'hyperbolic_regression'-Function [20250519_005951.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005951.]: Entered 'cubic_regression'-Function [20250519_005951.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005951.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005951.]: Logging df_agg: CpG#4 [20250519_005951.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005951.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005951.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005951.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005951.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005951.]: Entered 'hyperbolic_regression'-Function [20250519_005951.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005951.]: Entered 'cubic_regression'-Function [20250519_005951.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005951.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005951.]: Logging df_agg: CpG#5 [20250519_005951.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005951.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005951.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005951.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005951.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005951.]: Entered 'hyperbolic_regression'-Function [20250519_005951.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005952.]: Entered 'cubic_regression'-Function [20250519_005952.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005952.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005952.]: Logging df_agg: CpG#6 [20250519_005952.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005952.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005952.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005952.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005952.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005952.]: Entered 'hyperbolic_regression'-Function [20250519_005952.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005952.]: Entered 'cubic_regression'-Function [20250519_005952.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005952.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005952.]: Logging df_agg: CpG#7 [20250519_005952.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005952.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005952.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005952.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005952.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005952.]: Entered 'hyperbolic_regression'-Function [20250519_005952.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005952.]: Entered 'cubic_regression'-Function [20250519_005952.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005952.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005953.]: Logging df_agg: CpG#8 [20250519_005953.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005953.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005953.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005953.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005953.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005953.]: Entered 'hyperbolic_regression'-Function [20250519_005953.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005953.]: Entered 'cubic_regression'-Function [20250519_005953.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005953.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005953.]: Logging df_agg: CpG#9 [20250519_005953.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005953.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005953.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005953.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005953.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005953.]: Entered 'hyperbolic_regression'-Function [20250519_005953.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005953.]: Entered 'cubic_regression'-Function [20250519_005953.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005953.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005953.]: Logging df_agg: row_means [20250519_005953.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005953.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005953.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_005953.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005953.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005953.]: Entered 'hyperbolic_regression'-Function [20250519_005953.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005954.]: Entered 'cubic_regression'-Function [20250519_005954.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005954.]: ### Starting with regression calculations ### [20250519_005954.]: Entered 'regression_type1'-Function [20250519_005954.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005954.]: Logging df_agg: CpG#1 [20250519_005954.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005954.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005954.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005954.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005954.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005954.]: Entered 'hyperbolic_regression'-Function [20250519_005954.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005955.]: Entered 'cubic_regression'-Function [20250519_005955.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005955.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005955.]: Logging df_agg: CpG#2 [20250519_005955.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005955.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005955.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005955.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005955.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005955.]: Entered 'hyperbolic_regression'-Function [20250519_005955.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005955.]: Entered 'cubic_regression'-Function [20250519_005955.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005955.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005955.]: Logging df_agg: CpG#3 [20250519_005955.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005955.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005955.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005955.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005955.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005955.]: Entered 'hyperbolic_regression'-Function [20250519_005955.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005955.]: Entered 'cubic_regression'-Function [20250519_005955.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005955.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005955.]: Logging df_agg: CpG#4 [20250519_005955.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005955.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005955.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005955.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005955.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005955.]: Entered 'hyperbolic_regression'-Function [20250519_005955.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005956.]: Entered 'cubic_regression'-Function [20250519_005956.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005956.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005956.]: Logging df_agg: CpG#5 [20250519_005956.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005956.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005956.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005956.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005956.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005956.]: Entered 'hyperbolic_regression'-Function [20250519_005956.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005956.]: Entered 'cubic_regression'-Function [20250519_005956.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005956.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005956.]: Logging df_agg: CpG#6 [20250519_005956.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005956.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005956.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005956.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005956.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005956.]: Entered 'hyperbolic_regression'-Function [20250519_005956.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005956.]: Entered 'cubic_regression'-Function [20250519_005956.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005957.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005957.]: Logging df_agg: CpG#7 [20250519_005957.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005957.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005957.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005957.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005957.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_005957.]: Entered 'hyperbolic_regression'-Function [20250519_005957.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005957.]: Entered 'cubic_regression'-Function [20250519_005957.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005957.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005957.]: Logging df_agg: CpG#8 [20250519_005957.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005957.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005957.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005957.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005957.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_005957.]: Entered 'hyperbolic_regression'-Function [20250519_005957.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005957.]: Entered 'cubic_regression'-Function [20250519_005957.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005957.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005957.]: Logging df_agg: CpG#9 [20250519_005957.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005957.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005957.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005957.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005957.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_005957.]: Entered 'hyperbolic_regression'-Function [20250519_005957.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005958.]: Entered 'cubic_regression'-Function [20250519_005958.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005958.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005958.]: Logging df_agg: row_means [20250519_005958.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005958.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005958.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_005958.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005958.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_005958.]: Entered 'hyperbolic_regression'-Function [20250519_005958.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005958.]: Entered 'cubic_regression'-Function [20250519_005958.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005959.]: ### Starting with regression calculations ### [20250519_005959.]: Entered 'regression_type1'-Function [20250519_005959.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005959.]: Logging df_agg: CpG#1 [20250519_005959.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005959.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005959.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005959.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005959.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_005959.]: Entered 'hyperbolic_regression'-Function [20250519_005959.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005959.]: Entered 'cubic_regression'-Function [20250519_005959.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005959.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005959.]: Logging df_agg: CpG#2 [20250519_005959.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005959.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005959.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005959.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005959.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_005959.]: Entered 'hyperbolic_regression'-Function [20250519_005959.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005959.]: Entered 'cubic_regression'-Function [20250519_005959.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005959.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005959.]: Logging df_agg: CpG#3 [20250519_005959.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005959.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005959.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005959.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005959.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_005959.]: Entered 'hyperbolic_regression'-Function [20250519_005959.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005959.]: Entered 'cubic_regression'-Function [20250519_005959.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005959.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005959.]: Logging df_agg: CpG#4 [20250519_005959.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005959.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005959.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005959.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005959.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_005959.]: Entered 'hyperbolic_regression'-Function [20250519_005959.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005959.]: Entered 'cubic_regression'-Function [20250519_005959.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005959.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005959.]: Logging df_agg: CpG#5 [20250519_005959.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005959.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005959.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005959.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005959.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_005959.]: Entered 'hyperbolic_regression'-Function [20250519_005959.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005959.]: Entered 'cubic_regression'-Function [20250519_005959.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005959.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005959.]: Logging df_agg: CpG#6 [20250519_005959.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005959.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005959.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005959.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005959.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_005959.]: Entered 'hyperbolic_regression'-Function [20250519_005959.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005959.]: Entered 'cubic_regression'-Function [20250519_005959.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_005959.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_010000.]: Logging df_agg: CpG#7 [20250519_010000.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010000.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_010000.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_010000.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_010000.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_010000.]: Entered 'hyperbolic_regression'-Function [20250519_010000.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010000.]: Entered 'cubic_regression'-Function [20250519_010000.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010000.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_010000.]: Logging df_agg: CpG#8 [20250519_010000.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010000.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_010000.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_010000.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_010000.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_010000.]: Entered 'hyperbolic_regression'-Function [20250519_010000.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010000.]: Entered 'cubic_regression'-Function [20250519_010000.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010000.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_010000.]: Logging df_agg: CpG#9 [20250519_010000.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010000.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_010000.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_010000.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_010000.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_010000.]: Entered 'hyperbolic_regression'-Function [20250519_010000.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010000.]: Entered 'cubic_regression'-Function [20250519_010000.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010000.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_010000.]: Logging df_agg: row_means [20250519_010000.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010000.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_010000.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_010000.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_010000.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_010000.]: Entered 'hyperbolic_regression'-Function [20250519_010000.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010000.]: Entered 'cubic_regression'-Function [20250519_010000.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010001.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_010001.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_010001.]: Logging df_agg: CpG#1 [20250519_010001.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010001.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_010001.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_010001.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_010001.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_010001.]: Entered 'hyperbolic_regression'-Function [20250519_010001.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010001.]: Entered 'cubic_regression'-Function [20250519_010001.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010001.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_010001.]: Logging df_agg: CpG#2 [20250519_010001.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010001.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_010001.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_010001.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_010001.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_010001.]: Entered 'hyperbolic_regression'-Function [20250519_010001.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010002.]: Entered 'cubic_regression'-Function [20250519_010002.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010002.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_010002.]: Logging df_agg: CpG#3 [20250519_010002.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010002.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_010002.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_010002.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_010002.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_010002.]: Entered 'hyperbolic_regression'-Function [20250519_010002.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010002.]: Entered 'cubic_regression'-Function [20250519_010002.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010002.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_010002.]: Logging df_agg: CpG#4 [20250519_010002.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010002.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_010002.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_010002.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_010002.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_010002.]: Entered 'hyperbolic_regression'-Function [20250519_010002.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010003.]: Entered 'cubic_regression'-Function [20250519_010003.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010003.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_010003.]: Logging df_agg: CpG#5 [20250519_010003.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010003.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_010003.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_010003.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_010003.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_010003.]: Entered 'hyperbolic_regression'-Function [20250519_010003.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010003.]: Entered 'cubic_regression'-Function [20250519_010003.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010003.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_010003.]: Logging df_agg: CpG#6 [20250519_010003.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010003.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_010003.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_010003.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_010003.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_010003.]: Entered 'hyperbolic_regression'-Function [20250519_010003.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010003.]: Entered 'cubic_regression'-Function [20250519_010003.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010003.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_010003.]: Logging df_agg: CpG#7 [20250519_010003.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010003.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_010003.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_010003.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_010003.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_010003.]: Entered 'hyperbolic_regression'-Function [20250519_010003.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010004.]: Entered 'cubic_regression'-Function [20250519_010004.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010004.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_010004.]: Logging df_agg: CpG#8 [20250519_010004.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010004.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_010004.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_010004.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_010004.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_010004.]: Entered 'hyperbolic_regression'-Function [20250519_010004.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010004.]: Entered 'cubic_regression'-Function [20250519_010004.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010004.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_010004.]: Logging df_agg: CpG#9 [20250519_010004.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010004.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_010004.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_010004.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_010004.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_010004.]: Entered 'hyperbolic_regression'-Function [20250519_010004.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010004.]: Entered 'cubic_regression'-Function [20250519_010004.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010004.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_010004.]: Logging df_agg: row_means [20250519_010004.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010004.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_010004.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_010004.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_010004.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_010004.]: Entered 'hyperbolic_regression'-Function [20250519_010004.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010005.]: Entered 'cubic_regression'-Function [20250519_010005.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010005.]: Entered 'clean_dt'-Function [20250519_010005.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_010005.]: got experimental data [20250519_010005.]: Entered 'clean_dt'-Function [20250519_010005.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_010005.]: got calibration data [20250519_010005.]: ### Starting with regression calculations ### [20250519_010005.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_010005.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_010005.]: Logging df_agg: CpG#1 [20250519_010005.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010005.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_010005.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_010005.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_010005.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_010005.]: Entered 'hyperbolic_regression'-Function [20250519_010005.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010006.]: Entered 'cubic_regression'-Function [20250519_010006.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010006.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_010006.]: Logging df_agg: CpG#2 [20250519_010006.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010006.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_010006.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_010006.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_010006.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_010006.]: Entered 'hyperbolic_regression'-Function [20250519_010006.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010006.]: Entered 'cubic_regression'-Function [20250519_010006.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010006.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_010006.]: Logging df_agg: CpG#3 [20250519_010006.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010006.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_010006.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_010006.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_010006.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_010006.]: Entered 'hyperbolic_regression'-Function [20250519_010006.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010006.]: Entered 'cubic_regression'-Function [20250519_010006.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010007.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_010007.]: Logging df_agg: CpG#4 [20250519_010007.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010007.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_010007.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_010007.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_010007.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_010007.]: Entered 'hyperbolic_regression'-Function [20250519_010007.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010007.]: Entered 'cubic_regression'-Function [20250519_010007.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010007.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_010007.]: Logging df_agg: CpG#5 [20250519_010007.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010007.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_010007.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_010007.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_010007.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_010007.]: Entered 'hyperbolic_regression'-Function [20250519_010007.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010007.]: Entered 'cubic_regression'-Function [20250519_010007.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010007.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_010007.]: Logging df_agg: CpG#6 [20250519_010007.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010007.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_010007.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_010007.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_010007.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_010007.]: Entered 'hyperbolic_regression'-Function [20250519_010007.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010008.]: Entered 'cubic_regression'-Function [20250519_010008.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010008.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_010008.]: Logging df_agg: CpG#7 [20250519_010008.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010008.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_010008.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_010008.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_010008.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_010008.]: Entered 'hyperbolic_regression'-Function [20250519_010008.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010008.]: Entered 'cubic_regression'-Function [20250519_010008.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010008.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_010008.]: Logging df_agg: CpG#8 [20250519_010008.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010008.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_010008.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_010008.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_010008.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_010008.]: Entered 'hyperbolic_regression'-Function [20250519_010008.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010008.]: Entered 'cubic_regression'-Function [20250519_010008.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010008.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_010008.]: Logging df_agg: CpG#9 [20250519_010008.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010008.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_010008.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_010008.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_010008.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_010008.]: Entered 'hyperbolic_regression'-Function [20250519_010008.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010009.]: Entered 'cubic_regression'-Function [20250519_010009.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010009.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_010009.]: Logging df_agg: row_means [20250519_010009.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010009.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_010009.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_010009.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_010009.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_010009.]: Entered 'hyperbolic_regression'-Function [20250519_010009.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010009.]: Entered 'cubic_regression'-Function [20250519_010009.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010010.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_010010.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_010010.]: Logging df_agg: CpG#1 [20250519_010010.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010010.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_010010.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_010010.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_010010.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_010010.]: Entered 'hyperbolic_regression'-Function [20250519_010010.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010010.]: Entered 'cubic_regression'-Function [20250519_010010.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010010.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_010010.]: Logging df_agg: CpG#2 [20250519_010010.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010010.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_010010.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_010010.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_010010.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_010010.]: Entered 'hyperbolic_regression'-Function [20250519_010010.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010010.]: Entered 'cubic_regression'-Function [20250519_010010.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010010.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_010010.]: Logging df_agg: CpG#3 [20250519_010010.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010010.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_010010.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_010010.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_010010.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_010010.]: Entered 'hyperbolic_regression'-Function [20250519_010010.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010011.]: Entered 'cubic_regression'-Function [20250519_010011.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010011.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_010011.]: Logging df_agg: CpG#4 [20250519_010011.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010011.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_010011.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_010011.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_010011.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_010011.]: Entered 'hyperbolic_regression'-Function [20250519_010011.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010011.]: Entered 'cubic_regression'-Function [20250519_010011.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010011.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_010011.]: Logging df_agg: CpG#5 [20250519_010011.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010011.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_010011.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_010011.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_010011.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_010011.]: Entered 'hyperbolic_regression'-Function [20250519_010011.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010011.]: Entered 'cubic_regression'-Function [20250519_010011.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010012.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_010012.]: Logging df_agg: CpG#6 [20250519_010012.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010012.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_010012.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_010012.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_010012.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_010012.]: Entered 'hyperbolic_regression'-Function [20250519_010012.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010012.]: Entered 'cubic_regression'-Function [20250519_010012.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010012.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_010012.]: Logging df_agg: CpG#7 [20250519_010012.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010012.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_010012.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_010012.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_010012.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_010012.]: Entered 'hyperbolic_regression'-Function [20250519_010012.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010012.]: Entered 'cubic_regression'-Function [20250519_010012.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010012.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_010012.]: Logging df_agg: CpG#8 [20250519_010012.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010012.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_010012.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_010012.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_010012.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_010012.]: Entered 'hyperbolic_regression'-Function [20250519_010012.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010013.]: Entered 'cubic_regression'-Function [20250519_010013.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010013.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_010013.]: Logging df_agg: CpG#9 [20250519_010013.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010013.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_010013.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_010013.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_010013.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_010013.]: Entered 'hyperbolic_regression'-Function [20250519_010013.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010013.]: Entered 'cubic_regression'-Function [20250519_010013.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010013.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_010013.]: Logging df_agg: row_means [20250519_010013.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010013.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_010013.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_010013.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_010013.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_010013.]: Entered 'hyperbolic_regression'-Function [20250519_010013.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010013.]: Entered 'cubic_regression'-Function [20250519_010013.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010014.]: Entered 'solving_equations'-Function [20250519_010014.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 0 [20250519_010014.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 14.1480167109277 [20250519_010014.]: Samplename: 12.5 Root: 14.148 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1398443790446 [20250519_010014.]: Samplename: 25 Root: 26.14 --> Root in between the borders! Added to results. Hyperbolic solved: 39.3762039523382 [20250519_010014.]: Samplename: 37.5 Root: 39.376 --> Root in between the borders! Added to results. Hyperbolic solved: 52.9476244028783 [20250519_010014.]: Samplename: 50 Root: 52.948 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4195105873419 [20250519_010014.]: Samplename: 62.5 Root: 65.42 --> Root in between the borders! Added to results. Hyperbolic solved: 74.433837698331 [20250519_010014.]: Samplename: 75 Root: 74.434 --> Root in between the borders! Added to results. Hyperbolic solved: 80.5559264925808 [20250519_010014.]: Samplename: 87.5 Root: 80.556 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_010014.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_010014.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 0 [20250519_010014.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 10.7812303822548 [20250519_010014.]: Samplename: 12.5 Root: 10.781 --> Root in between the borders! Added to results. Hyperbolic solved: 26.0648314401535 [20250519_010014.]: Samplename: 25 Root: 26.065 --> Root in between the borders! Added to results. Hyperbolic solved: 35.1980950192158 [20250519_010014.]: Samplename: 37.5 Root: 35.198 --> Root in between the borders! Added to results. Hyperbolic solved: 47.9203836090837 [20250519_010014.]: Samplename: 50 Root: 47.92 --> Root in between the borders! Added to results. Hyperbolic solved: 67.275750545092 [20250519_010014.]: Samplename: 62.5 Root: 67.276 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7278143684244 [20250519_010014.]: Samplename: 75 Root: 75.728 --> Root in between the borders! Added to results. Hyperbolic solved: 84.1258428690749 [20250519_010014.]: Samplename: 87.5 Root: 84.126 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_010014.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_010014.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 0 [20250519_010014.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 10.8467198180169 [20250519_010014.]: Samplename: 12.5 Root: 10.847 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1449700612555 [20250519_010014.]: Samplename: 25 Root: 26.145 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2865760013773 [20250519_010014.]: Samplename: 37.5 Root: 37.287 --> Root in between the borders! Added to results. Hyperbolic solved: 51.4114078772697 [20250519_010014.]: Samplename: 50 Root: 51.411 --> Root in between the borders! Added to results. Hyperbolic solved: 65.0139634978039 [20250519_010014.]: Samplename: 62.5 Root: 65.014 --> Root in between the borders! Added to results. Hyperbolic solved: 76.9921394830877 [20250519_010014.]: Samplename: 75 Root: 76.992 --> Root in between the borders! Added to results. Hyperbolic solved: 79.6808818589171 [20250519_010014.]: Samplename: 87.5 Root: 79.681 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_010014.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_010014.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 0 [20250519_010014.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 13.2494913489925 [20250519_010014.]: Samplename: 12.5 Root: 13.249 --> Root in between the borders! Added to results. Hyperbolic solved: 25.0914701740206 [20250519_010014.]: Samplename: 25 Root: 25.091 --> Root in between the borders! Added to results. Hyperbolic solved: 38.8081740444332 [20250519_010014.]: Samplename: 37.5 Root: 38.808 --> Root in between the borders! Added to results. Hyperbolic solved: 49.1133034150896 [20250519_010014.]: Samplename: 50 Root: 49.113 --> Root in between the borders! Added to results. Hyperbolic solved: 67.573566028728 [20250519_010014.]: Samplename: 62.5 Root: 67.574 --> Root in between the borders! Added to results. Hyperbolic solved: 73.7655861480302 [20250519_010014.]: Samplename: 75 Root: 73.766 --> Root in between the borders! Added to results. Hyperbolic solved: 82.0404940898918 [20250519_010014.]: Samplename: 87.5 Root: 82.04 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_010014.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_010014.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 0 [20250519_010014.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 8.36101271071658 [20250519_010014.]: Samplename: 12.5 Root: 8.361 --> Root in between the borders! Added to results. Hyperbolic solved: 23.0724671456609 [20250519_010014.]: Samplename: 25 Root: 23.072 --> Root in between the borders! Added to results. Hyperbolic solved: 36.9926902689013 [20250519_010014.]: Samplename: 37.5 Root: 36.993 --> Root in between the borders! Added to results. Hyperbolic solved: 50.9902012556783 [20250519_010014.]: Samplename: 50 Root: 50.99 --> Root in between the borders! Added to results. Hyperbolic solved: 62.7269420395599 [20250519_010014.]: Samplename: 62.5 Root: 62.727 --> Root in between the borders! Added to results. Hyperbolic solved: 76.6726263557269 [20250519_010014.]: Samplename: 75 Root: 76.673 --> Root in between the borders! Added to results. Hyperbolic solved: 86.2959101486824 [20250519_010014.]: Samplename: 87.5 Root: 86.296 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_010014.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_010014.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 0 [20250519_010014.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 11.8139244542506 [20250519_010014.]: Samplename: 12.5 Root: 11.814 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5330423964606 [20250519_010014.]: Samplename: 25 Root: 26.533 --> Root in between the borders! Added to results. Hyperbolic solved: 35.365455365759 [20250519_010014.]: Samplename: 37.5 Root: 35.365 --> Root in between the borders! Added to results. Hyperbolic solved: 50.1054345020203 [20250519_010014.]: Samplename: 50 Root: 50.105 --> Root in between the borders! Added to results. Hyperbolic solved: 64.9683746571925 [20250519_010014.]: Samplename: 62.5 Root: 64.968 --> Root in between the borders! Added to results. Hyperbolic solved: 73.6331726521876 [20250519_010014.]: Samplename: 75 Root: 73.633 --> Root in between the borders! Added to results. Hyperbolic solved: 86.9938602772804 [20250519_010014.]: Samplename: 87.5 Root: 86.994 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_010014.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_010014.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 0 [20250519_010014.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 11.7880491100164 [20250519_010014.]: Samplename: 12.5 Root: 11.788 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1959234034328 [20250519_010014.]: Samplename: 25 Root: 26.196 --> Root in between the borders! Added to results. Hyperbolic solved: 39.1978567589215 [20250519_010014.]: Samplename: 37.5 Root: 39.198 --> Root in between the borders! Added to results. Hyperbolic solved: 54.3513505291649 [20250519_010014.]: Samplename: 50 Root: 54.351 --> Root in between the borders! Added to results. Hyperbolic solved: 66.0567953704493 [20250519_010014.]: Samplename: 62.5 Root: 66.057 --> Root in between the borders! Added to results. Hyperbolic solved: 75.1900867269869 [20250519_010014.]: Samplename: 75 Root: 75.19 --> Root in between the borders! Added to results. Hyperbolic solved: 78.6051660349008 [20250519_010014.]: Samplename: 87.5 Root: 78.605 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_010014.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_010014.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 0 [20250519_010014.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 7.27382484148278 [20250519_010014.]: Samplename: 12.5 Root: 7.274 --> Root in between the borders! Added to results. Hyperbolic solved: 24.9765598282779 [20250519_010014.]: Samplename: 25 Root: 24.977 --> Root in between the borders! Added to results. Hyperbolic solved: 34.028313807905 [20250519_010014.]: Samplename: 37.5 Root: 34.028 --> Root in between the borders! Added to results. Hyperbolic solved: 52.2946423509726 [20250519_010014.]: Samplename: 50 Root: 52.295 --> Root in between the borders! Added to results. Hyperbolic solved: 65.0742432627639 [20250519_010014.]: Samplename: 62.5 Root: 65.074 --> Root in between the borders! Added to results. Hyperbolic solved: 78.3047550247862 [20250519_010014.]: Samplename: 75 Root: 78.305 --> Root in between the borders! Added to results. Hyperbolic solved: 81.0501991298613 [20250519_010014.]: Samplename: 87.5 Root: 81.05 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_010014.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_010014.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 0 [20250519_010014.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 12.2068234201026 [20250519_010014.]: Samplename: 12.5 Root: 12.207 --> Root in between the borders! Added to results. Hyperbolic solved: 28.0688737636578 [20250519_010014.]: Samplename: 25 Root: 28.069 --> Root in between the borders! Added to results. Hyperbolic solved: 37.6661823177513 [20250519_010014.]: Samplename: 37.5 Root: 37.666 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3684233460174 [20250519_010014.]: Samplename: 50 Root: 52.368 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8636916649907 [20250519_010014.]: Samplename: 62.5 Root: 64.864 --> Root in between the borders! Added to results. Hyperbolic solved: 74.2551330907062 [20250519_010014.]: Samplename: 75 Root: 74.255 --> Root in between the borders! Added to results. Hyperbolic solved: 83.9343288872042 [20250519_010014.]: Samplename: 87.5 Root: 83.934 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_010014.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_010014.]: Solving hyperbolic regression for row_means Hyperbolic solved: 0.000228160853766634 [20250519_010014.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 11.1587839347378 [20250519_010014.]: Samplename: 12.5 Root: 11.159 --> Root in between the borders! Added to results. Hyperbolic solved: 25.8570776700364 [20250519_010014.]: Samplename: 25 Root: 25.857 --> Root in between the borders! Added to results. Hyperbolic solved: 37.065004454281 [20250519_010014.]: Samplename: 37.5 Root: 37.065 --> Root in between the borders! Added to results. Hyperbolic solved: 51.1881763075298 [20250519_010014.]: Samplename: 50 Root: 51.188 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4439754555105 [20250519_010014.]: Samplename: 62.5 Root: 65.444 --> Root in between the borders! Added to results. Hyperbolic solved: 75.3005883283082 [20250519_010014.]: Samplename: 75 Root: 75.301 --> Root in between the borders! Added to results. Hyperbolic solved: 82.6591025877361 [20250519_010014.]: Samplename: 87.5 Root: 82.659 --> Root in between the borders! Added to results. Hyperbolic solved: 100.000153506599 [20250519_010014.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 [20250519_010014.]: ### Starting with regression calculations ### [20250519_010014.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_010014.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 14.1480167109277, 26.1398443790446, 39.3762039523382, 52.9476244028783, 65.4195105873419, 74.433837698331, 80.5559264925808, 100)c(0, 1.6480167109277, 1.1398443790446, 1.8762039523382, 2.9476244028783, 2.9195105873419, 0.566162301668996, 6.9440735074192, 0)c(NA, 13.1841336874216, 4.5593775161784, 5.00321053956854, 5.89524880575659, 4.67121693974705, 0.754883068891995, 7.93608400847909, 0) [20250519_010014.]: Logging df_agg: CpG#1 [20250519_010014.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010014.]: c(0, 14.1480167109277, 26.1398443790446, 39.3762039523382, 52.9476244028783, 65.4195105873419, 74.433837698331, 80.5559264925808, 100)[20250519_010014.]: c(0, 1.6480167109277, 1.1398443790446, 1.8762039523382, 2.9476244028783, 2.9195105873419, 0.566162301668996, 6.9440735074192, 0)[20250519_010014.]: c(NA, 13.1841336874216, 4.5593775161784, 5.00321053956854, 5.89524880575659, 4.67121693974705, 0.754883068891995, 7.93608400847909, 0) [20250519_010014.]: Entered 'hyperbolic_regression'-Function [20250519_010014.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010015.]: Entered 'cubic_regression'-Function [20250519_010015.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010015.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.7812303822548, 26.0648314401535, 35.1980950192158, 47.9203836090837, 67.275750545092, 75.7278143684244, 84.1258428690749, 100)c(0, 1.7187696177452, 1.0648314401535, 2.3019049807842, 2.0796163909163, 4.775750545092, 0.727814368424404, 3.3741571309251, 0)c(NA, 13.7501569419616, 4.259325760614, 6.1384132820912, 4.1592327818326, 7.64120087214719, 0.970419157899206, 3.85617957820011, 0) [20250519_010015.]: Logging df_agg: CpG#2 [20250519_010015.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010015.]: c(0, 10.7812303822548, 26.0648314401535, 35.1980950192158, 47.9203836090837, 67.275750545092, 75.7278143684244, 84.1258428690749, 100)[20250519_010015.]: c(0, 1.7187696177452, 1.0648314401535, 2.3019049807842, 2.0796163909163, 4.775750545092, 0.727814368424404, 3.3741571309251, 0)[20250519_010015.]: c(NA, 13.7501569419616, 4.259325760614, 6.1384132820912, 4.1592327818326, 7.64120087214719, 0.970419157899206, 3.85617957820011, 0) [20250519_010015.]: Entered 'hyperbolic_regression'-Function [20250519_010015.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010015.]: Entered 'cubic_regression'-Function [20250519_010015.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010015.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.8467198180169, 26.1449700612555, 37.2865760013773, 51.4114078772697, 65.0139634978039, 76.9921394830877, 79.6808818589171, 100)c(0, 1.6532801819831, 1.1449700612555, 0.213423998622702, 1.4114078772697, 2.5139634978039, 1.9921394830877, 7.8191181410829, 0)c(NA, 13.2262414558648, 4.579880245022, 0.569130662993871, 2.82281575453941, 4.02234159648624, 2.65618597745026, 8.93613501838045, 0) [20250519_010015.]: Logging df_agg: CpG#3 [20250519_010015.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010015.]: c(0, 10.8467198180169, 26.1449700612555, 37.2865760013773, 51.4114078772697, 65.0139634978039, 76.9921394830877, 79.6808818589171, 100)[20250519_010015.]: c(0, 1.6532801819831, 1.1449700612555, 0.213423998622702, 1.4114078772697, 2.5139634978039, 1.9921394830877, 7.8191181410829, 0)[20250519_010015.]: c(NA, 13.2262414558648, 4.579880245022, 0.569130662993871, 2.82281575453941, 4.02234159648624, 2.65618597745026, 8.93613501838045, 0) [20250519_010015.]: Entered 'hyperbolic_regression'-Function [20250519_010015.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010016.]: Entered 'cubic_regression'-Function [20250519_010016.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010016.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 13.2494913489925, 25.0914701740206, 38.8081740444332, 49.1133034150896, 67.573566028728, 73.7655861480302, 82.0404940898918, 100)c(0, 0.7494913489925, 0.0914701740205999, 1.3081740444332, 0.886696584910403, 5.073566028728, 1.2344138519698, 5.45950591010821, 0)c(NA, 5.99593079194, 0.365880696082399, 3.48846411848854, 1.77339316982081, 8.11770564596479, 1.64588513595974, 6.23943532583795, 0) [20250519_010016.]: Logging df_agg: CpG#4 [20250519_010016.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010016.]: c(0, 13.2494913489925, 25.0914701740206, 38.8081740444332, 49.1133034150896, 67.573566028728, 73.7655861480302, 82.0404940898918, 100)[20250519_010016.]: c(0, 0.7494913489925, 0.0914701740205999, 1.3081740444332, 0.886696584910403, 5.073566028728, 1.2344138519698, 5.45950591010821, 0)[20250519_010016.]: c(NA, 5.99593079194, 0.365880696082399, 3.48846411848854, 1.77339316982081, 8.11770564596479, 1.64588513595974, 6.23943532583795, 0) [20250519_010016.]: Entered 'hyperbolic_regression'-Function [20250519_010016.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010016.]: Entered 'cubic_regression'-Function [20250519_010016.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010016.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.36101271071658, 23.0724671456609, 36.9926902689013, 50.9902012556783, 62.7269420395599, 76.6726263557269, 86.2959101486824, 100)c(0, 4.13898728928342, 1.9275328543391, 0.507309731098701, 0.990201255678301, 0.226942039559901, 1.6726263557269, 1.2040898513176, 0)c(NA, 33.1118983142674, 7.71013141735639, 1.35282594959654, 1.9804025113566, 0.363107263295842, 2.23016847430254, 1.37610268722012, 0) [20250519_010016.]: Logging df_agg: CpG#5 [20250519_010016.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010016.]: c(0, 8.36101271071658, 23.0724671456609, 36.9926902689013, 50.9902012556783, 62.7269420395599, 76.6726263557269, 86.2959101486824, 100)[20250519_010016.]: c(0, 4.13898728928342, 1.9275328543391, 0.507309731098701, 0.990201255678301, 0.226942039559901, 1.6726263557269, 1.2040898513176, 0)[20250519_010016.]: c(NA, 33.1118983142674, 7.71013141735639, 1.35282594959654, 1.9804025113566, 0.363107263295842, 2.23016847430254, 1.37610268722012, 0) [20250519_010016.]: Entered 'hyperbolic_regression'-Function [20250519_010016.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010016.]: Entered 'cubic_regression'-Function [20250519_010016.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010016.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.8139244542506, 26.5330423964606, 35.365455365759, 50.1054345020203, 64.9683746571925, 73.6331726521876, 86.9938602772804, 100)c(0, 0.6860755457494, 1.5330423964606, 2.134544634241, 0.105434502020302, 2.4683746571925, 1.3668273478124, 0.506139722719595, 0)c(NA, 5.4886043659952, 6.13216958584241, 5.69211902464267, 0.210869004040603, 3.949399451508, 1.82243646374987, 0.578445397393823, 0) [20250519_010016.]: Logging df_agg: CpG#6 [20250519_010016.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010016.]: c(0, 11.8139244542506, 26.5330423964606, 35.365455365759, 50.1054345020203, 64.9683746571925, 73.6331726521876, 86.9938602772804, 100)[20250519_010016.]: c(0, 0.6860755457494, 1.5330423964606, 2.134544634241, 0.105434502020302, 2.4683746571925, 1.3668273478124, 0.506139722719595, 0)[20250519_010016.]: c(NA, 5.4886043659952, 6.13216958584241, 5.69211902464267, 0.210869004040603, 3.949399451508, 1.82243646374987, 0.578445397393823, 0) [20250519_010016.]: Entered 'hyperbolic_regression'-Function [20250519_010016.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010017.]: Entered 'cubic_regression'-Function [20250519_010017.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010017.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.7880491100164, 26.1959234034328, 39.1978567589215, 54.3513505291649, 66.0567953704493, 75.1900867269869, 78.6051660349008, 100)c(0, 0.7119508899836, 1.1959234034328, 1.6978567589215, 4.3513505291649, 3.55679537044929, 0.190086726986905, 8.8948339650992, 0)c(NA, 5.6956071198688, 4.7836936137312, 4.52761802379068, 8.70270105832979, 5.69087259271887, 0.253448969315874, 10.1655245315419, 0) [20250519_010017.]: Logging df_agg: CpG#7 [20250519_010017.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010017.]: c(0, 11.7880491100164, 26.1959234034328, 39.1978567589215, 54.3513505291649, 66.0567953704493, 75.1900867269869, 78.6051660349008, 100)[20250519_010017.]: c(0, 0.7119508899836, 1.1959234034328, 1.6978567589215, 4.3513505291649, 3.55679537044929, 0.190086726986905, 8.8948339650992, 0)[20250519_010017.]: c(NA, 5.6956071198688, 4.7836936137312, 4.52761802379068, 8.70270105832979, 5.69087259271887, 0.253448969315874, 10.1655245315419, 0) [20250519_010017.]: Entered 'hyperbolic_regression'-Function [20250519_010017.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010017.]: Entered 'cubic_regression'-Function [20250519_010017.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010017.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 7.27382484148278, 24.9765598282779, 34.028313807905, 52.2946423509726, 65.0742432627639, 78.3047550247862, 81.0501991298613, 100)c(0, 5.22617515851722, 0.0234401717221004, 3.471686192095, 2.2946423509726, 2.5742432627639, 3.3047550247862, 6.4498008701387, 0)c(NA, 41.8094012681378, 0.0937606868884018, 9.25782984558666, 4.5892847019452, 4.11878922042224, 4.40634003304827, 7.37120099444423, 0) [20250519_010017.]: Logging df_agg: CpG#8 [20250519_010017.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010017.]: c(0, 7.27382484148278, 24.9765598282779, 34.028313807905, 52.2946423509726, 65.0742432627639, 78.3047550247862, 81.0501991298613, 100)[20250519_010017.]: c(0, 5.22617515851722, 0.0234401717221004, 3.471686192095, 2.2946423509726, 2.5742432627639, 3.3047550247862, 6.4498008701387, 0)[20250519_010017.]: c(NA, 41.8094012681378, 0.0937606868884018, 9.25782984558666, 4.5892847019452, 4.11878922042224, 4.40634003304827, 7.37120099444423, 0) [20250519_010017.]: Entered 'hyperbolic_regression'-Function [20250519_010017.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010017.]: Entered 'cubic_regression'-Function [20250519_010018.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010018.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.2068234201026, 28.0688737636578, 37.6661823177513, 52.3684233460174, 64.8636916649907, 74.2551330907062, 83.9343288872042, 100)c(0, 0.2931765798974, 3.0688737636578, 0.1661823177513, 2.3684233460174, 2.3636916649907, 0.744866909293805, 3.5656711127958, 0)c(NA, 2.3454126391792, 12.2754950546312, 0.4431528473368, 4.7368466920348, 3.78190666398511, 0.993155879058406, 4.07505270033805, 0) [20250519_010018.]: Logging df_agg: CpG#9 [20250519_010018.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010018.]: c(0, 12.2068234201026, 28.0688737636578, 37.6661823177513, 52.3684233460174, 64.8636916649907, 74.2551330907062, 83.9343288872042, 100)[20250519_010018.]: c(0, 0.2931765798974, 3.0688737636578, 0.1661823177513, 2.3684233460174, 2.3636916649907, 0.744866909293805, 3.5656711127958, 0)[20250519_010018.]: c(NA, 2.3454126391792, 12.2754950546312, 0.4431528473368, 4.7368466920348, 3.78190666398511, 0.993155879058406, 4.07505270033805, 0) [20250519_010018.]: Entered 'hyperbolic_regression'-Function [20250519_010018.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010018.]: Entered 'cubic_regression'-Function [20250519_010018.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010018.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.000228160853766634, 11.1587839347378, 25.8570776700364, 37.065004454281, 51.1881763075298, 65.4439754555105, 75.3005883283082, 82.6591025877361, 100)c(0.000228160853766634, 1.3412160652622, 0.8570776700364, 0.434995545718998, 1.1881763075298, 2.9439754555105, 0.300588328308194, 4.8408974122639, 0)c(NA, 10.7297285220976, 3.4283106801456, 1.15998812191733, 2.3763526150596, 4.7103607288168, 0.400784437744259, 5.53245418544446, 0) [20250519_010018.]: Logging df_agg: row_means [20250519_010018.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010018.]: c(0.000228160853766634, 11.1587839347378, 25.8570776700364, 37.065004454281, 51.1881763075298, 65.4439754555105, 75.3005883283082, 82.6591025877361, 100)[20250519_010018.]: c(0.000228160853766634, 1.3412160652622, 0.8570776700364, 0.434995545718998, 1.1881763075298, 2.9439754555105, 0.300588328308194, 4.8408974122639, 0)[20250519_010018.]: c(NA, 10.7297285220976, 3.4283106801456, 1.15998812191733, 2.3763526150596, 4.7103607288168, 0.400784437744259, 5.53245418544446, 0) [20250519_010018.]: Entered 'hyperbolic_regression'-Function [20250519_010018.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010018.]: Entered 'cubic_regression'-Function [20250519_010018.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010019.]: Entered 'solving_equations'-Function [20250519_010019.]: Solving cubic regression for CpG#1 Coefficients: 0Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20250519_010019.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -7.30533333333333Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20250519_010019.]: Samplename: 12.5 Root: 4.661 --> Root in between the borders! Added to results. Coefficients: -14.352Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20250519_010019.]: Samplename: 25 Root: 9.352 --> Root in between the borders! Added to results. Coefficients: -23.244Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20250519_010019.]: Samplename: 37.5 Root: 15.59 --> Root in between the borders! Added to results. Coefficients: -33.8645Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20250519_010019.]: Samplename: 50 Root: 23.601 --> Root in between the borders! Added to results. Coefficients: -45.318Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20250519_010019.]: Samplename: 62.5 Root: 33.121 --> Root in between the borders! Added to results. Coefficients: -54.857Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20250519_010019.]: Samplename: 75 Root: 42.001 --> Root in between the borders! Added to results. Coefficients: -62.062Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20250519_010019.]: Samplename: 87.5 Root: 49.51 --> Root in between the borders! Added to results. Coefficients: -90.01Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 [20250519_010019.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 [20250519_010019.]: Solving cubic regression for CpG#2 Coefficients: 0Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20250519_010019.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -6.05666666666666Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20250519_010019.]: Samplename: 12.5 Root: 8.415 --> Root in between the borders! Added to results. Coefficients: -15.656Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20250519_010019.]: Samplename: 25 Root: 21.016 --> Root in between the borders! Added to results. Coefficients: -22.054Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20250519_010019.]: Samplename: 37.5 Root: 28.984 --> Root in between the borders! Added to results. Coefficients: -31.945Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20250519_010019.]: Samplename: 50 Root: 40.726 --> Root in between the borders! Added to results. Coefficients: -49.68Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20250519_010019.]: Samplename: 62.5 Root: 60.322 --> Root in between the borders! Added to results. Coefficients: -58.6825Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20250519_010019.]: Samplename: 75 Root: 69.671 --> Root in between the borders! Added to results. Coefficients: -68.5533333333333Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20250519_010019.]: Samplename: 87.5 Root: 79.529 --> Root in between the borders! Added to results. Coefficients: -90.294Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 [20250519_010019.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 [20250519_010019.]: Solving cubic regression for CpG#3 Coefficients: 0Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20250519_010019.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -5.67Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20250519_010019.]: Samplename: 12.5 Root: 5.75 --> Root in between the borders! Added to results. Coefficients: -14.526Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20250519_010019.]: Samplename: 25 Root: 15.012 --> Root in between the borders! Added to results. Coefficients: -21.71Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20250519_010019.]: Samplename: 37.5 Root: 22.804 --> Root in between the borders! Added to results. Coefficients: -31.8725Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20250519_010019.]: Samplename: 50 Root: 34.308 --> Root in between the borders! Added to results. Coefficients: -42.986Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20250519_010019.]: Samplename: 62.5 Root: 47.638 --> Root in between the borders! Added to results. Coefficients: -54.0725Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20250519_010019.]: Samplename: 75 Root: 61.877 --> Root in between the borders! Added to results. Coefficients: -56.7533333333333Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20250519_010019.]: Samplename: 87.5 Root: 65.486 --> Root in between the borders! Added to results. Coefficients: -79.762Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 [20250519_010019.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 [20250519_010019.]: Solving cubic regression for CpG#4 Coefficients: 0Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20250519_010019.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -7.65533333333333Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20250519_010019.]: Samplename: 12.5 Root: 6.614 --> Root in between the borders! Added to results. Coefficients: -15.206Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20250519_010019.]: Samplename: 25 Root: 13.372 --> Root in between the borders! Added to results. Coefficients: -24.93Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20250519_010019.]: Samplename: 37.5 Root: 22.462 --> Root in between the borders! Added to results. Coefficients: -33.0395Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20250519_010019.]: Samplename: 50 Root: 30.424 --> Root in between the borders! Added to results. Coefficients: -49.658Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20250519_010019.]: Samplename: 62.5 Root: 48.07 --> Root in between the borders! Added to results. Coefficients: -55.942Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20250519_010019.]: Samplename: 75 Root: 55.318 --> Root in between the borders! Added to results. Coefficients: -64.9953333333333Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20250519_010019.]: Samplename: 87.5 Root: 66.469 --> Root in between the borders! Added to results. Coefficients: -87.724Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 [20250519_010019.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_010019.]: Solving cubic regression for CpG#5 Coefficients: 0Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_010019.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -4.144Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_010019.]: Samplename: 12.5 Root: 10.457 --> Root in between the borders! Added to results. Coefficients: -12.102Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_010019.]: Samplename: 25 Root: 26.323 --> Root in between the borders! Added to results. Coefficients: -20.536Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_010019.]: Samplename: 37.5 Root: 39.937 --> Root in between the borders! Added to results. Coefficients: -30.0715Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_010019.]: Samplename: 50 Root: 53.063 --> Root in between the borders! Added to results. Coefficients: -39.034Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_010019.]: Samplename: 62.5 Root: 63.959 --> Root in between the borders! Added to results. Coefficients: -51.059Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_010019.]: Samplename: 75 Root: 77.051 --> Root in between the borders! Added to results. Coefficients: -60.3906666666667Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_010019.]: Samplename: 87.5 Root: 86.31 --> Root in between the borders! Added to results. Coefficients: -75.446Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_010019.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_010019.]: Solving cubic regression for CpG#6 Coefficients: 0Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20250519_010019.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -6.54266666666667Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20250519_010019.]: Samplename: 12.5 Root: 8.15 --> Root in between the borders! Added to results. Coefficients: -15.692Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20250519_010019.]: Samplename: 25 Root: 19.28 --> Root in between the borders! Added to results. Coefficients: -21.804Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20250519_010019.]: Samplename: 37.5 Root: 26.553 --> Root in between the borders! Added to results. Coefficients: -33.2485Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20250519_010019.]: Samplename: 50 Root: 39.845 --> Root in between the borders! Added to results. Coefficients: -46.704Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20250519_010019.]: Samplename: 62.5 Root: 54.973 --> Root in between the borders! Added to results. Coefficients: -55.636Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20250519_010019.]: Samplename: 75 Root: 64.743 --> Root in between the borders! Added to results. Coefficients: -71.3493333333333Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20250519_010019.]: Samplename: 87.5 Root: 81.445 --> Root in between the borders! Added to results. Coefficients: -89.46Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 [20250519_010019.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_010019.]: Solving cubic regression for CpG#7 Coefficients: 0Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20250519_010019.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -4.18066666666667Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20250519_010019.]: Samplename: 12.5 Root: 3.662 --> Root in between the borders! Added to results. Coefficients: -10.05Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20250519_010019.]: Samplename: 25 Root: 9.013 --> Root in between the borders! Added to results. Coefficients: -16.236Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20250519_010019.]: Samplename: 37.5 Root: 14.96 --> Root in between the borders! Added to results. Coefficients: -24.8165Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20250519_010019.]: Samplename: 50 Root: 23.842 --> Root in between the borders! Added to results. Coefficients: -32.75Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20250519_010019.]: Samplename: 62.5 Root: 32.894 --> Root in between the borders! Added to results. Coefficients: -39.954Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20250519_010019.]: Samplename: 75 Root: 42.068 --> Root in between the borders! Added to results. Coefficients: -42.9206666666667Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20250519_010019.]: Samplename: 87.5 Root: 46.196 --> Root in between the borders! Added to results. Coefficients: -66.008Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 [20250519_010019.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_010019.]: Solving cubic regression for CpG#8 Coefficients: 0Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_010019.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -4.35066666666667Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_010019.]: Samplename: 12.5 Root: 8.961 --> Root in between the borders! Added to results. Coefficients: -15.834Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_010019.]: Samplename: 25 Root: 28.16 --> Root in between the borders! Added to results. Coefficients: -22.254Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_010019.]: Samplename: 37.5 Root: 37.189 --> Root in between the borders! Added to results. Coefficients: -36.529Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_010019.]: Samplename: 50 Root: 54.661 --> Root in between the borders! Added to results. Coefficients: -47.73Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_010019.]: Samplename: 62.5 Root: 66.642 --> Root in between the borders! Added to results. Coefficients: -60.5715Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_010019.]: Samplename: 75 Root: 79.079 --> Root in between the borders! Added to results. Coefficients: -63.414Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_010019.]: Samplename: 87.5 Root: 81.681 --> Root in between the borders! Added to results. Coefficients: -84.964Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_010019.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_010019.]: Solving cubic regression for CpG#9 Coefficients: 0Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20250519_010019.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -5.406Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20250519_010019.]: Samplename: 12.5 Root: 3.608 --> Root in between the borders! Added to results. Coefficients: -13.716Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20250519_010019.]: Samplename: 25 Root: 9.37 --> Root in between the borders! Added to results. Coefficients: -19.634Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20250519_010019.]: Samplename: 37.5 Root: 13.653 --> Root in between the borders! Added to results. Coefficients: -30.406Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20250519_010019.]: Samplename: 50 Root: 21.897 --> Root in between the borders! Added to results. Coefficients: -41.696Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20250519_010019.]: Samplename: 62.5 Root: 31.298 --> Root in between the borders! Added to results. Coefficients: -51.9135Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20250519_010019.]: Samplename: 75 Root: 40.689 --> Root in between the borders! Added to results. Coefficients: -64.5026666666667Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20250519_010019.]: Samplename: 87.5 Root: 53.907 --> Root in between the borders! Added to results. Coefficients: -92Coefficients: 1.52Coefficients: -0.006Coefficients: 0 [20250519_010019.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_010019.]: Solving cubic regression for row_means Coefficients: -0.000111111111110951Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20250519_010019.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -5.70137037037037Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20250519_010019.]: Samplename: 12.5 Root: 6.107 --> Root in between the borders! Added to results. Coefficients: -14.1261111111111Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20250519_010019.]: Samplename: 25 Root: 15.282 --> Root in between the borders! Added to results. Coefficients: -21.3781111111111Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20250519_010019.]: Samplename: 37.5 Root: 23.331 --> Root in between the borders! Added to results. Coefficients: -31.7548888888889Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20250519_010019.]: Samplename: 50 Root: 35.107 --> Root in between the borders! Added to results. Coefficients: -43.9507777777778Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20250519_010019.]: Samplename: 62.5 Root: 49.368 --> Root in between the borders! Added to results. Coefficients: -53.6321111111111Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20250519_010019.]: Samplename: 75 Root: 61.044 --> Root in between the borders! Added to results. Coefficients: -61.6602592592593Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20250519_010019.]: Samplename: 87.5 Root: 70.984 --> Root in between the borders! Added to results. Coefficients: -83.9632222222222Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 [20250519_010019.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 [20250519_010019.]: ### Starting with regression calculations ### [20250519_010019.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 [20250519_010019.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 4.66057108506072, 9.3520572622014, 15.5898391276712, 23.6006773278817, 33.1210812975699, 42.000790440049, 49.5097019721065, 100)c(0, 7.83942891493928, 15.6479427377986, 21.9101608723288, 26.3993226721183, 29.3789187024301, 32.999209559951, 37.9902980278935, 0)c(NA, 62.7154313195142, 62.5917709511944, 58.4270956595435, 52.7986453442366, 47.0062699238882, 43.9989460799347, 43.4174834604497, 0) [20250519_010019.]: Logging df_agg: CpG#1 [20250519_010019.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010019.]: c(0, 4.66057108506072, 9.3520572622014, 15.5898391276712, 23.6006773278817, 33.1210812975699, 42.000790440049, 49.5097019721065, 100)[20250519_010019.]: c(0, 7.83942891493928, 15.6479427377986, 21.9101608723288, 26.3993226721183, 29.3789187024301, 32.999209559951, 37.9902980278935, 0)[20250519_010019.]: c(NA, 62.7154313195142, 62.5917709511944, 58.4270956595435, 52.7986453442366, 47.0062699238882, 43.9989460799347, 43.4174834604497, 0) [20250519_010019.]: Entered 'hyperbolic_regression'-Function [20250519_010019.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010020.]: Entered 'cubic_regression'-Function [20250519_010020.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010020.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.41473135558285, 21.0155787157944, 28.98380769729, 40.7258283847219, 60.321752127534, 69.670868358929, 79.5285048174076, 100)c(0, 4.08526864441715, 3.9844212842056, 8.51619230271, 9.2741716152781, 2.178247872466, 5.329131641071, 7.97149518259241, 0)c(NA, 32.6821491553372, 15.9376851368224, 22.70984614056, 18.5483432305562, 3.4851965959456, 7.10550885476133, 9.11028020867704, 0) [20250519_010020.]: Logging df_agg: CpG#2 [20250519_010020.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010020.]: c(0, 8.41473135558285, 21.0155787157944, 28.98380769729, 40.7258283847219, 60.321752127534, 69.670868358929, 79.5285048174076, 100)[20250519_010020.]: c(0, 4.08526864441715, 3.9844212842056, 8.51619230271, 9.2741716152781, 2.178247872466, 5.329131641071, 7.97149518259241, 0)[20250519_010020.]: c(NA, 32.6821491553372, 15.9376851368224, 22.70984614056, 18.5483432305562, 3.4851965959456, 7.10550885476133, 9.11028020867704, 0) [20250519_010020.]: Entered 'hyperbolic_regression'-Function [20250519_010020.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010020.]: Entered 'cubic_regression'-Function [20250519_010020.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010020.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 5.74980505230347, 15.0124787349221, 22.8043512255536, 34.3082682140428, 47.6381691654944, 61.8773912993076, 65.4860304554385, 100)c(0, 6.75019494769653, 9.9875212650779, 14.6956487744464, 15.6917317859572, 14.8618308345056, 13.1226087006924, 22.0139695445615, 0)c(NA, 54.0015595815722, 39.9500850603116, 39.1883967318571, 31.3834635719144, 23.778929335209, 17.4968116009232, 25.1588223366417, 0) [20250519_010020.]: Logging df_agg: CpG#3 [20250519_010020.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010020.]: c(0, 5.74980505230347, 15.0124787349221, 22.8043512255536, 34.3082682140428, 47.6381691654944, 61.8773912993076, 65.4860304554385, 100)[20250519_010020.]: c(0, 6.75019494769653, 9.9875212650779, 14.6956487744464, 15.6917317859572, 14.8618308345056, 13.1226087006924, 22.0139695445615, 0)[20250519_010020.]: c(NA, 54.0015595815722, 39.9500850603116, 39.1883967318571, 31.3834635719144, 23.778929335209, 17.4968116009232, 25.1588223366417, 0) [20250519_010020.]: Entered 'hyperbolic_regression'-Function [20250519_010020.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010020.]: Entered 'cubic_regression'-Function [20250519_010020.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010021.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 6.6142663267093, 13.3723444572473, 22.4624407354305, 30.4240096190196, 48.070278909378, 55.3176226954937, 66.4686809672836, 100)c(0, 5.8857336732907, 11.6276555427527, 15.0375592645695, 19.5759903809804, 14.429721090622, 19.6823773045063, 21.0313190327164, 0)c(NA, 47.0858693863256, 46.5106221710108, 40.100158038852, 39.1519807619608, 23.0875537449952, 26.2431697393417, 24.0357931802473, 0) [20250519_010021.]: Logging df_agg: CpG#4 [20250519_010021.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010021.]: c(0, 6.6142663267093, 13.3723444572473, 22.4624407354305, 30.4240096190196, 48.070278909378, 55.3176226954937, 66.4686809672836, 100)[20250519_010021.]: c(0, 5.8857336732907, 11.6276555427527, 15.0375592645695, 19.5759903809804, 14.429721090622, 19.6823773045063, 21.0313190327164, 0)[20250519_010021.]: c(NA, 47.0858693863256, 46.5106221710108, 40.100158038852, 39.1519807619608, 23.0875537449952, 26.2431697393417, 24.0357931802473, 0) [20250519_010021.]: Entered 'hyperbolic_regression'-Function [20250519_010021.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010021.]: Entered 'cubic_regression'-Function [20250519_010021.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010021.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.4570373964758, 26.3229081557653, 39.9371114374682, 53.0630784939681, 63.9590902125397, 77.051078420522, 86.3095786035144, 100)c(0, 2.0429626035242, 1.3229081557653, 2.4371114374682, 3.0630784939681, 1.4590902125397, 2.051078420522, 1.1904213964856, 0)c(NA, 16.3437008281936, 5.2916326230612, 6.49896383324852, 6.1261569879362, 2.33454434006352, 2.73477122736267, 1.36048159598355, 0) [20250519_010021.]: Logging df_agg: CpG#5 [20250519_010021.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010021.]: c(0, 10.4570373964758, 26.3229081557653, 39.9371114374682, 53.0630784939681, 63.9590902125397, 77.051078420522, 86.3095786035144, 100)[20250519_010021.]: c(0, 2.0429626035242, 1.3229081557653, 2.4371114374682, 3.0630784939681, 1.4590902125397, 2.051078420522, 1.1904213964856, 0)[20250519_010021.]: c(NA, 16.3437008281936, 5.2916326230612, 6.49896383324852, 6.1261569879362, 2.33454434006352, 2.73477122736267, 1.36048159598355, 0) [20250519_010021.]: Entered 'hyperbolic_regression'-Function [20250519_010021.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010021.]: Entered 'cubic_regression'-Function [20250519_010021.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010021.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.15031343762292, 19.2804724179951, 26.5529108075111, 39.8450445790276, 54.9734681583699, 64.742519688394, 81.444841573602, 100)c(0, 4.34968656237708, 5.7195275820049, 10.9470891924889, 10.1549554209724, 7.5265318416301, 10.257480311606, 6.05515842639799, 0)c(NA, 34.7974924990166, 22.8781103280196, 29.1922378466371, 20.3099108419448, 12.0424509466082, 13.6766404154747, 6.92018105874057, 0) [20250519_010021.]: Logging df_agg: CpG#6 [20250519_010021.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010021.]: c(0, 8.15031343762292, 19.2804724179951, 26.5529108075111, 39.8450445790276, 54.9734681583699, 64.742519688394, 81.444841573602, 100)[20250519_010021.]: c(0, 4.34968656237708, 5.7195275820049, 10.9470891924889, 10.1549554209724, 7.5265318416301, 10.257480311606, 6.05515842639799, 0)[20250519_010021.]: c(NA, 34.7974924990166, 22.8781103280196, 29.1922378466371, 20.3099108419448, 12.0424509466082, 13.6766404154747, 6.92018105874057, 0) [20250519_010021.]: Entered 'hyperbolic_regression'-Function [20250519_010021.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010022.]: Entered 'cubic_regression'-Function [20250519_010022.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010022.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 3.66155933245296, 9.01334562885863, 14.9602090097958, 23.8420840440459, 32.8944868748769, 42.0684566960277, 46.1959470960303, 99.9999999999999)c(0, 8.83844066754704, 15.9866543711414, 22.5397909902042, 26.1579159559541, 29.6055131251231, 32.9315433039723, 41.3040529039697, 9.9475983006414e-14)c(NA, 70.7075253403763, 63.9466174845655, 60.1061093072112, 52.3158319119082, 47.368821000197, 43.9087244052964, 47.2046318902511, 9.9475983006414e-14) [20250519_010022.]: Logging df_agg: CpG#7 [20250519_010022.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010022.]: c(0, 3.66155933245296, 9.01334562885863, 14.9602090097958, 23.8420840440459, 32.8944868748769, 42.0684566960277, 46.1959470960303, 99.9999999999999)[20250519_010022.]: c(0, 8.83844066754704, 15.9866543711414, 22.5397909902042, 26.1579159559541, 29.6055131251231, 32.9315433039723, 41.3040529039697, 9.9475983006414e-14)[20250519_010022.]: c(NA, 70.7075253403763, 63.9466174845655, 60.1061093072112, 52.3158319119082, 47.368821000197, 43.9087244052964, 47.2046318902511, 9.9475983006414e-14) [20250519_010022.]: Entered 'hyperbolic_regression'-Function [20250519_010022.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010022.]: Entered 'cubic_regression'-Function [20250519_010022.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010022.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.96146907472175, 28.1602892189673, 37.1893361505397, 54.6609191320759, 66.6424746382684, 79.0794300908275, 81.6808296744085, 100)c(0, 3.53853092527825, 3.1602892189673, 0.310663849460298, 4.6609191320759, 4.1424746382684, 4.0794300908275, 5.8191703255915, 0)c(NA, 28.308247402226, 12.6411568758692, 0.828436931894127, 9.32183826415179, 6.62795942122943, 5.43924012110333, 6.65048037210457, 0) [20250519_010022.]: Logging df_agg: CpG#8 [20250519_010022.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010022.]: c(0, 8.96146907472175, 28.1602892189673, 37.1893361505397, 54.6609191320759, 66.6424746382684, 79.0794300908275, 81.6808296744085, 100)[20250519_010022.]: c(0, 3.53853092527825, 3.1602892189673, 0.310663849460298, 4.6609191320759, 4.1424746382684, 4.0794300908275, 5.8191703255915, 0)[20250519_010022.]: c(NA, 28.308247402226, 12.6411568758692, 0.828436931894127, 9.32183826415179, 6.62795942122943, 5.43924012110333, 6.65048037210457, 0) [20250519_010022.]: Entered 'hyperbolic_regression'-Function [20250519_010022.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010022.]: Entered 'cubic_regression'-Function [20250519_010022.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010023.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 3.60796342124625, 9.37027096567147, 13.6529015267019, 21.8965479410597, 31.2983747080124, 40.6887948365007, 53.9067876123417, 100)c(0, 8.89203657875375, 15.6297290343285, 23.8470984732981, 28.1034520589403, 31.2016252919876, 34.3112051634993, 33.5932123876583, 0)c(NA, 71.13629263003, 62.5189161373141, 63.5922625954616, 56.2069041178806, 49.9226004671802, 45.7482735513324, 38.3922427287523, 0) [20250519_010023.]: Logging df_agg: CpG#9 [20250519_010023.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010023.]: c(0, 3.60796342124625, 9.37027096567147, 13.6529015267019, 21.8965479410597, 31.2983747080124, 40.6887948365007, 53.9067876123417, 100)[20250519_010023.]: c(0, 8.89203657875375, 15.6297290343285, 23.8470984732981, 28.1034520589403, 31.2016252919876, 34.3112051634993, 33.5932123876583, 0)[20250519_010023.]: c(NA, 71.13629263003, 62.5189161373141, 63.5922625954616, 56.2069041178806, 49.9226004671802, 45.7482735513324, 38.3922427287523, 0) [20250519_010023.]: Entered 'hyperbolic_regression'-Function [20250519_010023.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010023.]: Entered 'cubic_regression'-Function [20250519_010023.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010023.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.000118249869729556, 6.10737244290489, 15.2822474794705, 23.3309318476314, 35.1067698776441, 49.3683845454072, 61.0436420088989, 70.9843655625978, 100)c(0.000118249869729556, 6.39262755709511, 9.7177525205295, 14.1690681523686, 14.8932301223559, 13.1316154545928, 13.9563579911011, 16.5156344374022, 0)c(NA, 51.1410204567609, 38.871010082118, 37.7841817396496, 29.7864602447118, 21.0105847273485, 18.6084773214681, 18.8750107856025, 0) [20250519_010023.]: Logging df_agg: row_means [20250519_010023.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010023.]: c(0.000118249869729556, 6.10737244290489, 15.2822474794705, 23.3309318476314, 35.1067698776441, 49.3683845454072, 61.0436420088989, 70.9843655625978, 100)[20250519_010023.]: c(0.000118249869729556, 6.39262755709511, 9.7177525205295, 14.1690681523686, 14.8932301223559, 13.1316154545928, 13.9563579911011, 16.5156344374022, 0)[20250519_010023.]: c(NA, 51.1410204567609, 38.871010082118, 37.7841817396496, 29.7864602447118, 21.0105847273485, 18.6084773214681, 18.8750107856025, 0) [20250519_010023.]: Entered 'hyperbolic_regression'-Function [20250519_010023.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010023.]: Entered 'cubic_regression'-Function [20250519_010023.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010024.]: Entered 'solving_equations'-Function [20250519_010024.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 78.9992198550223 [20250519_010024.]: Samplename: Sample#1 Root: 78.999 --> Root in between the borders! Added to results. Hyperbolic solved: 31.2870579236875 [20250519_010024.]: Samplename: Sample#10 Root: 31.287 --> Root in between the borders! Added to results. Hyperbolic solved: 42.7215290847412 [20250519_010024.]: Samplename: Sample#2 Root: 42.722 --> Root in between the borders! Added to results. Hyperbolic solved: 57.8350974981729 [20250519_010024.]: Samplename: Sample#3 Root: 57.835 --> Root in between the borders! Added to results. Hyperbolic solved: 11.2415690196975 [20250519_010024.]: Samplename: Sample#4 Root: 11.242 --> Root in between the borders! Added to results. Hyperbolic solved: 23.5440570752846 [20250519_010024.]: Samplename: Sample#5 Root: 23.544 --> Root in between the borders! Added to results. Hyperbolic solved: 24.7858714956021 [20250519_010024.]: Samplename: Sample#6 Root: 24.786 --> Root in between the borders! Added to results. Hyperbolic solved: 46.4156211496502 [20250519_010024.]: Samplename: Sample#7 Root: 46.416 --> Root in between the borders! Added to results. Hyperbolic solved: 84.4614182359021 [20250519_010024.]: Samplename: Sample#8 Root: 84.461 --> Root in between the borders! Added to results. Hyperbolic solved: -1.41454023853738 [20250519_010024.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -1.415 --> '-10 < root < 0' --> substitute 0 [20250519_010024.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 76.6628425084681 [20250519_010024.]: Samplename: Sample#1 Root: 76.663 --> Root in between the borders! Added to results. Hyperbolic solved: 31.002922067723 [20250519_010024.]: Samplename: Sample#10 Root: 31.003 --> Root in between the borders! Added to results. Hyperbolic solved: 42.8794162472613 [20250519_010024.]: Samplename: Sample#2 Root: 42.879 --> Root in between the borders! Added to results. Hyperbolic solved: 59.1562251576939 [20250519_010024.]: Samplename: Sample#3 Root: 59.156 --> Root in between the borders! Added to results. Hyperbolic solved: 4.04613842773285 [20250519_010024.]: Samplename: Sample#4 Root: 4.046 --> Root in between the borders! Added to results. Hyperbolic solved: 19.9574827771543 [20250519_010024.]: Samplename: Sample#5 Root: 19.957 --> Root in between the borders! Added to results. Hyperbolic solved: 17.4372350357645 [20250519_010024.]: Samplename: Sample#6 Root: 17.437 --> Root in between the borders! Added to results. Hyperbolic solved: 41.0968872148931 [20250519_010024.]: Samplename: Sample#7 Root: 41.097 --> Root in between the borders! Added to results. Hyperbolic solved: 85.7111018674234 [20250519_010024.]: Samplename: Sample#8 Root: 85.711 --> Root in between the borders! Added to results. Hyperbolic solved: 2.08554114625032 [20250519_010024.]: Samplename: Sample#9 Root: 2.086 --> Root in between the borders! Added to results. [20250519_010024.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 74.5413598656994 [20250519_010024.]: Samplename: Sample#1 Root: 74.541 --> Root in between the borders! Added to results. Hyperbolic solved: 28.3514323412579 [20250519_010024.]: Samplename: Sample#10 Root: 28.351 --> Root in between the borders! Added to results. Hyperbolic solved: 42.6007641487945 [20250519_010024.]: Samplename: Sample#2 Root: 42.601 --> Root in between the borders! Added to results. Hyperbolic solved: 56.3207791919885 [20250519_010024.]: Samplename: Sample#3 Root: 56.321 --> Root in between the borders! Added to results. Hyperbolic solved: 7.98800399083 [20250519_010024.]: Samplename: Sample#4 Root: 7.988 --> Root in between the borders! Added to results. Hyperbolic solved: 24.6963927948962 [20250519_010024.]: Samplename: Sample#5 Root: 24.696 --> Root in between the borders! Added to results. Hyperbolic solved: 26.8806215804592 [20250519_010024.]: Samplename: Sample#6 Root: 26.881 --> Root in between the borders! Added to results. Hyperbolic solved: 44.8239489304102 [20250519_010024.]: Samplename: Sample#7 Root: 44.824 --> Root in between the borders! Added to results. Hyperbolic solved: 84.669654917361 [20250519_010024.]: Samplename: Sample#8 Root: 84.67 --> Root in between the borders! Added to results. Hyperbolic solved: -1.26160051687973 [20250519_010024.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -1.262 --> '-10 < root < 0' --> substitute 0 [20250519_010024.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 75.8530019120299 [20250519_010024.]: Samplename: Sample#1 Root: 75.853 --> Root in between the borders! Added to results. Hyperbolic solved: 29.0711677507654 [20250519_010024.]: Samplename: Sample#10 Root: 29.071 --> Root in between the borders! Added to results. Hyperbolic solved: 44.0485537238583 [20250519_010024.]: Samplename: Sample#2 Root: 44.049 --> Root in between the borders! Added to results. Hyperbolic solved: 58.7878536128746 [20250519_010024.]: Samplename: Sample#3 Root: 58.788 --> Root in between the borders! Added to results. Hyperbolic solved: 11.0370782280591 [20250519_010024.]: Samplename: Sample#4 Root: 11.037 --> Root in between the borders! Added to results. Hyperbolic solved: 23.0042107924666 [20250519_010024.]: Samplename: Sample#5 Root: 23.004 --> Root in between the borders! Added to results. Hyperbolic solved: 27.9521038368967 [20250519_010024.]: Samplename: Sample#6 Root: 27.952 --> Root in between the borders! Added to results. Hyperbolic solved: 42.50025622677 [20250519_010024.]: Samplename: Sample#7 Root: 42.5 --> Root in between the borders! Added to results. Hyperbolic solved: 84.6870942263369 [20250519_010024.]: Samplename: Sample#8 Root: 84.687 --> Root in between the borders! Added to results. Hyperbolic solved: 3.01041300417756 [20250519_010024.]: Samplename: Sample#9 Root: 3.01 --> Root in between the borders! Added to results. [20250519_010024.]: Solving cubic regression for CpG#5 Coefficients: -47.8373333333333Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_010024.]: Samplename: Sample#1 Root: 73.686 --> Root in between the borders! Added to results. Coefficients: -13.588Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_010024.]: Samplename: Sample#10 Root: 28.906 --> Root in between the borders! Added to results. Coefficients: -25.3211428571429Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_010024.]: Samplename: Sample#2 Root: 46.761 --> Root in between the borders! Added to results. Coefficients: -32.064Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_010024.]: Samplename: Sample#3 Root: 55.588 --> Root in between the borders! Added to results. Coefficients: -4.074Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_010024.]: Samplename: Sample#4 Root: 10.297 --> Root in between the borders! Added to results. Coefficients: -11.434Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_010024.]: Samplename: Sample#5 Root: 25.131 --> Root in between the borders! Added to results. Coefficients: -13.294Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_010024.]: Samplename: Sample#6 Root: 28.402 --> Root in between the borders! Added to results. Coefficients: -24.288Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_010024.]: Samplename: Sample#7 Root: 45.332 --> Root in between the borders! Added to results. Coefficients: -63.134Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_010024.]: Samplename: Sample#8 Root: 88.909 --> Root in between the borders! Added to results. Coefficients: 0.0360000000000005Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_010024.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.102 --> '-10 < root < 0' --> substitute 0 [20250519_010024.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 79.2062097176894 [20250519_010024.]: Samplename: Sample#1 Root: 79.206 --> Root in between the borders! Added to results. Hyperbolic solved: 30.234912908094 [20250519_010024.]: Samplename: Sample#10 Root: 30.235 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8992139020538 [20250519_010024.]: Samplename: Sample#2 Root: 41.899 --> Root in between the borders! Added to results. Hyperbolic solved: 56.8778124304882 [20250519_010024.]: Samplename: Sample#3 Root: 56.878 --> Root in between the borders! Added to results. Hyperbolic solved: 8.80900690226663 [20250519_010024.]: Samplename: Sample#4 Root: 8.809 --> Root in between the borders! Added to results. Hyperbolic solved: 18.6793858198319 [20250519_010024.]: Samplename: Sample#5 Root: 18.679 --> Root in between the borders! Added to results. Hyperbolic solved: 29.9638526705022 [20250519_010024.]: Samplename: Sample#6 Root: 29.964 --> Root in between the borders! Added to results. Hyperbolic solved: 42.8669225173731 [20250519_010024.]: Samplename: Sample#7 Root: 42.867 --> Root in between the borders! Added to results. Hyperbolic solved: 86.6206311389839 [20250519_010024.]: Samplename: Sample#8 Root: 86.621 --> Root in between the borders! Added to results. Hyperbolic solved: 1.38882504570857 [20250519_010024.]: Samplename: Sample#9 Root: 1.389 --> Root in between the borders! Added to results. [20250519_010024.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 77.5203002291111 [20250519_010024.]: Samplename: Sample#1 Root: 77.52 --> Root in between the borders! Added to results. Hyperbolic solved: 27.0810020209347 [20250519_010024.]: Samplename: Sample#10 Root: 27.081 --> Root in between the borders! Added to results. Hyperbolic solved: 48.4274819445682 [20250519_010024.]: Samplename: Sample#2 Root: 48.427 --> Root in between the borders! Added to results. Hyperbolic solved: 58.8711296078074 [20250519_010024.]: Samplename: Sample#3 Root: 58.871 --> Root in between the borders! Added to results. Hyperbolic solved: 13.3245830390029 [20250519_010024.]: Samplename: Sample#4 Root: 13.325 --> Root in between the borders! Added to results. Hyperbolic solved: 26.9731029843141 [20250519_010024.]: Samplename: Sample#5 Root: 26.973 --> Root in between the borders! Added to results. Hyperbolic solved: 30.9519756409676 [20250519_010024.]: Samplename: Sample#6 Root: 30.952 --> Root in between the borders! Added to results. Hyperbolic solved: 45.7349250650843 [20250519_010024.]: Samplename: Sample#7 Root: 45.735 --> Root in between the borders! Added to results. Hyperbolic solved: 84.5977214697661 [20250519_010024.]: Samplename: Sample#8 Root: 84.598 --> Root in between the borders! Added to results. Hyperbolic solved: -2.87252278311384 [20250519_010024.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.873 --> '-10 < root < 0' --> substitute 0 [20250519_010024.]: Solving cubic regression for CpG#8 Coefficients: -55.3573333333333Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_010024.]: Samplename: Sample#1 Root: 74.173 --> Root in between the borders! Added to results. Coefficients: -17.574Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_010024.]: Samplename: Sample#10 Root: 30.7 --> Root in between the borders! Added to results. Coefficients: -22.9425714285714Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_010024.]: Samplename: Sample#2 Root: 38.106 --> Root in between the borders! Added to results. Coefficients: -42.849Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_010024.]: Samplename: Sample#3 Root: 61.571 --> Root in between the borders! Added to results. Coefficients: -4.604Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_010024.]: Samplename: Sample#4 Root: 9.446 --> Root in between the borders! Added to results. Coefficients: -11.389Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_010024.]: Samplename: Sample#5 Root: 21.295 --> Root in between the borders! Added to results. Coefficients: -25.784Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_010024.]: Samplename: Sample#6 Root: 41.8 --> Root in between the borders! Added to results. Coefficients: -30.746Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_010024.]: Samplename: Sample#7 Root: 47.937 --> Root in between the borders! Added to results. Coefficients: -66.912Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_010024.]: Samplename: Sample#8 Root: 84.816 --> Root in between the borders! Added to results. Coefficients: 3.176Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_010024.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -7.574 --> '-10 < root < 0' --> substitute 0 [20250519_010024.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 80.5447416574246 [20250519_010024.]: Samplename: Sample#1 Root: 80.545 --> Root in between the borders! Added to results. Hyperbolic solved: 27.8054725532271 [20250519_010024.]: Samplename: Sample#10 Root: 27.805 --> Root in between the borders! Added to results. Hyperbolic solved: 46.257978181877 [20250519_010024.]: Samplename: Sample#2 Root: 46.258 --> Root in between the borders! Added to results. Hyperbolic solved: 57.1842723611193 [20250519_010024.]: Samplename: Sample#3 Root: 57.184 --> Root in between the borders! Added to results. Hyperbolic solved: 8.63689944440251 [20250519_010024.]: Samplename: Sample#4 Root: 8.637 --> Root in between the borders! Added to results. Hyperbolic solved: 24.2116725772085 [20250519_010024.]: Samplename: Sample#5 Root: 24.212 --> Root in between the borders! Added to results. Hyperbolic solved: 39.6334888107959 [20250519_010024.]: Samplename: Sample#6 Root: 39.633 --> Root in between the borders! Added to results. Hyperbolic solved: 44.3019478766814 [20250519_010024.]: Samplename: Sample#7 Root: 44.302 --> Root in between the borders! Added to results. Hyperbolic solved: 87.3231552867261 [20250519_010024.]: Samplename: Sample#8 Root: 87.323 --> Root in between the borders! Added to results. Hyperbolic solved: -1.1792994162273 [20250519_010024.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -1.179 --> '-10 < root < 0' --> substitute 0 [20250519_010024.]: Solving hyperbolic regression for row_means Hyperbolic solved: 76.7712420256811 [20250519_010024.]: Samplename: Sample#1 Root: 76.771 --> Root in between the borders! Added to results. Hyperbolic solved: 28.8491793177963 [20250519_010024.]: Samplename: Sample#10 Root: 28.849 --> Root in between the borders! Added to results. Hyperbolic solved: 43.0342096405004 [20250519_010024.]: Samplename: Sample#2 Root: 43.034 --> Root in between the borders! Added to results. Hyperbolic solved: 57.634070615517 [20250519_010024.]: Samplename: Sample#3 Root: 57.634 --> Root in between the borders! Added to results. Hyperbolic solved: 8.87182398849477 [20250519_010024.]: Samplename: Sample#4 Root: 8.872 --> Root in between the borders! Added to results. Hyperbolic solved: 22.1989155573519 [20250519_010024.]: Samplename: Sample#5 Root: 22.199 --> Root in between the borders! Added to results. Hyperbolic solved: 29.2140219298625 [20250519_010024.]: Samplename: Sample#6 Root: 29.214 --> Root in between the borders! Added to results. Hyperbolic solved: 43.9371957594463 [20250519_010024.]: Samplename: Sample#7 Root: 43.937 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6706924479325 [20250519_010024.]: Samplename: Sample#8 Root: 85.671 --> Root in between the borders! Added to results. Hyperbolic solved: -0.55137023392917 [20250519_010024.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.551 --> '-10 < root < 0' --> substitute 0 [20250519_010024.]: Entered 'solving_equations'-Function [20250519_010024.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 0 [20250519_010024.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 14.1480167109277 [20250519_010024.]: Samplename: 12.5 Root: 14.148 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1398443790446 [20250519_010024.]: Samplename: 25 Root: 26.14 --> Root in between the borders! Added to results. Hyperbolic solved: 39.3762039523382 [20250519_010024.]: Samplename: 37.5 Root: 39.376 --> Root in between the borders! Added to results. Hyperbolic solved: 52.9476244028783 [20250519_010024.]: Samplename: 50 Root: 52.948 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4195105873419 [20250519_010024.]: Samplename: 62.5 Root: 65.42 --> Root in between the borders! Added to results. Hyperbolic solved: 74.433837698331 [20250519_010024.]: Samplename: 75 Root: 74.434 --> Root in between the borders! Added to results. Hyperbolic solved: 80.5559264925808 [20250519_010024.]: Samplename: 87.5 Root: 80.556 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_010024.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_010024.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 0 [20250519_010024.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 10.7812303822548 [20250519_010024.]: Samplename: 12.5 Root: 10.781 --> Root in between the borders! Added to results. Hyperbolic solved: 26.0648314401535 [20250519_010024.]: Samplename: 25 Root: 26.065 --> Root in between the borders! Added to results. Hyperbolic solved: 35.1980950192158 [20250519_010024.]: Samplename: 37.5 Root: 35.198 --> Root in between the borders! Added to results. Hyperbolic solved: 47.9203836090837 [20250519_010024.]: Samplename: 50 Root: 47.92 --> Root in between the borders! Added to results. Hyperbolic solved: 67.275750545092 [20250519_010024.]: Samplename: 62.5 Root: 67.276 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7278143684244 [20250519_010024.]: Samplename: 75 Root: 75.728 --> Root in between the borders! Added to results. Hyperbolic solved: 84.1258428690749 [20250519_010024.]: Samplename: 87.5 Root: 84.126 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_010024.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_010024.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 0 [20250519_010024.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 10.8467198180169 [20250519_010024.]: Samplename: 12.5 Root: 10.847 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1449700612555 [20250519_010024.]: Samplename: 25 Root: 26.145 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2865760013773 [20250519_010024.]: Samplename: 37.5 Root: 37.287 --> Root in between the borders! Added to results. Hyperbolic solved: 51.4114078772697 [20250519_010024.]: Samplename: 50 Root: 51.411 --> Root in between the borders! Added to results. Hyperbolic solved: 65.0139634978039 [20250519_010024.]: Samplename: 62.5 Root: 65.014 --> Root in between the borders! Added to results. Hyperbolic solved: 76.9921394830877 [20250519_010024.]: Samplename: 75 Root: 76.992 --> Root in between the borders! Added to results. Hyperbolic solved: 79.6808818589171 [20250519_010024.]: Samplename: 87.5 Root: 79.681 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_010024.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_010024.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 0 [20250519_010024.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 13.2494913489925 [20250519_010024.]: Samplename: 12.5 Root: 13.249 --> Root in between the borders! Added to results. Hyperbolic solved: 25.0914701740206 [20250519_010024.]: Samplename: 25 Root: 25.091 --> Root in between the borders! Added to results. Hyperbolic solved: 38.8081740444332 [20250519_010024.]: Samplename: 37.5 Root: 38.808 --> Root in between the borders! Added to results. Hyperbolic solved: 49.1133034150896 [20250519_010024.]: Samplename: 50 Root: 49.113 --> Root in between the borders! Added to results. Hyperbolic solved: 67.573566028728 [20250519_010024.]: Samplename: 62.5 Root: 67.574 --> Root in between the borders! Added to results. Hyperbolic solved: 73.7655861480302 [20250519_010024.]: Samplename: 75 Root: 73.766 --> Root in between the borders! Added to results. Hyperbolic solved: 82.0404940898918 [20250519_010024.]: Samplename: 87.5 Root: 82.04 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_010024.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_010024.]: Solving cubic regression for CpG#5 Coefficients: 0Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_010024.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -4.144Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_010024.]: Samplename: 12.5 Root: 10.457 --> Root in between the borders! Added to results. Coefficients: -12.102Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_010024.]: Samplename: 25 Root: 26.323 --> Root in between the borders! Added to results. Coefficients: -20.536Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_010024.]: Samplename: 37.5 Root: 39.937 --> Root in between the borders! Added to results. Coefficients: -30.0715Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_010024.]: Samplename: 50 Root: 53.063 --> Root in between the borders! Added to results. Coefficients: -39.034Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_010024.]: Samplename: 62.5 Root: 63.959 --> Root in between the borders! Added to results. Coefficients: -51.059Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_010024.]: Samplename: 75 Root: 77.051 --> Root in between the borders! Added to results. Coefficients: -60.3906666666667Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_010024.]: Samplename: 87.5 Root: 86.31 --> Root in between the borders! Added to results. Coefficients: -75.446Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 [20250519_010024.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_010024.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 0 [20250519_010024.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 11.8139244542506 [20250519_010024.]: Samplename: 12.5 Root: 11.814 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5330423964606 [20250519_010024.]: Samplename: 25 Root: 26.533 --> Root in between the borders! Added to results. Hyperbolic solved: 35.365455365759 [20250519_010024.]: Samplename: 37.5 Root: 35.365 --> Root in between the borders! Added to results. Hyperbolic solved: 50.1054345020203 [20250519_010024.]: Samplename: 50 Root: 50.105 --> Root in between the borders! Added to results. Hyperbolic solved: 64.9683746571925 [20250519_010024.]: Samplename: 62.5 Root: 64.968 --> Root in between the borders! Added to results. Hyperbolic solved: 73.6331726521876 [20250519_010024.]: Samplename: 75 Root: 73.633 --> Root in between the borders! Added to results. Hyperbolic solved: 86.9938602772804 [20250519_010024.]: Samplename: 87.5 Root: 86.994 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_010024.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_010024.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 0 [20250519_010024.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 11.7880491100164 [20250519_010024.]: Samplename: 12.5 Root: 11.788 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1959234034328 [20250519_010024.]: Samplename: 25 Root: 26.196 --> Root in between the borders! Added to results. Hyperbolic solved: 39.1978567589215 [20250519_010024.]: Samplename: 37.5 Root: 39.198 --> Root in between the borders! Added to results. Hyperbolic solved: 54.3513505291649 [20250519_010024.]: Samplename: 50 Root: 54.351 --> Root in between the borders! Added to results. Hyperbolic solved: 66.0567953704493 [20250519_010024.]: Samplename: 62.5 Root: 66.057 --> Root in between the borders! Added to results. Hyperbolic solved: 75.1900867269869 [20250519_010024.]: Samplename: 75 Root: 75.19 --> Root in between the borders! Added to results. Hyperbolic solved: 78.6051660349008 [20250519_010024.]: Samplename: 87.5 Root: 78.605 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_010024.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_010024.]: Solving cubic regression for CpG#8 Coefficients: 0Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_010024.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -4.35066666666667Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_010024.]: Samplename: 12.5 Root: 8.961 --> Root in between the borders! Added to results. Coefficients: -15.834Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_010024.]: Samplename: 25 Root: 28.16 --> Root in between the borders! Added to results. Coefficients: -22.254Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_010024.]: Samplename: 37.5 Root: 37.189 --> Root in between the borders! Added to results. Coefficients: -36.529Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_010024.]: Samplename: 50 Root: 54.661 --> Root in between the borders! Added to results. Coefficients: -47.73Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_010024.]: Samplename: 62.5 Root: 66.642 --> Root in between the borders! Added to results. Coefficients: -60.5715Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_010024.]: Samplename: 75 Root: 79.079 --> Root in between the borders! Added to results. Coefficients: -63.414Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_010024.]: Samplename: 87.5 Root: 81.681 --> Root in between the borders! Added to results. Coefficients: -84.964Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 [20250519_010024.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_010024.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 0 [20250519_010025.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 12.2068234201026 [20250519_010025.]: Samplename: 12.5 Root: 12.207 --> Root in between the borders! Added to results. Hyperbolic solved: 28.0688737636578 [20250519_010025.]: Samplename: 25 Root: 28.069 --> Root in between the borders! Added to results. Hyperbolic solved: 37.6661823177513 [20250519_010025.]: Samplename: 37.5 Root: 37.666 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3684233460174 [20250519_010025.]: Samplename: 50 Root: 52.368 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8636916649907 [20250519_010025.]: Samplename: 62.5 Root: 64.864 --> Root in between the borders! Added to results. Hyperbolic solved: 74.2551330907062 [20250519_010025.]: Samplename: 75 Root: 74.255 --> Root in between the borders! Added to results. Hyperbolic solved: 83.9343288872042 [20250519_010025.]: Samplename: 87.5 Root: 83.934 --> Root in between the borders! Added to results. Hyperbolic solved: 100 [20250519_010025.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. [20250519_010025.]: Solving hyperbolic regression for row_means Hyperbolic solved: 0.000228160853766634 [20250519_010025.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 11.1587839347378 [20250519_010025.]: Samplename: 12.5 Root: 11.159 --> Root in between the borders! Added to results. Hyperbolic solved: 25.8570776700364 [20250519_010025.]: Samplename: 25 Root: 25.857 --> Root in between the borders! Added to results. Hyperbolic solved: 37.065004454281 [20250519_010025.]: Samplename: 37.5 Root: 37.065 --> Root in between the borders! Added to results. Hyperbolic solved: 51.1881763075298 [20250519_010025.]: Samplename: 50 Root: 51.188 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4439754555105 [20250519_010025.]: Samplename: 62.5 Root: 65.444 --> Root in between the borders! Added to results. Hyperbolic solved: 75.3005883283082 [20250519_010025.]: Samplename: 75 Root: 75.301 --> Root in between the borders! Added to results. Hyperbolic solved: 82.6591025877361 [20250519_010025.]: Samplename: 87.5 Root: 82.659 --> Root in between the borders! Added to results. Hyperbolic solved: 100.000153506599 [20250519_010025.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 [20250519_010027.]: on_start: using future::plan("sequential") [20250519_010027.]: Entered 'clean_dt'-Function [20250519_010027.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_010027.]: got experimental data [20250519_010027.]: Entered 'clean_dt'-Function [20250519_010027.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_010027.]: got calibration data [20250519_010027.]: ### Starting with regression calculations ### [20250519_010027.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_010027.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_010027.]: Logging df_agg: CpG#1 [20250519_010027.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010027.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_010027.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_010027.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_010027.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_010027.]: Entered 'hyperbolic_regression'-Function [20250519_010027.]: 'hyperbolic_regression': minmax = FALSE [20250519_010027.]: Entered 'cubic_regression'-Function [20250519_010027.]: 'cubic_regression': minmax = FALSE [20250519_010027.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_010027.]: Logging df_agg: CpG#2 [20250519_010027.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010027.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_010027.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_010027.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_010027.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_010027.]: Entered 'hyperbolic_regression'-Function [20250519_010027.]: 'hyperbolic_regression': minmax = FALSE [20250519_010027.]: Entered 'cubic_regression'-Function [20250519_010027.]: 'cubic_regression': minmax = FALSE [20250519_010027.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_010027.]: Logging df_agg: CpG#3 [20250519_010027.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010027.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_010027.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_010027.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_010027.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_010027.]: Entered 'hyperbolic_regression'-Function [20250519_010027.]: 'hyperbolic_regression': minmax = FALSE [20250519_010028.]: Entered 'cubic_regression'-Function [20250519_010028.]: 'cubic_regression': minmax = FALSE [20250519_010028.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_010028.]: Logging df_agg: CpG#4 [20250519_010028.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010028.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_010028.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_010028.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_010028.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_010028.]: Entered 'hyperbolic_regression'-Function [20250519_010028.]: 'hyperbolic_regression': minmax = FALSE [20250519_010028.]: Entered 'cubic_regression'-Function [20250519_010028.]: 'cubic_regression': minmax = FALSE [20250519_010028.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_010028.]: Logging df_agg: CpG#5 [20250519_010028.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010028.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_010028.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_010028.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_010028.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_010028.]: Entered 'hyperbolic_regression'-Function [20250519_010028.]: 'hyperbolic_regression': minmax = FALSE [20250519_010028.]: Entered 'cubic_regression'-Function [20250519_010028.]: 'cubic_regression': minmax = FALSE [20250519_010028.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_010028.]: Logging df_agg: CpG#6 [20250519_010028.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010028.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_010028.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_010028.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_010028.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_010028.]: Entered 'hyperbolic_regression'-Function [20250519_010028.]: 'hyperbolic_regression': minmax = FALSE [20250519_010028.]: Entered 'cubic_regression'-Function [20250519_010028.]: 'cubic_regression': minmax = FALSE [20250519_010028.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_010028.]: Logging df_agg: CpG#7 [20250519_010028.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010028.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_010028.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_010028.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_010028.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_010028.]: Entered 'hyperbolic_regression'-Function [20250519_010028.]: 'hyperbolic_regression': minmax = FALSE [20250519_010029.]: Entered 'cubic_regression'-Function [20250519_010029.]: 'cubic_regression': minmax = FALSE [20250519_010029.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_010029.]: Logging df_agg: CpG#8 [20250519_010029.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010029.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_010029.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_010029.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_010029.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_010029.]: Entered 'hyperbolic_regression'-Function [20250519_010029.]: 'hyperbolic_regression': minmax = FALSE [20250519_010029.]: Entered 'cubic_regression'-Function [20250519_010029.]: 'cubic_regression': minmax = FALSE [20250519_010029.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_010029.]: Logging df_agg: CpG#9 [20250519_010029.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010029.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_010029.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_010029.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_010029.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_010029.]: Entered 'hyperbolic_regression'-Function [20250519_010029.]: 'hyperbolic_regression': minmax = FALSE [20250519_010029.]: Entered 'cubic_regression'-Function [20250519_010029.]: 'cubic_regression': minmax = FALSE [20250519_010029.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723122, 2.47760998435345)c(5.94911111111111, 0.84962962962963, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703704, 19.6995555555555, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_010029.]: Logging df_agg: row_means [20250519_010029.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010029.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_010029.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723122, 2.47760998435345)[20250519_010029.]: c(5.94911111111111, 0.84962962962963, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_010029.]: c(NA, 6.79703703703704, 19.6995555555555, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_010029.]: Entered 'hyperbolic_regression'-Function [20250519_010029.]: 'hyperbolic_regression': minmax = FALSE [20250519_010030.]: Entered 'cubic_regression'-Function [20250519_010030.]: 'cubic_regression': minmax = FALSE [20250519_010030.]: ### Starting with plotting ### [20250519_010030.]: Creating plot No. 1 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG1.png [20250519_010030.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -937.719419567728 , a = -108.56796364622 , b = -937.719419567728 , d = -232.057111323599 [20250519_010030.]: # CpG-site: CpG#1 Cubic: Using a = 6.53413423120091e-05 , b = -0.0055806968734969 , c = 0.784061853455188 , d = 1.93182659932656 [20250519_010033.]: Creating plot No. 2 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG2.png [20250519_010033.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = -2393.1426335766 , a = -141.862264327279 , b = -2393.1426335766 , d = -268.331583599043 [20250519_010033.]: # CpG-site: CpG#2 Cubic: Using a = 8.04237934904601e-06 , b = 0.00293158941798942 , c = 0.514821742825076 , d = 9.27667003367003 [20250519_010035.]: Creating plot No. 3 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG3.png [20250519_010035.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = -1477.46855437885 , a = -170.424507883158 , b = -1477.46855437885 , d = -327.190157171162 [20250519_010035.]: # CpG-site: CpG#3 Cubic: Using a = 2.30555869809204e-05 , b = -0.000797009331409346 , c = 0.62783129228796 , d = 3.88705218855218 [20250519_010038.]: Creating plot No. 4 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG4.png [20250519_010038.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = -2115.87792930847 , a = -162.169768036114 , b = -2115.87792930847 , d = -299.662268971294 [20250519_010038.]: # CpG-site: CpG#4 Cubic: Using a = 3.2166356902357e-05 , b = -0.0015913142857143 , c = 0.697398364598366 , d = 6.17055050505048 [20250519_010040.]: Creating plot No. 5 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG5.png [20250519_010040.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = -1383.32738741788 , a = -151.440067063617 , b = -1383.32738741788 , d = -304.200253800186 [20250519_010040.]: # CpG-site: CpG#5 Cubic: Using a = -4.48459708193036e-06 , b = 0.00375371236171235 , c = 0.422446384479718 , d = 5.17203872053872 [20250519_010042.]: Creating plot No. 6 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG6.png [20250519_010042.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = -1550.59307603208 , a = -125.016146072952 , b = -1550.59307603208 , d = -247.279679418643 [20250519_010042.]: # CpG-site: CpG#6 Cubic: Using a = 2.38852884399552e-05 , b = 0.000918637037037021 , c = 0.561022132435467 , d = 6.1479276094276 [20250519_010044.]: Creating plot No. 7 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG7.png [20250519_010044.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -772.844192345493 , a = -75.2724875322984 , b = -772.844192345493 , d = -226.673147125363 [20250519_010044.]: # CpG-site: CpG#7 Cubic: Using a = 4.98010505050505e-05 , b = -0.00436152150072151 , c = 0.579588917748918 , d = 1.72104545454544 [20250519_010046.]: Creating plot No. 8 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG8.png [20250519_010046.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = -3872.73616547768 , a = -278.017275815885 , b = -3872.73616547768 , d = -438.894268920994 [20250519_010046.]: # CpG-site: CpG#8 Cubic: Using a = -1.50060965207632e-05 , b = 0.00447978143338143 , c = 0.534897737694404 , d = 9.56981481481482 [20250519_010048.]: Creating plot No. 9 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG9.png [20250519_010048.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -843.925451494751 , a = -70.6796095478879 , b = -843.925451494751 , d = -184.482153986057 [20250519_010048.]: # CpG-site: CpG#9 Cubic: Using a = 7.72300426487093e-05 , b = -0.00542281173641174 , c = 0.67899229597563 , d = 2.72413468013467 [20250519_010051.]: Creating plot No. 10 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_rowmeans.png [20250519_010051.]: # CpG-site: row_means Hyperbolic: Using bias_weight = -1525.75758254501 , a = -127.67501720702 , b = -1525.75758254501 , d = -263.003030149428 [20250519_010051.]: # CpG-site: row_means Cubic: Using a = 2.88923726150392e-05 , b = -0.0006299592752926 , c = 0.600117857944524 , d = 5.17789562289563 [20250519_010053.]: Entered 'solving_equations'-Function [20250519_010053.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: -2.23222990163966 [20250519_010053.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.232 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.1698489850618 [20250519_010053.]: Samplename: 12.5 Root: 12.17 --> Root in between the borders! Added to results. Hyperbolic solved: 24.4781920312644 [20250519_010053.]: Samplename: 25 Root: 24.478 --> Root in between the borders! Added to results. Hyperbolic solved: 38.173044740918 [20250519_010053.]: Samplename: 37.5 Root: 38.173 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3349371964438 [20250519_010053.]: Samplename: 50 Root: 52.335 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4582773627666 [20250519_010053.]: Samplename: 62.5 Root: 65.458 --> Root in between the borders! Added to results. Hyperbolic solved: 75.0090795260796 [20250519_010053.]: Samplename: 75 Root: 75.009 --> Root in between the borders! Added to results. Hyperbolic solved: 81.5271920968417 [20250519_010053.]: Samplename: 87.5 Root: 81.527 --> Root in between the borders! Added to results. Hyperbolic solved: 102.400893095062 [20250519_010053.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.401 --> '100 < root < 110' --> substitute 100 [20250519_010053.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 1.13660501904968 [20250519_010053.]: Samplename: 0 Root: 1.137 --> Root in between the borders! Added to results. Hyperbolic solved: 11.4129696733689 [20250519_010053.]: Samplename: 12.5 Root: 11.413 --> Root in between the borders! Added to results. Hyperbolic solved: 26.174000526428 [20250519_010053.]: Samplename: 25 Root: 26.174 --> Root in between the borders! Added to results. Hyperbolic solved: 35.1050449117028 [20250519_010053.]: Samplename: 37.5 Root: 35.105 --> Root in between the borders! Added to results. Hyperbolic solved: 47.685500330611 [20250519_010053.]: Samplename: 50 Root: 47.686 --> Root in between the borders! Added to results. Hyperbolic solved: 67.1440494417104 [20250519_010053.]: Samplename: 62.5 Root: 67.144 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7644668894086 [20250519_010053.]: Samplename: 75 Root: 75.764 --> Root in between the borders! Added to results. Hyperbolic solved: 84.4054158616395 [20250519_010053.]: Samplename: 87.5 Root: 84.405 --> Root in between the borders! Added to results. Hyperbolic solved: 100.94827248399 [20250519_010053.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.948 --> '100 < root < 110' --> substitute 100 [20250519_010053.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 0.51235653688495 [20250519_010053.]: Samplename: 0 Root: 0.512 --> Root in between the borders! Added to results. Hyperbolic solved: 10.7523884294604 [20250519_010053.]: Samplename: 12.5 Root: 10.752 --> Root in between the borders! Added to results. Hyperbolic solved: 25.5218907947761 [20250519_010053.]: Samplename: 25 Root: 25.522 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5270462675211 [20250519_010053.]: Samplename: 37.5 Root: 36.527 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7909245028224 [20250519_010053.]: Samplename: 50 Root: 50.791 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8686317550184 [20250519_010053.]: Samplename: 62.5 Root: 64.869 --> Root in between the borders! Added to results. Hyperbolic solved: 77.5524188495235 [20250519_010053.]: Samplename: 75 Root: 77.552 --> Root in between the borders! Added to results. Hyperbolic solved: 80.4374617358174 [20250519_010053.]: Samplename: 87.5 Root: 80.437 --> Root in between the borders! Added to results. Hyperbolic solved: 102.704024900825 [20250519_010053.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.704 --> '100 < root < 110' --> substitute 100 [20250519_010053.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: -0.519503092357606 [20250519_010053.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.52 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.4934147844872 [20250519_010053.]: Samplename: 12.5 Root: 12.493 --> Root in between the borders! Added to results. Hyperbolic solved: 24.2685420024115 [20250519_010053.]: Samplename: 25 Root: 24.269 --> Root in between the borders! Added to results. Hyperbolic solved: 38.0817128465023 [20250519_010053.]: Samplename: 37.5 Root: 38.082 --> Root in between the borders! Added to results. Hyperbolic solved: 48.5843181174811 [20250519_010053.]: Samplename: 50 Root: 48.584 --> Root in between the borders! Added to results. Hyperbolic solved: 67.6722399183037 [20250519_010053.]: Samplename: 62.5 Root: 67.672 --> Root in between the borders! Added to results. Hyperbolic solved: 74.1549277799119 [20250519_010053.]: Samplename: 75 Root: 74.155 --> Root in between the borders! Added to results. Hyperbolic solved: 82.8821797890026 [20250519_010053.]: Samplename: 87.5 Root: 82.882 --> Root in between the borders! Added to results. Hyperbolic solved: 102.0791269023 [20250519_010053.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.079 --> '100 < root < 110' --> substitute 100 [20250519_010053.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 2.41558626275183 [20250519_010053.]: Samplename: 0 Root: 2.416 --> Root in between the borders! Added to results. Hyperbolic solved: 10.1649674907454 [20250519_010053.]: Samplename: 12.5 Root: 10.165 --> Root in between the borders! Added to results. Hyperbolic solved: 23.9830820412762 [20250519_010053.]: Samplename: 25 Root: 23.983 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2773619900429 [20250519_010053.]: Samplename: 37.5 Root: 37.277 --> Root in between the borders! Added to results. Hyperbolic solved: 50.8659386543864 [20250519_010053.]: Samplename: 50 Root: 50.866 --> Root in between the borders! Added to results. Hyperbolic solved: 62.4342273571069 [20250519_010053.]: Samplename: 62.5 Root: 62.434 --> Root in between the borders! Added to results. Hyperbolic solved: 76.3915260534323 [20250519_010053.]: Samplename: 75 Root: 76.392 --> Root in between the borders! Added to results. Hyperbolic solved: 86.159788778566 [20250519_010053.]: Samplename: 87.5 Root: 86.16 --> Root in between the borders! Added to results. Hyperbolic solved: 100.267759893323 [20250519_010053.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.268 --> '100 < root < 110' --> substitute 100 [20250519_010053.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 0.138163748613034 [20250519_010053.]: Samplename: 0 Root: 0.138 --> Root in between the borders! Added to results. Hyperbolic solved: 11.8635558881981 [20250519_010053.]: Samplename: 12.5 Root: 11.864 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5107449550797 [20250519_010053.]: Samplename: 25 Root: 26.511 --> Root in between the borders! Added to results. Hyperbolic solved: 35.3205073050661 [20250519_010053.]: Samplename: 37.5 Root: 35.321 --> Root in between the borders! Added to results. Hyperbolic solved: 50.0570767570666 [20250519_010053.]: Samplename: 50 Root: 50.057 --> Root in between the borders! Added to results. Hyperbolic solved: 64.9602944381018 [20250519_010053.]: Samplename: 62.5 Root: 64.96 --> Root in between the borders! Added to results. Hyperbolic solved: 73.66890571617 [20250519_010053.]: Samplename: 75 Root: 73.669 --> Root in between the borders! Added to results. Hyperbolic solved: 87.1266086585036 [20250519_010053.]: Samplename: 87.5 Root: 87.127 --> Root in between the borders! Added to results. Hyperbolic solved: 100.261637014212 [20250519_010053.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.262 --> '100 < root < 110' --> substitute 100 [20250519_010053.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: -1.37238087287012 [20250519_010053.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -1.372 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.1993162352498 [20250519_010053.]: Samplename: 12.5 Root: 10.199 --> Root in between the borders! Added to results. Hyperbolic solved: 24.595178967123 [20250519_010053.]: Samplename: 25 Root: 24.595 --> Root in between the borders! Added to results. Hyperbolic solved: 37.8310421041787 [20250519_010053.]: Samplename: 37.5 Root: 37.831 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5588739724067 [20250519_010053.]: Samplename: 50 Root: 53.559 --> Root in between the borders! Added to results. Hyperbolic solved: 65.9364947980258 [20250519_010053.]: Samplename: 62.5 Root: 65.936 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7361094434913 [20250519_010053.]: Samplename: 75 Root: 75.736 --> Root in between the borders! Added to results. Hyperbolic solved: 79.432823759854 [20250519_010053.]: Samplename: 87.5 Root: 79.433 --> Root in between the borders! Added to results. Hyperbolic solved: 103.004237013737 [20250519_010053.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 103.004 --> '100 < root < 110' --> substitute 100 [20250519_010053.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 2.80068218205093 [20250519_010053.]: Samplename: 0 Root: 2.801 --> Root in between the borders! Added to results. Hyperbolic solved: 9.27535134596596 [20250519_010053.]: Samplename: 12.5 Root: 9.275 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4762621928197 [20250519_010053.]: Samplename: 25 Root: 25.476 --> Root in between the borders! Added to results. Hyperbolic solved: 34.0122075735416 [20250519_010053.]: Samplename: 37.5 Root: 34.012 --> Root in between the borders! Added to results. Hyperbolic solved: 51.7842655662325 [20250519_010053.]: Samplename: 50 Root: 51.784 --> Root in between the borders! Added to results. Hyperbolic solved: 64.6732311906145 [20250519_010053.]: Samplename: 62.5 Root: 64.673 --> Root in between the borders! Added to results. Hyperbolic solved: 78.4326978859189 [20250519_010053.]: Samplename: 75 Root: 78.433 --> Root in between the borders! Added to results. Hyperbolic solved: 81.3427232852719 [20250519_010053.]: Samplename: 87.5 Root: 81.343 --> Root in between the borders! Added to results. Hyperbolic solved: 101.964406640583 [20250519_010053.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.964 --> '100 < root < 110' --> substitute 100 [20250519_010053.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: -2.13403721845678 [20250519_010053.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.134 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.5082192457956 [20250519_010053.]: Samplename: 12.5 Root: 10.508 --> Root in between the borders! Added to results. Hyperbolic solved: 26.9164567253388 [20250519_010053.]: Samplename: 25 Root: 26.916 --> Root in between the borders! Added to results. Hyperbolic solved: 36.8334779159501 [20250519_010053.]: Samplename: 37.5 Root: 36.833 --> Root in between the borders! Added to results. Hyperbolic solved: 52.0097895977263 [20250519_010053.]: Samplename: 50 Root: 52.01 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8930527921581 [20250519_010053.]: Samplename: 62.5 Root: 64.893 --> Root in between the borders! Added to results. Hyperbolic solved: 74.5671055499357 [20250519_010053.]: Samplename: 75 Root: 74.567 --> Root in between the borders! Added to results. Hyperbolic solved: 84.5294954832669 [20250519_010053.]: Samplename: 87.5 Root: 84.529 --> Root in between the borders! Added to results. Hyperbolic solved: 101.047146466811 [20250519_010053.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.047 --> '100 < root < 110' --> substitute 100 [20250519_010053.]: Solving hyperbolic regression for row_means Hyperbolic solved: 0.290940467580009 [20250519_010053.]: Samplename: 0 Root: 0.291 --> Root in between the borders! Added to results. Hyperbolic solved: 11.041240609297 [20250519_010053.]: Samplename: 12.5 Root: 11.041 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4081503210647 [20250519_010053.]: Samplename: 25 Root: 25.408 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5243723188613 [20250519_010053.]: Samplename: 37.5 Root: 36.524 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7348829528732 [20250519_010053.]: Samplename: 50 Root: 50.735 --> Root in between the borders! Added to results. Hyperbolic solved: 65.3135214250794 [20250519_010053.]: Samplename: 62.5 Root: 65.314 --> Root in between the borders! Added to results. Hyperbolic solved: 75.5342711952113 [20250519_010053.]: Samplename: 75 Root: 75.534 --> Root in between the borders! Added to results. Hyperbolic solved: 83.2411229564887 [20250519_010053.]: Samplename: 87.5 Root: 83.241 --> Root in between the borders! Added to results. Hyperbolic solved: 101.666942268285 [20250519_010053.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.667 --> '100 < root < 110' --> substitute 100 [20250519_010053.]: ### Starting with regression calculations ### [20250519_010053.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_010053.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.1698489850618, 24.4781920312644, 38.173044740918, 52.3349371964438, 65.4582773627666, 75.0090795260796, 81.5271920968417, 100)c(0, 0.330151014938201, 0.521807968735601, 0.673044740918002, 2.3349371964438, 2.95827736276659, 0.00907952607960283, 5.9728079031583, 0)c(NA, 2.64120811950561, 2.0872318749424, 1.79478597578134, 4.66987439288761, 4.73324378042655, 0.0121060347728038, 6.82606617503806, 0) [20250519_010053.]: Logging df_agg: CpG#1 [20250519_010053.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010053.]: c(0, 12.1698489850618, 24.4781920312644, 38.173044740918, 52.3349371964438, 65.4582773627666, 75.0090795260796, 81.5271920968417, 100)[20250519_010053.]: c(0, 0.330151014938201, 0.521807968735601, 0.673044740918002, 2.3349371964438, 2.95827736276659, 0.00907952607960283, 5.9728079031583, 0)[20250519_010053.]: c(NA, 2.64120811950561, 2.0872318749424, 1.79478597578134, 4.66987439288761, 4.73324378042655, 0.0121060347728038, 6.82606617503806, 0) [20250519_010053.]: Entered 'hyperbolic_regression'-Function [20250519_010053.]: 'hyperbolic_regression': minmax = FALSE [20250519_010054.]: Entered 'cubic_regression'-Function [20250519_010054.]: 'cubic_regression': minmax = FALSE [20250519_010054.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.13660501904968, 11.4129696733689, 26.174000526428, 35.1050449117028, 47.685500330611, 67.1440494417104, 75.7644668894086, 84.4054158616395, 100)c(1.13660501904968, 1.0870303266311, 1.174000526428, 2.3949550882972, 2.314499669389, 4.64404944171039, 0.764466889408595, 3.0945841383605, 0)c(NA, 8.6962426130488, 4.696002105712, 6.38654690212587, 4.628999338778, 7.43047910673663, 1.01928918587813, 3.53666758669772, 0) [20250519_010054.]: Logging df_agg: CpG#2 [20250519_010054.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010054.]: c(1.13660501904968, 11.4129696733689, 26.174000526428, 35.1050449117028, 47.685500330611, 67.1440494417104, 75.7644668894086, 84.4054158616395, 100)[20250519_010054.]: c(1.13660501904968, 1.0870303266311, 1.174000526428, 2.3949550882972, 2.314499669389, 4.64404944171039, 0.764466889408595, 3.0945841383605, 0)[20250519_010054.]: c(NA, 8.6962426130488, 4.696002105712, 6.38654690212587, 4.628999338778, 7.43047910673663, 1.01928918587813, 3.53666758669772, 0) [20250519_010054.]: Entered 'hyperbolic_regression'-Function [20250519_010054.]: 'hyperbolic_regression': minmax = FALSE [20250519_010054.]: Entered 'cubic_regression'-Function [20250519_010054.]: 'cubic_regression': minmax = FALSE [20250519_010054.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.51235653688495, 10.7523884294604, 25.5218907947761, 36.5270462675211, 50.7909245028224, 64.8686317550184, 77.5524188495235, 80.4374617358174, 100)c(0.51235653688495, 1.7476115705396, 0.521890794776098, 0.972953732478899, 0.790924502822399, 2.3686317550184, 2.55241884952351, 7.06253826418261, 0)c(NA, 13.9808925643168, 2.08756317910439, 2.5945432866104, 1.5818490056448, 3.78981080802944, 3.40322513269801, 8.07147230192298, 0) [20250519_010054.]: Logging df_agg: CpG#3 [20250519_010054.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010054.]: c(0.51235653688495, 10.7523884294604, 25.5218907947761, 36.5270462675211, 50.7909245028224, 64.8686317550184, 77.5524188495235, 80.4374617358174, 100)[20250519_010054.]: c(0.51235653688495, 1.7476115705396, 0.521890794776098, 0.972953732478899, 0.790924502822399, 2.3686317550184, 2.55241884952351, 7.06253826418261, 0)[20250519_010054.]: c(NA, 13.9808925643168, 2.08756317910439, 2.5945432866104, 1.5818490056448, 3.78981080802944, 3.40322513269801, 8.07147230192298, 0) [20250519_010054.]: Entered 'hyperbolic_regression'-Function [20250519_010054.]: 'hyperbolic_regression': minmax = FALSE [20250519_010054.]: Entered 'cubic_regression'-Function [20250519_010054.]: 'cubic_regression': minmax = FALSE [20250519_010054.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.4934147844872, 24.2685420024115, 38.0817128465023, 48.5843181174811, 67.6722399183037, 74.1549277799119, 82.8821797890026, 100)c(0, 0.00658521551279989, 0.7314579975885, 0.581712846502299, 1.4156818825189, 5.1722399183037, 0.845072220088099, 4.61782021099739, 0)c(NA, 0.0526817241023991, 2.925831990354, 1.55123425733946, 2.83136376503781, 8.27558386928592, 1.12676296011747, 5.27750881256845, 0) [20250519_010054.]: Logging df_agg: CpG#4 [20250519_010054.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010054.]: c(0, 12.4934147844872, 24.2685420024115, 38.0817128465023, 48.5843181174811, 67.6722399183037, 74.1549277799119, 82.8821797890026, 100)[20250519_010054.]: c(0, 0.00658521551279989, 0.7314579975885, 0.581712846502299, 1.4156818825189, 5.1722399183037, 0.845072220088099, 4.61782021099739, 0)[20250519_010054.]: c(NA, 0.0526817241023991, 2.925831990354, 1.55123425733946, 2.83136376503781, 8.27558386928592, 1.12676296011747, 5.27750881256845, 0) [20250519_010054.]: Entered 'hyperbolic_regression'-Function [20250519_010054.]: 'hyperbolic_regression': minmax = FALSE [20250519_010054.]: Entered 'cubic_regression'-Function [20250519_010054.]: 'cubic_regression': minmax = FALSE [20250519_010054.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.41558626275183, 10.1649674907454, 23.9830820412762, 37.2773619900429, 50.8659386543864, 62.4342273571069, 76.3915260534323, 86.159788778566, 100)c(2.41558626275183, 2.3350325092546, 1.0169179587238, 0.222638009957102, 0.865938654386397, 0.0657726428931014, 1.3915260534323, 1.340211221434, 0)c(NA, 18.6802600740368, 4.06767183489519, 0.593701359885604, 1.73187730877279, 0.105236228628962, 1.85536807124307, 1.53166996735315, 0) [20250519_010054.]: Logging df_agg: CpG#5 [20250519_010054.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010054.]: c(2.41558626275183, 10.1649674907454, 23.9830820412762, 37.2773619900429, 50.8659386543864, 62.4342273571069, 76.3915260534323, 86.159788778566, 100)[20250519_010054.]: c(2.41558626275183, 2.3350325092546, 1.0169179587238, 0.222638009957102, 0.865938654386397, 0.0657726428931014, 1.3915260534323, 1.340211221434, 0)[20250519_010054.]: c(NA, 18.6802600740368, 4.06767183489519, 0.593701359885604, 1.73187730877279, 0.105236228628962, 1.85536807124307, 1.53166996735315, 0) [20250519_010054.]: Entered 'hyperbolic_regression'-Function [20250519_010054.]: 'hyperbolic_regression': minmax = FALSE [20250519_010055.]: Entered 'cubic_regression'-Function [20250519_010055.]: 'cubic_regression': minmax = FALSE [20250519_010055.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.138163748613034, 11.8635558881981, 26.5107449550797, 35.3205073050661, 50.0570767570666, 64.9602944381018, 73.66890571617, 87.1266086585036, 100)c(0.138163748613034, 0.6364441118019, 1.5107449550797, 2.1794926949339, 0.0570767570666035, 2.4602944381018, 1.33109428383, 0.373391341496401, 0)c(NA, 5.0915528944152, 6.04297982031881, 5.81198051982373, 0.114153514133207, 3.93647110096288, 1.77479237844, 0.426732961710172, 0) [20250519_010055.]: Logging df_agg: CpG#6 [20250519_010055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010055.]: c(0.138163748613034, 11.8635558881981, 26.5107449550797, 35.3205073050661, 50.0570767570666, 64.9602944381018, 73.66890571617, 87.1266086585036, 100)[20250519_010055.]: c(0.138163748613034, 0.6364441118019, 1.5107449550797, 2.1794926949339, 0.0570767570666035, 2.4602944381018, 1.33109428383, 0.373391341496401, 0)[20250519_010055.]: c(NA, 5.0915528944152, 6.04297982031881, 5.81198051982373, 0.114153514133207, 3.93647110096288, 1.77479237844, 0.426732961710172, 0) [20250519_010055.]: Entered 'hyperbolic_regression'-Function [20250519_010055.]: 'hyperbolic_regression': minmax = FALSE [20250519_010055.]: Entered 'cubic_regression'-Function [20250519_010055.]: 'cubic_regression': minmax = FALSE [20250519_010055.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.1993162352498, 24.595178967123, 37.8310421041787, 53.5588739724067, 65.9364947980258, 75.7361094434913, 79.432823759854, 100)c(0, 2.3006837647502, 0.404821032876999, 0.3310421041787, 3.5588739724067, 3.43649479802581, 0.736109443491301, 8.067176240146, 0)c(NA, 18.4054701180016, 1.619284131508, 0.882778944476532, 7.1177479448134, 5.49839167684129, 0.981479257988402, 9.21962998873828, 0) [20250519_010055.]: Logging df_agg: CpG#7 [20250519_010055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010055.]: c(0, 10.1993162352498, 24.595178967123, 37.8310421041787, 53.5588739724067, 65.9364947980258, 75.7361094434913, 79.432823759854, 100)[20250519_010055.]: c(0, 2.3006837647502, 0.404821032876999, 0.3310421041787, 3.5588739724067, 3.43649479802581, 0.736109443491301, 8.067176240146, 0)[20250519_010055.]: c(NA, 18.4054701180016, 1.619284131508, 0.882778944476532, 7.1177479448134, 5.49839167684129, 0.981479257988402, 9.21962998873828, 0) [20250519_010055.]: Entered 'hyperbolic_regression'-Function [20250519_010055.]: 'hyperbolic_regression': minmax = FALSE [20250519_010055.]: Entered 'cubic_regression'-Function [20250519_010055.]: 'cubic_regression': minmax = FALSE [20250519_010055.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.80068218205093, 9.27535134596596, 25.4762621928197, 34.0122075735416, 51.7842655662325, 64.6732311906145, 78.4326978859189, 81.3427232852719, 100)c(2.80068218205093, 3.22464865403404, 0.4762621928197, 3.4877924264584, 1.7842655662325, 2.1732311906145, 3.4326978859189, 6.1572767147281, 0)c(NA, 25.7971892322723, 1.9050487712788, 9.30077980388906, 3.568531132465, 3.4771699049832, 4.57693051455853, 7.03688767397497, 0) [20250519_010055.]: Logging df_agg: CpG#8 [20250519_010055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010055.]: c(2.80068218205093, 9.27535134596596, 25.4762621928197, 34.0122075735416, 51.7842655662325, 64.6732311906145, 78.4326978859189, 81.3427232852719, 100)[20250519_010055.]: c(2.80068218205093, 3.22464865403404, 0.4762621928197, 3.4877924264584, 1.7842655662325, 2.1732311906145, 3.4326978859189, 6.1572767147281, 0)[20250519_010055.]: c(NA, 25.7971892322723, 1.9050487712788, 9.30077980388906, 3.568531132465, 3.4771699049832, 4.57693051455853, 7.03688767397497, 0) [20250519_010055.]: Entered 'hyperbolic_regression'-Function [20250519_010055.]: 'hyperbolic_regression': minmax = FALSE [20250519_010055.]: Entered 'cubic_regression'-Function [20250519_010055.]: 'cubic_regression': minmax = FALSE [20250519_010055.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.5082192457956, 26.9164567253388, 36.8334779159501, 52.0097895977263, 64.8930527921581, 74.5671055499357, 84.5294954832669, 100)c(0, 1.9917807542044, 1.9164567253388, 0.666522084049902, 2.0097895977263, 2.3930527921581, 0.432894450064296, 2.9705045167331, 0)c(NA, 15.9342460336352, 7.6658269013552, 1.77739222413307, 4.01957919545259, 3.82888446745296, 0.577192600085728, 3.39486230483783, 0) [20250519_010055.]: Logging df_agg: CpG#9 [20250519_010055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010055.]: c(0, 10.5082192457956, 26.9164567253388, 36.8334779159501, 52.0097895977263, 64.8930527921581, 74.5671055499357, 84.5294954832669, 100)[20250519_010055.]: c(0, 1.9917807542044, 1.9164567253388, 0.666522084049902, 2.0097895977263, 2.3930527921581, 0.432894450064296, 2.9705045167331, 0)[20250519_010055.]: c(NA, 15.9342460336352, 7.6658269013552, 1.77739222413307, 4.01957919545259, 3.82888446745296, 0.577192600085728, 3.39486230483783, 0) [20250519_010055.]: Entered 'hyperbolic_regression'-Function [20250519_010055.]: 'hyperbolic_regression': minmax = FALSE [20250519_010056.]: Entered 'cubic_regression'-Function [20250519_010056.]: 'cubic_regression': minmax = FALSE [20250519_010056.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.290940467580009, 11.041240609297, 25.4081503210647, 36.5243723188613, 50.7348829528732, 65.3135214250794, 75.5342711952113, 83.2411229564887, 100)c(0.290940467580009, 1.458759390703, 0.408150321064699, 0.975627681138697, 0.7348829528732, 2.8135214250794, 0.534271195211304, 4.2588770435113, 0)c(NA, 11.670075125624, 1.6326012842588, 2.60167381636986, 1.4697659057464, 4.50163428012704, 0.712361593615071, 4.8672880497272, 0) [20250519_010056.]: Logging df_agg: row_means [20250519_010056.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010056.]: c(0.290940467580009, 11.041240609297, 25.4081503210647, 36.5243723188613, 50.7348829528732, 65.3135214250794, 75.5342711952113, 83.2411229564887, 100)[20250519_010056.]: c(0.290940467580009, 1.458759390703, 0.408150321064699, 0.975627681138697, 0.7348829528732, 2.8135214250794, 0.534271195211304, 4.2588770435113, 0)[20250519_010056.]: c(NA, 11.670075125624, 1.6326012842588, 2.60167381636986, 1.4697659057464, 4.50163428012704, 0.712361593615071, 4.8672880497272, 0) [20250519_010056.]: Entered 'hyperbolic_regression'-Function [20250519_010056.]: 'hyperbolic_regression': minmax = FALSE [20250519_010056.]: Entered 'cubic_regression'-Function [20250519_010056.]: 'cubic_regression': minmax = FALSE [20250519_010056.]: ### Starting with plotting ### [20250519_010056.]: Creating BiasCorrected (hyperbolic) plot No. 1 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG1_corrected_h.png [20250519_010056.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -657.853491622191 , a = 985.267531706584 , b = -657.853491622191 , d = 903.45414536045 [20250519_010056.]: # CpG-site: CpG#1 Cubic: Using a = 5.18463232357963e-06 , b = -0.00180592068110451 , c = 1.11404020909943 , d = -0.858600112691325 [20250519_010058.]: Creating BiasCorrected (hyperbolic) plot No. 2 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG2_corrected_h.png [20250519_010058.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = 3399.55230393578 , a = 30276.0421015763 , b = 3399.55230393578 , d = 30352.4984502595 [20250519_010058.]: # CpG-site: CpG#2 Cubic: Using a = -2.58097549676629e-05 , b = 0.00383753278164476 , c = 0.8516077608074 , d = 0.957127237506517 [20250519_010100.]: Creating BiasCorrected (hyperbolic) plot No. 3 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG3_corrected_h.png [20250519_010100.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = -945.757057025882 , a = 1333.61975246108 , b = -945.757057025882 , d = 1252.95856205348 [20250519_010100.]: # CpG-site: CpG#3 Cubic: Using a = -1.13948611491254e-05 , b = 0.000937180276062016 , c = 0.998630620080636 , d = -0.383337686690576 [20250519_010102.]: Creating BiasCorrected (hyperbolic) plot No. 4 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG4_corrected_h.png [20250519_010102.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = -754.050736416394 , a = 1637.26786961468 , b = -754.050736416394 , d = 1558.27361713715 [20250519_010102.]: # CpG-site: CpG#4 Cubic: Using a = -3.25779988054422e-06 , b = -0.000129596030700936 , c = 1.03077246222439 , d = -0.370343704072989 [20250519_010104.]: Creating BiasCorrected (hyperbolic) plot No. 5 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG5_corrected_h.png [20250519_010104.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = -1917.44420974597 , a = -5621.4534701009 , b = -1917.44420974597 , d = -5734.47300613195 [20250519_010104.]: # CpG-site: CpG#5 Cubic: Using a = -3.16782834876228e-05 , b = 0.00493527189107616 , c = 0.800194601834967 , d = 1.38653015028503 [20250519_010106.]: Creating BiasCorrected (hyperbolic) plot No. 6 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG6_corrected_h.png [20250519_010106.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = -1255.67712347228 , a = 13775.5934089325 , b = -1255.67712347228 , d = 13690.4825730065 [20250519_010106.]: # CpG-site: CpG#6 Cubic: Using a = -3.70201684653088e-06 , b = 0.000482210694203131 , c = 0.985293133118367 , d = 0.0293568698433451 [20250519_010108.]: Creating BiasCorrected (hyperbolic) plot No. 7 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG7_corrected_h.png [20250519_010108.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -1065.05415478934 , a = 722.083975601617 , b = -1065.05415478934 , d = 634.402060442185 [20250519_010108.]: # CpG-site: CpG#7 Cubic: Using a = -1.04111972402046e-06 , b = -0.00130150084904922 , c = 1.12399516228677 , d = -1.5985899870939 [20250519_010110.]: Creating BiasCorrected (hyperbolic) plot No. 8 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG8_corrected_h.png [20250519_010110.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = 68.5784871630731 , a = 4784.90969361316 , b = 68.5784871630731 , d = 4739.12296431021 [20250519_010110.]: # CpG-site: CpG#8 Cubic: Using a = -4.65730502627556e-05 , b = 0.00676525570440644 , c = 0.747405744075959 , d = 1.52337158754213 [20250519_010111.]: Creating BiasCorrected (hyperbolic) plot No. 9 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG9_corrected_h.png [20250519_010111.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -1022.70169872734 , a = 1273.73039431947 , b = -1022.70169872734 , d = 1181.86496720377 [20250519_010111.]: # CpG-site: CpG#9 Cubic: Using a = 4.24790097543708e-06 , b = -0.00144169637811555 , c = 1.09837791472274 , d = -0.97898795630014 [20250519_010113.]: Creating BiasCorrected (hyperbolic) plot No. 10 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_rowmeans_corrected_h.png [20250519_010113.]: # CpG-site: row_means Hyperbolic: Using bias_weight = -1166.73858297355 , a = 1740.63784318208 , b = -1166.73858297355 , d = 1653.86071043116 [20250519_010113.]: # CpG-site: row_means Cubic: Using a = -8.71186803343537e-06 , b = 0.000715553560638339 , c = 1.00248252890793 , d = -0.420554394203132 [20250519_010115.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG1_corrected_h.png [20250519_010117.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG2_corrected_h.png [20250519_010119.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG3_corrected_h.png [20250519_010121.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG4_corrected_h.png [20250519_010122.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG5_corrected_h.png [20250519_010124.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG6_corrected_h.png [20250519_010126.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG7_corrected_h.png [20250519_010128.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG8_corrected_h.png [20250519_010129.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG9_corrected_h.png [20250519_010131.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_rowmeans_corrected_h.png [20250519_010133.]: Entered 'solving_equations'-Function [20250519_010133.]: Solving cubic regression for CpG#1 Coefficients: -1.03617340067344Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_010133.]: Samplename: 0 Root: 1.334 --> Root in between the borders! Added to results. Coefficients: -8.34150673400678Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_010133.]: Samplename: 12.5 Root: 11.446 --> Root in between the borders! Added to results. Coefficients: -15.3881734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_010133.]: Samplename: 25 Root: 22.228 --> Root in between the borders! Added to results. Coefficients: -24.2801734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_010133.]: Samplename: 37.5 Root: 36.374 --> Root in between the borders! Added to results. Coefficients: -34.9006734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_010133.]: Samplename: 50 Root: 52.044 --> Root in between the borders! Added to results. Coefficients: -46.3541734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_010133.]: Samplename: 62.5 Root: 66.144 --> Root in between the borders! Added to results. Coefficients: -55.8931734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_010133.]: Samplename: 75 Root: 75.864 --> Root in between the borders! Added to results. Coefficients: -63.0981734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_010133.]: Samplename: 87.5 Root: 82.254 --> Root in between the borders! Added to results. Coefficients: -91.0461734006735Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_010133.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.877 --> '100 < root < 110' --> substitute 100 [20250519_010133.]: Solving cubic regression for CpG#2 Coefficients: -0.283329966329966Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_010133.]: Samplename: 0 Root: 0.549 --> Root in between the borders! Added to results. Coefficients: -6.33999663299663Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_010133.]: Samplename: 12.5 Root: 11.533 --> Root in between the borders! Added to results. Coefficients: -15.93932996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_010133.]: Samplename: 25 Root: 26.628 --> Root in between the borders! Added to results. Coefficients: -22.33732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_010133.]: Samplename: 37.5 Root: 35.509 --> Root in between the borders! Added to results. Coefficients: -32.22832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_010133.]: Samplename: 50 Root: 47.851 --> Root in between the borders! Added to results. Coefficients: -49.96332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_010133.]: Samplename: 62.5 Root: 66.893 --> Root in between the borders! Added to results. Coefficients: -58.96582996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_010133.]: Samplename: 75 Root: 75.431 --> Root in between the borders! Added to results. Coefficients: -68.8366632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_010133.]: Samplename: 87.5 Root: 84.118 --> Root in between the borders! Added to results. Coefficients: -90.57732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_010133.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.287 --> '100 < root < 110' --> substitute 100 [20250519_010133.]: Solving cubic regression for CpG#3 Coefficients: -0.90294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_010133.]: Samplename: 0 Root: 1.441 --> Root in between the borders! Added to results. Coefficients: -6.57294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_010133.]: Samplename: 12.5 Root: 10.568 --> Root in between the borders! Added to results. Coefficients: -15.4289478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_010133.]: Samplename: 25 Root: 24.796 --> Root in between the borders! Added to results. Coefficients: -22.6129478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_010133.]: Samplename: 37.5 Root: 35.952 --> Root in between the borders! Added to results. Coefficients: -32.7754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_010133.]: Samplename: 50 Root: 50.684 --> Root in between the borders! Added to results. Coefficients: -43.8889478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_010133.]: Samplename: 62.5 Root: 65.142 --> Root in between the borders! Added to results. Coefficients: -54.9754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_010133.]: Samplename: 75 Root: 77.905 --> Root in between the borders! Added to results. Coefficients: -57.6562811447812Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_010133.]: Samplename: 87.5 Root: 80.767 --> Root in between the borders! Added to results. Coefficients: -80.6649478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_010133.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.38 --> '100 < root < 110' --> substitute 100 [20250519_010133.]: Solving cubic regression for CpG#4 Coefficients: -0.597449494949524Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_010133.]: Samplename: 0 Root: 0.858 --> Root in between the borders! Added to results. Coefficients: -8.25278282828286Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_010133.]: Samplename: 12.5 Root: 12.086 --> Root in between the borders! Added to results. Coefficients: -15.8034494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_010133.]: Samplename: 25 Root: 23.316 --> Root in between the borders! Added to results. Coefficients: -25.5274494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_010133.]: Samplename: 37.5 Root: 37.383 --> Root in between the borders! Added to results. Coefficients: -33.6369494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_010133.]: Samplename: 50 Root: 48.353 --> Root in between the borders! Added to results. Coefficients: -50.2554494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_010133.]: Samplename: 62.5 Root: 68.082 --> Root in between the borders! Added to results. Coefficients: -56.5394494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_010133.]: Samplename: 75 Root: 74.615 --> Root in between the borders! Added to results. Coefficients: -65.5927828282829Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_010133.]: Samplename: 87.5 Root: 83.254 --> Root in between the borders! Added to results. Coefficients: -88.3214494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_010133.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.715 --> '100 < root < 110' --> substitute 100 [20250519_010133.]: Solving cubic regression for CpG#5 Coefficients: -0.623961279461278Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_010133.]: Samplename: 0 Root: 1.458 --> Root in between the borders! Added to results. Coefficients: -4.76796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_010133.]: Samplename: 12.5 Root: 10.347 --> Root in between the borders! Added to results. Coefficients: -12.7259612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_010133.]: Samplename: 25 Root: 24.815 --> Root in between the borders! Added to results. Coefficients: -21.1599612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_010133.]: Samplename: 37.5 Root: 37.902 --> Root in between the borders! Added to results. Coefficients: -30.6954612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_010133.]: Samplename: 50 Root: 50.977 --> Root in between the borders! Added to results. Coefficients: -39.6579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_010133.]: Samplename: 62.5 Root: 62.126 --> Root in between the borders! Added to results. Coefficients: -51.6829612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_010133.]: Samplename: 75 Root: 75.852 --> Root in between the borders! Added to results. Coefficients: -61.0146279461279Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_010133.]: Samplename: 87.5 Root: 85.767 --> Root in between the borders! Added to results. Coefficients: -76.0699612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_010133.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.743 --> '100 < root < 110' --> substitute 100 [20250519_010133.]: Solving cubic regression for CpG#6 Coefficients: -0.196072390572403Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_010133.]: Samplename: 0 Root: 0.349 --> Root in between the borders! Added to results. Coefficients: -6.73873905723907Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_010133.]: Samplename: 12.5 Root: 11.718 --> Root in between the borders! Added to results. Coefficients: -15.8880723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_010133.]: Samplename: 25 Root: 26.396 --> Root in between the borders! Added to results. Coefficients: -22.0000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_010133.]: Samplename: 37.5 Root: 35.301 --> Root in between the borders! Added to results. Coefficients: -33.4445723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_010133.]: Samplename: 50 Root: 50.134 --> Root in between the borders! Added to results. Coefficients: -46.9000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_010133.]: Samplename: 62.5 Root: 64.993 --> Root in between the borders! Added to results. Coefficients: -55.8320723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_010133.]: Samplename: 75 Root: 73.639 --> Root in between the borders! Added to results. Coefficients: -71.5454057239057Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_010133.]: Samplename: 87.5 Root: 87.043 --> Root in between the borders! Added to results. Coefficients: -89.6560723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_010133.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.329 --> '100 < root < 110' --> substitute 100 [20250519_010133.]: Solving cubic regression for CpG#7 Coefficients: -1.21495454545456Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_010133.]: Samplename: 0 Root: 2.13 --> Root in between the borders! Added to results. Coefficients: -5.39562121212123Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_010133.]: Samplename: 12.5 Root: 9.973 --> Root in between the borders! Added to results. Coefficients: -11.2649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_010133.]: Samplename: 25 Root: 22.206 --> Root in between the borders! Added to results. Coefficients: -17.4509545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_010133.]: Samplename: 37.5 Root: 35.814 --> Root in between the borders! Added to results. Coefficients: -26.0314545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_010133.]: Samplename: 50 Root: 53.28 --> Root in between the borders! Added to results. Coefficients: -33.9649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_010133.]: Samplename: 62.5 Root: 66.598 --> Root in between the borders! Added to results. Coefficients: -41.1689545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_010133.]: Samplename: 75 Root: 76.575 --> Root in between the borders! Added to results. Coefficients: -44.1356212121212Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_010133.]: Samplename: 87.5 Root: 80.219 --> Root in between the borders! Added to results. Coefficients: -67.2229545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_010133.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.506 --> '100 < root < 110' --> substitute 100 [20250519_010133.]: Solving cubic regression for CpG#8 Coefficients: -1.09618518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_010133.]: Samplename: 0 Root: 2.016 --> Root in between the borders! Added to results. Coefficients: -5.44685185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_010133.]: Samplename: 12.5 Root: 9.458 --> Root in between the borders! Added to results. Coefficients: -16.9301851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_010133.]: Samplename: 25 Root: 26.35 --> Root in between the borders! Added to results. Coefficients: -23.3501851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_010133.]: Samplename: 37.5 Root: 34.728 --> Root in between the borders! Added to results. Coefficients: -37.6251851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_010133.]: Samplename: 50 Root: 51.781 --> Root in between the borders! Added to results. Coefficients: -48.8261851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_010133.]: Samplename: 62.5 Root: 64.192 --> Root in between the borders! Added to results. Coefficients: -61.6676851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_010133.]: Samplename: 75 Root: 77.804 --> Root in between the borders! Added to results. Coefficients: -64.5101851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_010133.]: Samplename: 87.5 Root: 80.758 --> Root in between the borders! Added to results. Coefficients: -86.0601851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_010133.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.834 --> '100 < root < 110' --> substitute 100 [20250519_010133.]: Solving cubic regression for CpG#9 Coefficients: -0.989865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_010133.]: Samplename: 0 Root: 1.475 --> Root in between the borders! Added to results. Coefficients: -6.39586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_010133.]: Samplename: 12.5 Root: 10.12 --> Root in between the borders! Added to results. Coefficients: -14.7058653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_010133.]: Samplename: 25 Root: 24.844 --> Root in between the borders! Added to results. Coefficients: -20.6238653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_010133.]: Samplename: 37.5 Root: 35.327 --> Root in between the borders! Added to results. Coefficients: -31.3958653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_010133.]: Samplename: 50 Root: 51.855 --> Root in between the borders! Added to results. Coefficients: -42.6858653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_010133.]: Samplename: 62.5 Root: 65.265 --> Root in between the borders! Added to results. Coefficients: -52.9033653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_010133.]: Samplename: 75 Root: 74.915 --> Root in between the borders! Added to results. Coefficients: -65.492531986532Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_010133.]: Samplename: 87.5 Root: 84.67 --> Root in between the borders! Added to results. Coefficients: -92.9898653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_010133.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.082 --> '100 < root < 110' --> substitute 100 [20250519_010133.]: Solving cubic regression for row_means Coefficients: -0.771215488215478Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_010133.]: Samplename: 0 Root: 1.287 --> Root in between the borders! Added to results. Coefficients: -6.47247474747474Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_010133.]: Samplename: 12.5 Root: 10.847 --> Root in between the borders! Added to results. Coefficients: -14.8972154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_010133.]: Samplename: 25 Root: 24.737 --> Root in between the borders! Added to results. Coefficients: -22.1492154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_010133.]: Samplename: 37.5 Root: 36.02 --> Root in between the borders! Added to results. Coefficients: -32.5259932659933Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_010133.]: Samplename: 50 Root: 50.639 --> Root in between the borders! Added to results. Coefficients: -44.7218821548821Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_010133.]: Samplename: 62.5 Root: 65.497 --> Root in between the borders! Added to results. Coefficients: -54.4032154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_010133.]: Samplename: 75 Root: 75.751 --> Root in between the borders! Added to results. Coefficients: -62.4313636363636Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_010133.]: Samplename: 87.5 Root: 83.403 --> Root in between the borders! Added to results. Coefficients: -84.7343265993266Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_010133.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.573 --> '100 < root < 110' --> substitute 100 [20250519_010133.]: ### Starting with regression calculations ### [20250519_010133.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_010133.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) [20250519_010133.]: Logging df_agg: CpG#1 [20250519_010133.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010133.]: c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)[20250519_010133.]: c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)[20250519_010133.]: c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) [20250519_010133.]: Entered 'hyperbolic_regression'-Function [20250519_010133.]: 'hyperbolic_regression': minmax = FALSE [20250519_010133.]: Entered 'cubic_regression'-Function [20250519_010133.]: 'cubic_regression': minmax = FALSE [20250519_010133.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) [20250519_010133.]: Logging df_agg: CpG#2 [20250519_010133.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010133.]: c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)[20250519_010133.]: c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)[20250519_010133.]: c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) [20250519_010133.]: Entered 'hyperbolic_regression'-Function [20250519_010133.]: 'hyperbolic_regression': minmax = FALSE [20250519_010134.]: Entered 'cubic_regression'-Function [20250519_010134.]: 'cubic_regression': minmax = FALSE [20250519_010134.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) [20250519_010134.]: Logging df_agg: CpG#3 [20250519_010134.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010134.]: c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)[20250519_010134.]: c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)[20250519_010134.]: c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) [20250519_010134.]: Entered 'hyperbolic_regression'-Function [20250519_010134.]: 'hyperbolic_regression': minmax = FALSE [20250519_010134.]: Entered 'cubic_regression'-Function [20250519_010134.]: 'cubic_regression': minmax = FALSE [20250519_010134.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) [20250519_010134.]: Logging df_agg: CpG#4 [20250519_010134.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010134.]: c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)[20250519_010134.]: c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)[20250519_010134.]: c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) [20250519_010134.]: Entered 'hyperbolic_regression'-Function [20250519_010134.]: 'hyperbolic_regression': minmax = FALSE [20250519_010134.]: Entered 'cubic_regression'-Function [20250519_010134.]: 'cubic_regression': minmax = FALSE [20250519_010134.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) [20250519_010134.]: Logging df_agg: CpG#5 [20250519_010134.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010134.]: c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)[20250519_010134.]: c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)[20250519_010134.]: c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) [20250519_010134.]: Entered 'hyperbolic_regression'-Function [20250519_010134.]: 'hyperbolic_regression': minmax = FALSE [20250519_010134.]: Entered 'cubic_regression'-Function [20250519_010134.]: 'cubic_regression': minmax = FALSE [20250519_010134.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) [20250519_010134.]: Logging df_agg: CpG#6 [20250519_010134.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010134.]: c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)[20250519_010134.]: c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)[20250519_010134.]: c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) [20250519_010134.]: Entered 'hyperbolic_regression'-Function [20250519_010134.]: 'hyperbolic_regression': minmax = FALSE [20250519_010135.]: Entered 'cubic_regression'-Function [20250519_010135.]: 'cubic_regression': minmax = FALSE [20250519_010135.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) [20250519_010135.]: Logging df_agg: CpG#7 [20250519_010135.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010135.]: c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)[20250519_010135.]: c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)[20250519_010135.]: c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) [20250519_010135.]: Entered 'hyperbolic_regression'-Function [20250519_010135.]: 'hyperbolic_regression': minmax = FALSE [20250519_010135.]: Entered 'cubic_regression'-Function [20250519_010135.]: 'cubic_regression': minmax = FALSE [20250519_010135.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) [20250519_010135.]: Logging df_agg: CpG#8 [20250519_010135.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010135.]: c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)[20250519_010135.]: c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)[20250519_010135.]: c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) [20250519_010135.]: Entered 'hyperbolic_regression'-Function [20250519_010135.]: 'hyperbolic_regression': minmax = FALSE [20250519_010135.]: Entered 'cubic_regression'-Function [20250519_010135.]: 'cubic_regression': minmax = FALSE [20250519_010135.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) [20250519_010135.]: Logging df_agg: CpG#9 [20250519_010135.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010135.]: c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)[20250519_010135.]: c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)[20250519_010135.]: c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) [20250519_010135.]: Entered 'hyperbolic_regression'-Function [20250519_010135.]: 'hyperbolic_regression': minmax = FALSE [20250519_010135.]: Entered 'cubic_regression'-Function [20250519_010135.]: 'cubic_regression': minmax = FALSE [20250519_010135.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) [20250519_010135.]: Logging df_agg: row_means [20250519_010135.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010135.]: c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)[20250519_010135.]: c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)[20250519_010135.]: c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) [20250519_010135.]: Entered 'hyperbolic_regression'-Function [20250519_010135.]: 'hyperbolic_regression': minmax = FALSE [20250519_010136.]: Entered 'cubic_regression'-Function [20250519_010136.]: 'cubic_regression': minmax = FALSE [20250519_010136.]: ### Starting with plotting ### [20250519_010136.]: Creating BiasCorrected (cubic) plot No. 1 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG1_corrected_c.png [20250519_010136.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -1164.77580684101 , a = 2039.49844669959 , b = -1164.77580684101 , d = 1955.83553683067 [20250519_010136.]: # CpG-site: CpG#1 Cubic: Using a = -3.42821196240366e-05 , b = 0.00462173155013798 , c = 0.850326258865826 , d = 0.500073874211125 [20250519_010138.]: Creating BiasCorrected (cubic) plot No. 2 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG2_corrected_c.png [20250519_010138.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = -464.128853555559 , a = 4988.85258428155 , b = -464.128853555559 , d = 4925.32559808582 [20250519_010138.]: # CpG-site: CpG#2 Cubic: Using a = -1.40811796906314e-05 , b = 0.00190849431754485 , c = 0.933455870941473 , d = 0.363096210099674 [20250519_010140.]: Creating BiasCorrected (cubic) plot No. 3 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG3_corrected_c.png [20250519_010140.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = -745.611435752166 , a = 2026.53033232932 , b = -745.611435752166 , d = 1953.28002286922 [20250519_010140.]: # CpG-site: CpG#3 Cubic: Using a = -2.78835051857186e-05 , b = 0.00366863707889089 , c = 0.880665171220893 , d = 0.510779308833653 [20250519_010141.]: Creating BiasCorrected (cubic) plot No. 4 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG4_corrected_c.png [20250519_010141.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = -833.073059054674 , a = 2871.1407505286 , b = -833.073059054674 , d = 2796.8155099796 [20250519_010141.]: # CpG-site: CpG#4 Cubic: Using a = -2.30028865643e-05 , b = 0.00309017851940692 , c = 0.897073107777645 , d = 0.443455761841866 [20250519_010143.]: Creating BiasCorrected (cubic) plot No. 5 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG5_corrected_c.png [20250519_010143.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = 222.127659665624 , a = 12477.5957294815 , b = 222.127659665624 , d = 12432.2636419037 [20250519_010143.]: # CpG-site: CpG#5 Cubic: Using a = -1.28632696989134e-05 , b = 0.00184822285299014 , c = 0.931001046631176 , d = 0.437963095185599 [20250519_010145.]: Creating BiasCorrected (cubic) plot No. 6 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG6_corrected_c.png [20250519_010145.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = -700.510534379779 , a = 21299.1825568909 , b = -700.510534379779 , d = 21234.5878606004 [20250519_010145.]: # CpG-site: CpG#6 Cubic: Using a = -5.838335151375e-06 , b = 0.000828447163218526 , c = 0.970047764475166 , d = 0.158074701412251 [20250519_010147.]: Creating BiasCorrected (cubic) plot No. 7 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG7_corrected_c.png [20250519_010147.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -1144.90822837929 , a = 1477.91227480711 , b = -1144.90822837929 , d = 1394.70075909546 [20250519_010147.]: # CpG-site: CpG#7 Cubic: Using a = -4.54220535614047e-05 , b = 0.00607993130565131 , c = 0.805411493452389 , d = 0.615125382790254 [20250519_010149.]: Creating BiasCorrected (cubic) plot No. 8 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG8_corrected_c.png [20250519_010149.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = -349.378022452652 , a = 2377.37834502072 , b = -349.378022452652 , d = 2315.2055766781 [20250519_010149.]: # CpG-site: CpG#8 Cubic: Using a = -2.64622306669639e-05 , b = 0.00353621593528015 , c = 0.877916844709074 , d = 0.698684606493051 [20250519_010151.]: Creating BiasCorrected (cubic) plot No. 9 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG9_corrected_c.png [20250519_010151.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -1536.91601393605 , a = 3745.17687624221 , b = -1536.91601393605 , d = 3656.2687244524 [20250519_010151.]: # CpG-site: CpG#9 Cubic: Using a = -2.70976130611725e-05 , b = 0.00378478247182342 , c = 0.871825263051793 , d = 0.455475740974113 [20250519_010153.]: Creating BiasCorrected (cubic) plot No. 10 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_rowmeans_corrected_c.png [20250519_010153.]: # CpG-site: row_means Hyperbolic: Using bias_weight = -820.650348722241 , a = 3198.46441212347 , b = -820.650348722241 , d = 3124.20895615508 [20250519_010153.]: # CpG-site: row_means Cubic: Using a = -2.34328362100009e-05 , b = 0.00319139073176266 , c = 0.891842168032129 , d = 0.493517689787135 [20250519_010155.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG1_corrected_c.png [20250519_010157.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG2_corrected_c.png [20250519_010158.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG3_corrected_c.png [20250519_010200.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG4_corrected_c.png [20250519_010202.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG5_corrected_c.png [20250519_010204.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG6_corrected_c.png [20250519_010206.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG7_corrected_c.png [20250519_010207.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG8_corrected_c.png [20250519_010209.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG9_corrected_c.png [20250519_010211.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_rowmeans_corrected_c.png [20250519_010213.]: Entered 'solving_equations'-Function [20250519_010213.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 79.8673456895745 [20250519_010213.]: Samplename: Sample#1 Root: 79.867 --> Root in between the borders! Added to results. Hyperbolic solved: 29.7900184340805 [20250519_010213.]: Samplename: Sample#10 Root: 29.79 --> Root in between the borders! Added to results. Hyperbolic solved: 41.6525415639691 [20250519_010213.]: Samplename: Sample#2 Root: 41.653 --> Root in between the borders! Added to results. Hyperbolic solved: 57.4652090254513 [20250519_010213.]: Samplename: Sample#3 Root: 57.465 --> Root in between the borders! Added to results. Hyperbolic solved: 9.2007130627765 [20250519_010213.]: Samplename: Sample#4 Root: 9.201 --> Root in between the borders! Added to results. Hyperbolic solved: 21.8059600538131 [20250519_010213.]: Samplename: Sample#5 Root: 21.806 --> Root in between the borders! Added to results. Hyperbolic solved: 23.083796735881 [20250519_010213.]: Samplename: Sample#6 Root: 23.084 --> Root in between the borders! Added to results. Hyperbolic solved: 45.5034245569385 [20250519_010213.]: Samplename: Sample#7 Root: 45.503 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6987904075704 [20250519_010213.]: Samplename: Sample#8 Root: 85.699 --> Root in between the borders! Added to results. Hyperbolic solved: -3.66512807265101 [20250519_010213.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -3.665 --> '-10 < root < 0' --> substitute 0 [20250519_010213.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 76.7227931065881 [20250519_010213.]: Samplename: Sample#1 Root: 76.723 --> Root in between the borders! Added to results. Hyperbolic solved: 30.992427511473 [20250519_010213.]: Samplename: Sample#10 Root: 30.992 --> Root in between the borders! Added to results. Hyperbolic solved: 42.6810882780827 [20250519_010213.]: Samplename: Sample#2 Root: 42.681 --> Root in between the borders! Added to results. Hyperbolic solved: 58.9337908379889 [20250519_010213.]: Samplename: Sample#3 Root: 58.934 --> Root in between the borders! Added to results. Hyperbolic solved: 4.98020457313721 [20250519_010213.]: Samplename: Sample#4 Root: 4.98 --> Root in between the borders! Added to results. Hyperbolic solved: 20.2480263675669 [20250519_010213.]: Samplename: Sample#5 Root: 20.248 --> Root in between the borders! Added to results. Hyperbolic solved: 17.8133085643845 [20250519_010213.]: Samplename: Sample#6 Root: 17.813 --> Root in between the borders! Added to results. Hyperbolic solved: 40.9176745340012 [20250519_010213.]: Samplename: Sample#7 Root: 40.918 --> Root in between the borders! Added to results. Hyperbolic solved: 86.0450715732916 [20250519_010213.]: Samplename: Sample#8 Root: 86.045 --> Root in between the borders! Added to results. Hyperbolic solved: 3.11579770508969 [20250519_010213.]: Samplename: Sample#9 Root: 3.116 --> Root in between the borders! Added to results. [20250519_010213.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 74.9349254100163 [20250519_010213.]: Samplename: Sample#1 Root: 74.935 --> Root in between the borders! Added to results. Hyperbolic solved: 27.6844381581493 [20250519_010213.]: Samplename: Sample#10 Root: 27.684 --> Root in between the borders! Added to results. Hyperbolic solved: 41.852019114379 [20250519_010213.]: Samplename: Sample#2 Root: 41.852 --> Root in between the borders! Added to results. Hyperbolic solved: 55.8325180209418 [20250519_010213.]: Samplename: Sample#3 Root: 55.833 --> Root in between the borders! Added to results. Hyperbolic solved: 8.03519251633153 [20250519_010213.]: Samplename: Sample#4 Root: 8.035 --> Root in between the borders! Added to results. Hyperbolic solved: 24.1066315721853 [20250519_010213.]: Samplename: Sample#5 Root: 24.107 --> Root in between the borders! Added to results. Hyperbolic solved: 26.2419820027673 [20250519_010213.]: Samplename: Sample#6 Root: 26.242 --> Root in between the borders! Added to results. Hyperbolic solved: 44.0944922703422 [20250519_010213.]: Samplename: Sample#7 Root: 44.094 --> Root in between the borders! Added to results. Hyperbolic solved: 85.8279382585787 [20250519_010213.]: Samplename: Sample#8 Root: 85.828 --> Root in between the borders! Added to results. Hyperbolic solved: -0.666482392725758 [20250519_010213.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.666 --> '-10 < root < 0' --> substitute 0 [20250519_010213.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 76.3495278640236 [20250519_010213.]: Samplename: Sample#1 Root: 76.35 --> Root in between the borders! Added to results. Hyperbolic solved: 28.2568553570941 [20250519_010213.]: Samplename: Sample#10 Root: 28.257 --> Root in between the borders! Added to results. Hyperbolic solved: 43.4089839390807 [20250519_010213.]: Samplename: Sample#2 Root: 43.409 --> Root in between the borders! Added to results. Hyperbolic solved: 58.5435236860146 [20250519_010213.]: Samplename: Sample#3 Root: 58.544 --> Root in between the borders! Added to results. Hyperbolic solved: 10.3087045690571 [20250519_010213.]: Samplename: Sample#4 Root: 10.309 --> Root in between the borders! Added to results. Hyperbolic solved: 22.183045165659 [20250519_010213.]: Samplename: Sample#5 Root: 22.183 --> Root in between the borders! Added to results. Hyperbolic solved: 27.1337769553499 [20250519_010213.]: Samplename: Sample#6 Root: 27.134 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8321096080155 [20250519_010213.]: Samplename: Sample#7 Root: 41.832 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6890189074743 [20250519_010213.]: Samplename: Sample#8 Root: 85.689 --> Root in between the borders! Added to results. Hyperbolic solved: 2.42232098177269 [20250519_010213.]: Samplename: Sample#9 Root: 2.422 --> Root in between the borders! Added to results. [20250519_010213.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 72.8126914355011 [20250519_010213.]: Samplename: Sample#1 Root: 72.813 --> Root in between the borders! Added to results. Hyperbolic solved: 26.4206935815673 [20250519_010213.]: Samplename: Sample#10 Root: 26.421 --> Root in between the borders! Added to results. Hyperbolic solved: 44.2738765057261 [20250519_010213.]: Samplename: Sample#2 Root: 44.274 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5324386232341 [20250519_010213.]: Samplename: Sample#3 Root: 53.532 --> Root in between the borders! Added to results. Hyperbolic solved: 10.0373717962154 [20250519_010213.]: Samplename: Sample#4 Root: 10.037 --> Root in between the borders! Added to results. Hyperbolic solved: 22.8733115052607 [20250519_010213.]: Samplename: Sample#5 Root: 22.873 --> Root in between the borders! Added to results. Hyperbolic solved: 25.9417915784748 [20250519_010213.]: Samplename: Sample#6 Root: 25.942 --> Root in between the borders! Added to results. Hyperbolic solved: 42.7945074918604 [20250519_010213.]: Samplename: Sample#7 Root: 42.795 --> Root in between the borders! Added to results. Hyperbolic solved: 88.8741214630346 [20250519_010213.]: Samplename: Sample#8 Root: 88.874 --> Root in between the borders! Added to results. Hyperbolic solved: 2.34647529966966 [20250519_010213.]: Samplename: Sample#9 Root: 2.346 --> Root in between the borders! Added to results. [20250519_010213.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 79.2780593622711 [20250519_010213.]: Samplename: Sample#1 Root: 79.278 --> Root in between the borders! Added to results. Hyperbolic solved: 30.2012458984074 [20250519_010213.]: Samplename: Sample#10 Root: 30.201 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8474393624107 [20250519_010213.]: Samplename: Sample#2 Root: 41.847 --> Root in between the borders! Added to results. Hyperbolic solved: 56.8423517321508 [20250519_010213.]: Samplename: Sample#3 Root: 56.842 --> Root in between the borders! Added to results. Hyperbolic solved: 8.87856046118588 [20250519_010213.]: Samplename: Sample#4 Root: 8.879 --> Root in between the borders! Added to results. Hyperbolic solved: 18.69015950004 [20250519_010213.]: Samplename: Sample#5 Root: 18.69 --> Root in between the borders! Added to results. Hyperbolic solved: 29.9309263534749 [20250519_010213.]: Samplename: Sample#6 Root: 29.931 --> Root in between the borders! Added to results. Hyperbolic solved: 42.8148560027697 [20250519_010213.]: Samplename: Sample#7 Root: 42.815 --> Root in between the borders! Added to results. Hyperbolic solved: 86.7501831416152 [20250519_010213.]: Samplename: Sample#8 Root: 86.75 --> Root in between the borders! Added to results. Hyperbolic solved: 1.51516194985267 [20250519_010213.]: Samplename: Sample#9 Root: 1.515 --> Root in between the borders! Added to results. [20250519_010213.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 78.2565592569279 [20250519_010213.]: Samplename: Sample#1 Root: 78.257 --> Root in between the borders! Added to results. Hyperbolic solved: 25.488739349283 [20250519_010213.]: Samplename: Sample#10 Root: 25.489 --> Root in between the borders! Added to results. Hyperbolic solved: 47.3712258915285 [20250519_010213.]: Samplename: Sample#2 Root: 47.371 --> Root in between the borders! Added to results. Hyperbolic solved: 58.3142673189298 [20250519_010213.]: Samplename: Sample#3 Root: 58.314 --> Root in between the borders! Added to results. Hyperbolic solved: 11.7212231360573 [20250519_010213.]: Samplename: Sample#4 Root: 11.721 --> Root in between the borders! Added to results. Hyperbolic solved: 25.3797485992238 [20250519_010213.]: Samplename: Sample#5 Root: 25.38 --> Root in between the borders! Added to results. Hyperbolic solved: 29.4095133062523 [20250519_010213.]: Samplename: Sample#6 Root: 29.41 --> Root in between the borders! Added to results. Hyperbolic solved: 44.5755071469546 [20250519_010213.]: Samplename: Sample#7 Root: 44.576 --> Root in between the borders! Added to results. Hyperbolic solved: 85.9628731021447 [20250519_010213.]: Samplename: Sample#8 Root: 85.963 --> Root in between the borders! Added to results. Hyperbolic solved: -4.1645647175353 [20250519_010213.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -4.165 --> '-10 < root < 0' --> substitute 0 [20250519_010213.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 72.9696605050898 [20250519_010213.]: Samplename: Sample#1 Root: 72.97 --> Root in between the borders! Added to results. Hyperbolic solved: 27.8250956361743 [20250519_010213.]: Samplename: Sample#10 Root: 27.825 --> Root in between the borders! Added to results. Hyperbolic solved: 34.9068388175484 [20250519_010213.]: Samplename: Sample#2 Root: 34.907 --> Root in between the borders! Added to results. Hyperbolic solved: 59.163743824213 [20250519_010213.]: Samplename: Sample#3 Root: 59.164 --> Root in between the borders! Added to results. Hyperbolic solved: 9.64644413255063 [20250519_010213.]: Samplename: Sample#4 Root: 9.646 --> Root in between the borders! Added to results. Hyperbolic solved: 19.3522607837916 [20250519_010213.]: Samplename: Sample#5 Root: 19.352 --> Root in between the borders! Added to results. Hyperbolic solved: 38.5571436812761 [20250519_010213.]: Samplename: Sample#6 Root: 38.557 --> Root in between the borders! Added to results. Hyperbolic solved: 44.7759625743335 [20250519_010213.]: Samplename: Sample#7 Root: 44.776 --> Root in between the borders! Added to results. Hyperbolic solved: 84.8599671624974 [20250519_010213.]: Samplename: Sample#8 Root: 84.86 --> Root in between the borders! Added to results. Hyperbolic solved: -2.05044894070214 [20250519_010213.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.05 --> '-10 < root < 0' --> substitute 0 [20250519_010213.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 81.041667802328 [20250519_010213.]: Samplename: Sample#1 Root: 81.042 --> Root in between the borders! Added to results. Hyperbolic solved: 26.6441666289306 [20250519_010213.]: Samplename: Sample#10 Root: 26.644 --> Root in between the borders! Added to results. Hyperbolic solved: 45.7046199658262 [20250519_010213.]: Samplename: Sample#2 Root: 45.705 --> Root in between the borders! Added to results. Hyperbolic solved: 56.9767986854544 [20250519_010213.]: Samplename: Sample#3 Root: 56.977 --> Root in between the borders! Added to results. Hyperbolic solved: 6.81231222322667 [20250519_010213.]: Samplename: Sample#4 Root: 6.812 --> Root in between the borders! Added to results. Hyperbolic solved: 22.9284780361346 [20250519_010213.]: Samplename: Sample#5 Root: 22.928 --> Root in between the borders! Added to results. Hyperbolic solved: 38.8653203361426 [20250519_010213.]: Samplename: Sample#6 Root: 38.865 --> Root in between the borders! Added to results. Hyperbolic solved: 43.6855631975613 [20250519_010213.]: Samplename: Sample#7 Root: 43.686 --> Root in between the borders! Added to results. Hyperbolic solved: 88.0155364495973 [20250519_010213.]: Samplename: Sample#8 Root: 88.016 --> Root in between the borders! Added to results. Hyperbolic solved: -3.35610072407525 [20250519_010213.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -3.356 --> '-10 < root < 0' --> substitute 0 [20250519_010213.]: Solving hyperbolic regression for row_means Hyperbolic solved: 77.0692799954538 [20250519_010213.]: Samplename: Sample#1 Root: 77.069 --> Root in between the borders! Added to results. Hyperbolic solved: 28.3620043244584 [20250519_010213.]: Samplename: Sample#10 Root: 28.362 --> Root in between the borders! Added to results. Hyperbolic solved: 42.5026175468089 [20250519_010213.]: Samplename: Sample#2 Root: 42.503 --> Root in between the borders! Added to results. Hyperbolic solved: 57.2972050444248 [20250519_010213.]: Samplename: Sample#3 Root: 57.297 --> Root in between the borders! Added to results. Hyperbolic solved: 8.82704012618393 [20250519_010213.]: Samplename: Sample#4 Root: 8.827 --> Root in between the borders! Added to results. Hyperbolic solved: 21.8102592524881 [20250519_010213.]: Samplename: Sample#5 Root: 21.81 --> Root in between the borders! Added to results. Hyperbolic solved: 28.7228660045975 [20250519_010213.]: Samplename: Sample#6 Root: 28.723 --> Root in between the borders! Added to results. Hyperbolic solved: 43.410510290696 [20250519_010213.]: Samplename: Sample#7 Root: 43.411 --> Root in between the borders! Added to results. Hyperbolic solved: 86.4143551963301 [20250519_010213.]: Samplename: Sample#8 Root: 86.414 --> Root in between the borders! Added to results. Hyperbolic solved: -0.237020571201611 [20250519_010213.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.237 --> '-10 < root < 0' --> substitute 0 [20250519_010213.]: on_start: using future::plan("sequential") [20250519_010213.]: Entered 'clean_dt'-Function [20250519_010213.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_010213.]: got experimental data [20250519_010213.]: Entered 'clean_dt'-Function [20250519_010213.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_010213.]: got calibration data [20250519_010213.]: ### Starting with regression calculations ### [20250519_010213.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_010213.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_010213.]: Logging df_agg: CpG#1 [20250519_010213.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010213.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_010213.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_010213.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_010213.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_010213.]: Entered 'hyperbolic_regression'-Function [20250519_010213.]: 'hyperbolic_regression': minmax = FALSE [20250519_010213.]: Entered 'cubic_regression'-Function [20250519_010213.]: 'cubic_regression': minmax = FALSE [20250519_010213.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_010213.]: Logging df_agg: CpG#2 [20250519_010213.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010213.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_010213.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_010213.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_010213.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_010213.]: Entered 'hyperbolic_regression'-Function [20250519_010213.]: 'hyperbolic_regression': minmax = FALSE [20250519_010214.]: Entered 'cubic_regression'-Function [20250519_010214.]: 'cubic_regression': minmax = FALSE [20250519_010214.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_010214.]: Logging df_agg: CpG#3 [20250519_010214.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010214.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_010214.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_010214.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_010214.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_010214.]: Entered 'hyperbolic_regression'-Function [20250519_010214.]: 'hyperbolic_regression': minmax = FALSE [20250519_010214.]: Entered 'cubic_regression'-Function [20250519_010214.]: 'cubic_regression': minmax = FALSE [20250519_010214.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_010214.]: Logging df_agg: CpG#4 [20250519_010214.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010214.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_010214.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_010214.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_010214.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_010214.]: Entered 'hyperbolic_regression'-Function [20250519_010214.]: 'hyperbolic_regression': minmax = FALSE [20250519_010214.]: Entered 'cubic_regression'-Function [20250519_010214.]: 'cubic_regression': minmax = FALSE [20250519_010214.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_010214.]: Logging df_agg: CpG#5 [20250519_010214.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010214.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_010214.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_010214.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_010214.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_010214.]: Entered 'hyperbolic_regression'-Function [20250519_010214.]: 'hyperbolic_regression': minmax = FALSE [20250519_010214.]: Entered 'cubic_regression'-Function [20250519_010214.]: 'cubic_regression': minmax = FALSE [20250519_010214.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_010214.]: Logging df_agg: CpG#6 [20250519_010214.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010214.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_010214.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_010214.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_010214.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_010214.]: Entered 'hyperbolic_regression'-Function [20250519_010214.]: 'hyperbolic_regression': minmax = FALSE [20250519_010214.]: Entered 'cubic_regression'-Function [20250519_010214.]: 'cubic_regression': minmax = FALSE [20250519_010214.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_010214.]: Logging df_agg: CpG#7 [20250519_010214.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010214.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_010214.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_010214.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_010214.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_010214.]: Entered 'hyperbolic_regression'-Function [20250519_010214.]: 'hyperbolic_regression': minmax = FALSE [20250519_010215.]: Entered 'cubic_regression'-Function [20250519_010215.]: 'cubic_regression': minmax = FALSE [20250519_010215.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_010215.]: Logging df_agg: CpG#8 [20250519_010215.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010215.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_010215.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_010215.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_010215.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_010215.]: Entered 'hyperbolic_regression'-Function [20250519_010215.]: 'hyperbolic_regression': minmax = FALSE [20250519_010215.]: Entered 'cubic_regression'-Function [20250519_010215.]: 'cubic_regression': minmax = FALSE [20250519_010215.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_010215.]: Logging df_agg: CpG#9 [20250519_010215.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010215.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_010215.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_010215.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_010215.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_010215.]: Entered 'hyperbolic_regression'-Function [20250519_010215.]: 'hyperbolic_regression': minmax = FALSE [20250519_010215.]: Entered 'cubic_regression'-Function [20250519_010215.]: 'cubic_regression': minmax = FALSE [20250519_010215.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723122, 2.47760998435345)c(5.94911111111111, 0.84962962962963, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703704, 19.6995555555555, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_010215.]: Logging df_agg: row_means [20250519_010215.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010215.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_010215.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723122, 2.47760998435345)[20250519_010215.]: c(5.94911111111111, 0.84962962962963, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_010215.]: c(NA, 6.79703703703704, 19.6995555555555, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_010215.]: Entered 'hyperbolic_regression'-Function [20250519_010215.]: 'hyperbolic_regression': minmax = FALSE [20250519_010215.]: Entered 'cubic_regression'-Function [20250519_010215.]: 'cubic_regression': minmax = FALSE [20250519_010216.]: ### Starting with plotting ### [20250519_010216.]: Creating plot No. 1 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG1.png [20250519_010216.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -937.71833725671 , a = -108.568188547925 , b = -937.71833725671 , d = -232.05735859911 [20250519_010216.]: # CpG-site: CpG#1 Cubic: Using a = 6.53413423120091e-05 , b = -0.0055806968734969 , c = 0.784061853455188 , d = 1.93182659932656 [20250519_010218.]: Creating plot No. 2 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG2.png [20250519_010218.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = -2393.14264996499 , a = -141.862267639951 , b = -2393.14264996499 , d = -268.331587255735 [20250519_010218.]: # CpG-site: CpG#2 Cubic: Using a = 8.04237934904601e-06 , b = 0.00293158941798942 , c = 0.514821742825076 , d = 9.27667003367003 [20250519_010220.]: Creating plot No. 3 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG3.png [20250519_010220.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = -1477.46867711742 , a = -170.424279984248 , b = -1477.46867711742 , d = -327.189866698889 [20250519_010220.]: # CpG-site: CpG#3 Cubic: Using a = 2.30555869809204e-05 , b = -0.000797009331409346 , c = 0.62783129228796 , d = 3.88705218855218 [20250519_010223.]: Creating plot No. 4 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG4.png [20250519_010223.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = -2115.87814484424 , a = -162.169929539519 , b = -2115.87814484424 , d = -299.662454768746 [20250519_010223.]: # CpG-site: CpG#4 Cubic: Using a = 3.2166356902357e-05 , b = -0.0015913142857143 , c = 0.697398364598366 , d = 6.17055050505048 [20250519_010225.]: Creating plot No. 5 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG5.png [20250519_010225.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = -1383.32734884945 , a = -151.440088502132 , b = -1383.32734884945 , d = -304.200280997675 [20250519_010225.]: # CpG-site: CpG#5 Cubic: Using a = -4.48459708193036e-06 , b = 0.00375371236171235 , c = 0.422446384479718 , d = 5.17203872053872 [20250519_010227.]: Creating plot No. 6 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG6.png [20250519_010227.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = -1550.59304894212 , a = -125.016150436792 , b = -1550.59304894212 , d = -247.279683783527 [20250519_010227.]: # CpG-site: CpG#6 Cubic: Using a = 2.38852884399552e-05 , b = 0.000918637037037021 , c = 0.561022132435467 , d = 6.1479276094276 [20250519_010229.]: Creating plot No. 7 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG7.png [20250519_010229.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -772.843265948977 , a = -75.2726697123378 , b = -772.843265948977 , d = -226.67343046903 [20250519_010229.]: # CpG-site: CpG#7 Cubic: Using a = 4.98010505050505e-05 , b = -0.00436152150072151 , c = 0.579588917748918 , d = 1.72104545454544 [20250519_010231.]: Creating plot No. 8 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG8.png [20250519_010231.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = -3872.73534838154 , a = -278.017155483789 , b = -3872.73534838154 , d = -438.894126934244 [20250519_010231.]: # CpG-site: CpG#8 Cubic: Using a = -1.50060965207632e-05 , b = 0.00447978143338143 , c = 0.534897737694404 , d = 9.56981481481482 [20250519_010233.]: Creating plot No. 9 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG9.png [20250519_010233.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -843.925271611492 , a = -70.6796326247736 , b = -843.925271611492 , d = -184.482178799669 [20250519_010233.]: # CpG-site: CpG#9 Cubic: Using a = 7.72300426487093e-05 , b = -0.00542281173641174 , c = 0.67899229597563 , d = 2.72413468013467 [20250519_010235.]: Creating plot No. 10 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_rowmeans.png [20250519_010235.]: # CpG-site: row_means Hyperbolic: Using bias_weight = -1525.75751056682 , a = -127.675093193972 , b = -1525.75751056682 , d = -263.003121358972 [20250519_010235.]: # CpG-site: row_means Cubic: Using a = 2.88923726150392e-05 , b = -0.0006299592752926 , c = 0.600117857944524 , d = 5.17789562289563 [20250519_010238.]: Entered 'solving_equations'-Function [20250519_010238.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: -2.23220911684257 [20250519_010238.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.232 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.1698564372971 [20250519_010238.]: Samplename: 12.5 Root: 12.17 --> Root in between the borders! Added to results. Hyperbolic solved: 24.478190918656 [20250519_010238.]: Samplename: 25 Root: 24.478 --> Root in between the borders! Added to results. Hyperbolic solved: 38.1730371634976 [20250519_010238.]: Samplename: 37.5 Root: 38.173 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3349263290239 [20250519_010238.]: Samplename: 50 Root: 52.335 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4582665283612 [20250519_010238.]: Samplename: 62.5 Root: 65.458 --> Root in between the borders! Added to results. Hyperbolic solved: 75.0090705794847 [20250519_010238.]: Samplename: 75 Root: 75.009 --> Root in between the borders! Added to results. Hyperbolic solved: 81.5271853400487 [20250519_010238.]: Samplename: 87.5 Root: 81.527 --> Root in between the borders! Added to results. Hyperbolic solved: 102.400898271831 [20250519_010238.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.401 --> '100 < root < 110' --> substitute 100 [20250519_010238.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 1.13660511681856 [20250519_010238.]: Samplename: 0 Root: 1.137 --> Root in between the borders! Added to results. Hyperbolic solved: 11.4129696918445 [20250519_010238.]: Samplename: 12.5 Root: 11.413 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1740004612661 [20250519_010238.]: Samplename: 25 Root: 26.174 --> Root in between the borders! Added to results. Hyperbolic solved: 35.1050448132614 [20250519_010238.]: Samplename: 37.5 Root: 35.105 --> Root in between the borders! Added to results. Hyperbolic solved: 47.6855002074492 [20250519_010238.]: Samplename: 50 Root: 47.686 --> Root in between the borders! Added to results. Hyperbolic solved: 67.1440493313574 [20250519_010238.]: Samplename: 62.5 Root: 67.144 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7644668045486 [20250519_010238.]: Samplename: 75 Root: 75.764 --> Root in between the borders! Added to results. Hyperbolic solved: 84.4054158145455 [20250519_010238.]: Samplename: 87.5 Root: 84.405 --> Root in between the borders! Added to results. Hyperbolic solved: 100.948272543309 [20250519_010238.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.948 --> '100 < root < 110' --> substitute 100 [20250519_010238.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 0.512348561877098 [20250519_010238.]: Samplename: 0 Root: 0.512 --> Root in between the borders! Added to results. Hyperbolic solved: 10.7523845008661 [20250519_010238.]: Samplename: 12.5 Root: 10.752 --> Root in between the borders! Added to results. Hyperbolic solved: 25.5218912317424 [20250519_010238.]: Samplename: 25 Root: 25.522 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5270488279988 [20250519_010238.]: Samplename: 37.5 Root: 36.527 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7909283805111 [20250519_010238.]: Samplename: 50 Root: 50.791 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8686353441458 [20250519_010238.]: Samplename: 62.5 Root: 64.869 --> Root in between the borders! Added to results. Hyperbolic solved: 77.5524208271734 [20250519_010238.]: Samplename: 75 Root: 77.552 --> Root in between the borders! Added to results. Hyperbolic solved: 80.4374631680835 [20250519_010238.]: Samplename: 87.5 Root: 80.437 --> Root in between the borders! Added to results. Hyperbolic solved: 102.704019894026 [20250519_010238.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.704 --> '100 < root < 110' --> substitute 100 [20250519_010238.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: -0.519496428117613 [20250519_010238.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.519 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.4934173132007 [20250519_010238.]: Samplename: 12.5 Root: 12.493 --> Root in between the borders! Added to results. Hyperbolic solved: 24.2685417185317 [20250519_010238.]: Samplename: 25 Root: 24.269 --> Root in between the borders! Added to results. Hyperbolic solved: 38.0817103888785 [20250519_010238.]: Samplename: 37.5 Root: 38.082 --> Root in between the borders! Added to results. Hyperbolic solved: 48.5843148203973 [20250519_010238.]: Samplename: 50 Root: 48.584 --> Root in between the borders! Added to results. Hyperbolic solved: 67.672236894341 [20250519_010238.]: Samplename: 62.5 Root: 67.672 --> Root in between the borders! Added to results. Hyperbolic solved: 74.1549253766253 [20250519_010238.]: Samplename: 75 Root: 74.155 --> Root in between the borders! Added to results. Hyperbolic solved: 82.8821786440368 [20250519_010238.]: Samplename: 87.5 Root: 82.882 --> Root in between the borders! Added to results. Hyperbolic solved: 102.079130232402 [20250519_010238.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.079 --> '100 < root < 110' --> substitute 100 [20250519_010238.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 2.41558718123367 [20250519_010238.]: Samplename: 0 Root: 2.416 --> Root in between the borders! Added to results. Hyperbolic solved: 10.1649680545674 [20250519_010238.]: Samplename: 12.5 Root: 10.165 --> Root in between the borders! Added to results. Hyperbolic solved: 23.9830821073418 [20250519_010238.]: Samplename: 25 Root: 23.983 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2773617400679 [20250519_010238.]: Samplename: 37.5 Root: 37.277 --> Root in between the borders! Added to results. Hyperbolic solved: 50.8659382464167 [20250519_010238.]: Samplename: 50 Root: 50.866 --> Root in between the borders! Added to results. Hyperbolic solved: 62.4342269461158 [20250519_010238.]: Samplename: 62.5 Root: 62.434 --> Root in between the borders! Added to results. Hyperbolic solved: 76.3915257997575 [20250519_010238.]: Samplename: 75 Root: 76.392 --> Root in between the borders! Added to results. Hyperbolic solved: 86.1597887396996 [20250519_010238.]: Samplename: 87.5 Root: 86.16 --> Root in between the borders! Added to results. Hyperbolic solved: 100.267760316884 [20250519_010238.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.268 --> '100 < root < 110' --> substitute 100 [20250519_010238.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 0.138164161050506 [20250519_010238.]: Samplename: 0 Root: 0.138 --> Root in between the borders! Added to results. Hyperbolic solved: 11.8635561171135 [20250519_010238.]: Samplename: 12.5 Root: 11.864 --> Root in between the borders! Added to results. Hyperbolic solved: 26.510745006666 [20250519_010238.]: Samplename: 25 Root: 26.511 --> Root in between the borders! Added to results. Hyperbolic solved: 35.3205072777727 [20250519_010238.]: Samplename: 37.5 Root: 35.321 --> Root in between the borders! Added to results. Hyperbolic solved: 50.0570766444694 [20250519_010238.]: Samplename: 50 Root: 50.057 --> Root in between the borders! Added to results. Hyperbolic solved: 64.9602942986125 [20250519_010238.]: Samplename: 62.5 Root: 64.96 --> Root in between the borders! Added to results. Hyperbolic solved: 73.6689055886064 [20250519_010238.]: Samplename: 75 Root: 73.669 --> Root in between the borders! Added to results. Hyperbolic solved: 87.1266085894673 [20250519_010238.]: Samplename: 87.5 Root: 87.127 --> Root in between the borders! Added to results. Hyperbolic solved: 100.261637049252 [20250519_010238.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.262 --> '100 < root < 110' --> substitute 100 [20250519_010238.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: -1.37235519395329 [20250519_010238.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -1.372 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.1993294013604 [20250519_010238.]: Samplename: 12.5 Root: 10.199 --> Root in between the borders! Added to results. Hyperbolic solved: 24.5951803850766 [20250519_010238.]: Samplename: 25 Root: 24.595 --> Root in between the borders! Added to results. Hyperbolic solved: 37.8310364564335 [20250519_010238.]: Samplename: 37.5 Root: 37.831 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5588645818264 [20250519_010238.]: Samplename: 50 Root: 53.559 --> Root in between the borders! Added to results. Hyperbolic solved: 65.9364860153449 [20250519_010238.]: Samplename: 62.5 Root: 65.936 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7361033620359 [20250519_010238.]: Samplename: 75 Root: 75.736 --> Root in between the borders! Added to results. Hyperbolic solved: 79.4328192070151 [20250519_010238.]: Samplename: 87.5 Root: 79.433 --> Root in between the borders! Added to results. Hyperbolic solved: 103.004248773309 [20250519_010238.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 103.004 --> '100 < root < 110' --> substitute 100 [20250519_010238.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 2.80068093389159 [20250519_010238.]: Samplename: 0 Root: 2.801 --> Root in between the borders! Added to results. Hyperbolic solved: 9.27535066703723 [20250519_010238.]: Samplename: 12.5 Root: 9.275 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4762625870809 [20250519_010238.]: Samplename: 25 Root: 25.476 --> Root in between the borders! Added to results. Hyperbolic solved: 34.0122083314201 [20250519_010238.]: Samplename: 37.5 Root: 34.012 --> Root in between the borders! Added to results. Hyperbolic solved: 51.7842666342759 [20250519_010238.]: Samplename: 50 Root: 51.784 --> Root in between the borders! Added to results. Hyperbolic solved: 64.6732321058682 [20250519_010238.]: Samplename: 62.5 Root: 64.673 --> Root in between the borders! Added to results. Hyperbolic solved: 78.4326982875914 [20250519_010238.]: Samplename: 75 Root: 78.433 --> Root in between the borders! Added to results. Hyperbolic solved: 81.3427235319597 [20250519_010238.]: Samplename: 87.5 Root: 81.343 --> Root in between the borders! Added to results. Hyperbolic solved: 101.964405325155 [20250519_010238.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.964 --> '100 < root < 110' --> substitute 100 [20250519_010238.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: -2.1340328997008 [20250519_010238.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.134 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.5082212970134 [20250519_010238.]: Samplename: 12.5 Root: 10.508 --> Root in between the borders! Added to results. Hyperbolic solved: 26.9164566258777 [20250519_010238.]: Samplename: 25 Root: 26.916 --> Root in between the borders! Added to results. Hyperbolic solved: 36.8334769505904 [20250519_010238.]: Samplename: 37.5 Root: 36.833 --> Root in between the borders! Added to results. Hyperbolic solved: 52.0097879402751 [20250519_010238.]: Samplename: 50 Root: 52.01 --> Root in between the borders! Added to results. Hyperbolic solved: 64.893051148082 [20250519_010238.]: Samplename: 62.5 Root: 64.893 --> Root in between the borders! Added to results. Hyperbolic solved: 74.5671042786367 [20250519_010238.]: Samplename: 75 Root: 74.567 --> Root in between the borders! Added to results. Hyperbolic solved: 84.5294949210319 [20250519_010238.]: Samplename: 87.5 Root: 84.529 --> Root in between the borders! Added to results. Hyperbolic solved: 101.047147807246 [20250519_010238.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.047 --> '100 < root < 110' --> substitute 100 [20250519_010238.]: Solving hyperbolic regression for row_means Hyperbolic solved: 0.290944901553815 [20250519_010238.]: Samplename: 0 Root: 0.291 --> Root in between the borders! Added to results. Hyperbolic solved: 11.0412427310408 [20250519_010238.]: Samplename: 12.5 Root: 11.041 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4081501338029 [20250519_010238.]: Samplename: 25 Root: 25.408 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5243709582085 [20250519_010238.]: Samplename: 37.5 Root: 36.524 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7348808712519 [20250519_010238.]: Samplename: 50 Root: 50.735 --> Root in between the borders! Added to results. Hyperbolic solved: 65.3135195123017 [20250519_010238.]: Samplename: 62.5 Root: 65.314 --> Root in between the borders! Added to results. Hyperbolic solved: 75.5342699494589 [20250519_010238.]: Samplename: 75 Root: 75.534 --> Root in between the borders! Added to results. Hyperbolic solved: 83.2411225127701 [20250519_010238.]: Samplename: 87.5 Root: 83.241 --> Root in between the borders! Added to results. Hyperbolic solved: 101.666944784378 [20250519_010238.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.667 --> '100 < root < 110' --> substitute 100 [20250519_010238.]: ### Starting with regression calculations ### [20250519_010238.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_010238.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.1698564372971, 24.478190918656, 38.1730371634976, 52.3349263290239, 65.4582665283612, 75.0090705794847, 81.5271853400487, 100)c(0, 0.330143562702901, 0.521809081343999, 0.673037163497597, 2.3349263290239, 2.95826652836119, 0.00907057948469969, 5.9728146599513, 0)c(NA, 2.64114850162321, 2.087236325376, 1.79476576932692, 4.6698526580478, 4.73322644537791, 0.0120941059795996, 6.8260738970872, 0) [20250519_010238.]: Logging df_agg: CpG#1 [20250519_010238.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010238.]: c(0, 12.1698564372971, 24.478190918656, 38.1730371634976, 52.3349263290239, 65.4582665283612, 75.0090705794847, 81.5271853400487, 100)[20250519_010238.]: c(0, 0.330143562702901, 0.521809081343999, 0.673037163497597, 2.3349263290239, 2.95826652836119, 0.00907057948469969, 5.9728146599513, 0)[20250519_010238.]: c(NA, 2.64114850162321, 2.087236325376, 1.79476576932692, 4.6698526580478, 4.73322644537791, 0.0120941059795996, 6.8260738970872, 0) [20250519_010238.]: Entered 'hyperbolic_regression'-Function [20250519_010238.]: 'hyperbolic_regression': minmax = FALSE [20250519_010238.]: Entered 'cubic_regression'-Function [20250519_010238.]: 'cubic_regression': minmax = FALSE [20250519_010238.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.13660511681856, 11.4129696918445, 26.1740004612661, 35.1050448132614, 47.6855002074492, 67.1440493313574, 75.7644668045486, 84.4054158145455, 100)c(1.13660511681856, 1.0870303081555, 1.1740004612661, 2.3949551867386, 2.3144997925508, 4.64404933135739, 0.764466804548604, 3.09458418545449, 0)c(NA, 8.696242465244, 4.6960018450644, 6.38654716463626, 4.6289995851016, 7.43047893017183, 1.01928907273147, 3.53666764051942, 0) [20250519_010238.]: Logging df_agg: CpG#2 [20250519_010238.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010238.]: c(1.13660511681856, 11.4129696918445, 26.1740004612661, 35.1050448132614, 47.6855002074492, 67.1440493313574, 75.7644668045486, 84.4054158145455, 100)[20250519_010238.]: c(1.13660511681856, 1.0870303081555, 1.1740004612661, 2.3949551867386, 2.3144997925508, 4.64404933135739, 0.764466804548604, 3.09458418545449, 0)[20250519_010238.]: c(NA, 8.696242465244, 4.6960018450644, 6.38654716463626, 4.6289995851016, 7.43047893017183, 1.01928907273147, 3.53666764051942, 0) [20250519_010238.]: Entered 'hyperbolic_regression'-Function [20250519_010238.]: 'hyperbolic_regression': minmax = FALSE [20250519_010238.]: Entered 'cubic_regression'-Function [20250519_010238.]: 'cubic_regression': minmax = FALSE [20250519_010238.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.512348561877098, 10.7523845008661, 25.5218912317424, 36.5270488279988, 50.7909283805111, 64.8686353441458, 77.5524208271734, 80.4374631680835, 100)c(0.512348561877098, 1.7476154991339, 0.521891231742401, 0.972951172001203, 0.790928380511097, 2.3686353441458, 2.55242082717341, 7.0625368319165, 0)c(NA, 13.9809239930712, 2.08756492696961, 2.59453645866987, 1.58185676102219, 3.78981655063328, 3.40322776956454, 8.07147066504743, 0) [20250519_010239.]: Logging df_agg: CpG#3 [20250519_010239.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010239.]: c(0.512348561877098, 10.7523845008661, 25.5218912317424, 36.5270488279988, 50.7909283805111, 64.8686353441458, 77.5524208271734, 80.4374631680835, 100)[20250519_010239.]: c(0.512348561877098, 1.7476154991339, 0.521891231742401, 0.972951172001203, 0.790928380511097, 2.3686353441458, 2.55242082717341, 7.0625368319165, 0)[20250519_010239.]: c(NA, 13.9809239930712, 2.08756492696961, 2.59453645866987, 1.58185676102219, 3.78981655063328, 3.40322776956454, 8.07147066504743, 0) [20250519_010239.]: Entered 'hyperbolic_regression'-Function [20250519_010239.]: 'hyperbolic_regression': minmax = FALSE [20250519_010239.]: Entered 'cubic_regression'-Function [20250519_010239.]: 'cubic_regression': minmax = FALSE [20250519_010239.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.4934173132007, 24.2685417185317, 38.0817103888785, 48.5843148203973, 67.672236894341, 74.1549253766253, 82.8821786440368, 100)c(0, 0.00658268679929996, 0.731458281468299, 0.581710388878498, 1.4156851796027, 5.172236894341, 0.8450746233747, 4.6178213559632, 0)c(NA, 0.0526614943943997, 2.9258331258732, 1.551227703676, 2.83137035920539, 8.27557903094559, 1.1267661644996, 5.2775101211008, 0) [20250519_010239.]: Logging df_agg: CpG#4 [20250519_010239.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010239.]: c(0, 12.4934173132007, 24.2685417185317, 38.0817103888785, 48.5843148203973, 67.672236894341, 74.1549253766253, 82.8821786440368, 100)[20250519_010239.]: c(0, 0.00658268679929996, 0.731458281468299, 0.581710388878498, 1.4156851796027, 5.172236894341, 0.8450746233747, 4.6178213559632, 0)[20250519_010239.]: c(NA, 0.0526614943943997, 2.9258331258732, 1.551227703676, 2.83137035920539, 8.27557903094559, 1.1267661644996, 5.2775101211008, 0) [20250519_010239.]: Entered 'hyperbolic_regression'-Function [20250519_010239.]: 'hyperbolic_regression': minmax = FALSE [20250519_010239.]: Entered 'cubic_regression'-Function [20250519_010239.]: 'cubic_regression': minmax = FALSE [20250519_010239.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.41558718123367, 10.1649680545674, 23.9830821073418, 37.2773617400679, 50.8659382464167, 62.4342269461158, 76.3915257997575, 86.1597887396996, 100)c(2.41558718123367, 2.3350319454326, 1.0169178926582, 0.222638259932097, 0.865938246416697, 0.0657730538842003, 1.3915257997575, 1.34021126030041, 0)c(NA, 18.6802555634608, 4.0676715706328, 0.593702026485592, 1.73187649283339, 0.10523688621472, 1.85536773301, 1.53167001177189, 0) [20250519_010239.]: Logging df_agg: CpG#5 [20250519_010239.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010239.]: c(2.41558718123367, 10.1649680545674, 23.9830821073418, 37.2773617400679, 50.8659382464167, 62.4342269461158, 76.3915257997575, 86.1597887396996, 100)[20250519_010239.]: c(2.41558718123367, 2.3350319454326, 1.0169178926582, 0.222638259932097, 0.865938246416697, 0.0657730538842003, 1.3915257997575, 1.34021126030041, 0)[20250519_010239.]: c(NA, 18.6802555634608, 4.0676715706328, 0.593702026485592, 1.73187649283339, 0.10523688621472, 1.85536773301, 1.53167001177189, 0) [20250519_010239.]: Entered 'hyperbolic_regression'-Function [20250519_010239.]: 'hyperbolic_regression': minmax = FALSE [20250519_010239.]: Entered 'cubic_regression'-Function [20250519_010239.]: 'cubic_regression': minmax = FALSE [20250519_010239.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.138164161050506, 11.8635561171135, 26.510745006666, 35.3205072777727, 50.0570766444694, 64.9602942986125, 73.6689055886064, 87.1266085894673, 100)c(0.138164161050506, 0.6364438828865, 1.510745006666, 2.1794927222273, 0.0570766444693973, 2.4602942986125, 1.33109441139359, 0.373391410532705, 0)c(NA, 5.091551063092, 6.042980026664, 5.81198059260614, 0.114153288938795, 3.93647087777999, 1.77479254852479, 0.426733040608805, 0) [20250519_010239.]: Logging df_agg: CpG#6 [20250519_010239.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010239.]: c(0.138164161050506, 11.8635561171135, 26.510745006666, 35.3205072777727, 50.0570766444694, 64.9602942986125, 73.6689055886064, 87.1266085894673, 100)[20250519_010239.]: c(0.138164161050506, 0.6364438828865, 1.510745006666, 2.1794927222273, 0.0570766444693973, 2.4602942986125, 1.33109441139359, 0.373391410532705, 0)[20250519_010239.]: c(NA, 5.091551063092, 6.042980026664, 5.81198059260614, 0.114153288938795, 3.93647087777999, 1.77479254852479, 0.426733040608805, 0) [20250519_010239.]: Entered 'hyperbolic_regression'-Function [20250519_010239.]: 'hyperbolic_regression': minmax = FALSE [20250519_010239.]: Entered 'cubic_regression'-Function [20250519_010239.]: 'cubic_regression': minmax = FALSE [20250519_010239.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.1993294013604, 24.5951803850766, 37.8310364564335, 53.5588645818264, 65.9364860153449, 75.7361033620359, 79.4328192070151, 100)c(0, 2.3006705986396, 0.404819614923401, 0.331036456433502, 3.5588645818264, 3.4364860153449, 0.736103362035905, 8.0671807929849, 0)c(NA, 18.4053647891168, 1.6192784596936, 0.882763883822671, 7.1177291636528, 5.49837762455184, 0.981471149381207, 9.21963519198274, 0) [20250519_010239.]: Logging df_agg: CpG#7 [20250519_010239.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010239.]: c(0, 10.1993294013604, 24.5951803850766, 37.8310364564335, 53.5588645818264, 65.9364860153449, 75.7361033620359, 79.4328192070151, 100)[20250519_010239.]: c(0, 2.3006705986396, 0.404819614923401, 0.331036456433502, 3.5588645818264, 3.4364860153449, 0.736103362035905, 8.0671807929849, 0)[20250519_010239.]: c(NA, 18.4053647891168, 1.6192784596936, 0.882763883822671, 7.1177291636528, 5.49837762455184, 0.981471149381207, 9.21963519198274, 0) [20250519_010239.]: Entered 'hyperbolic_regression'-Function [20250519_010239.]: 'hyperbolic_regression': minmax = FALSE [20250519_010240.]: Entered 'cubic_regression'-Function [20250519_010240.]: 'cubic_regression': minmax = FALSE [20250519_010240.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.80068093389159, 9.27535066703723, 25.4762625870809, 34.0122083314201, 51.7842666342759, 64.6732321058682, 78.4326982875914, 81.3427235319597, 100)c(2.80068093389159, 3.22464933296277, 0.4762625870809, 3.4877916685799, 1.7842666342759, 2.17323210586819, 3.43269828759141, 6.15727646804029, 0)c(NA, 25.7971946637022, 1.9050503483236, 9.30077778287974, 3.5685332685518, 3.47717136938911, 4.57693105012188, 7.03688739204605, 0) [20250519_010240.]: Logging df_agg: CpG#8 [20250519_010240.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010240.]: c(2.80068093389159, 9.27535066703723, 25.4762625870809, 34.0122083314201, 51.7842666342759, 64.6732321058682, 78.4326982875914, 81.3427235319597, 100)[20250519_010240.]: c(2.80068093389159, 3.22464933296277, 0.4762625870809, 3.4877916685799, 1.7842666342759, 2.17323210586819, 3.43269828759141, 6.15727646804029, 0)[20250519_010240.]: c(NA, 25.7971946637022, 1.9050503483236, 9.30077778287974, 3.5685332685518, 3.47717136938911, 4.57693105012188, 7.03688739204605, 0) [20250519_010240.]: Entered 'hyperbolic_regression'-Function [20250519_010240.]: 'hyperbolic_regression': minmax = FALSE [20250519_010240.]: Entered 'cubic_regression'-Function [20250519_010240.]: 'cubic_regression': minmax = FALSE [20250519_010240.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.5082212970134, 26.9164566258777, 36.8334769505904, 52.0097879402751, 64.893051148082, 74.5671042786367, 84.5294949210319, 100)c(0, 1.9917787029866, 1.9164566258777, 0.666523049409598, 2.0097879402751, 2.39305114808199, 0.432895721363295, 2.9705050789681, 0)c(NA, 15.9342296238928, 7.6658265035108, 1.77739479842559, 4.01957588055021, 3.82888183693119, 0.57719429515106, 3.39486294739212, 0) [20250519_010240.]: Logging df_agg: CpG#9 [20250519_010240.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010240.]: c(0, 10.5082212970134, 26.9164566258777, 36.8334769505904, 52.0097879402751, 64.893051148082, 74.5671042786367, 84.5294949210319, 100)[20250519_010240.]: c(0, 1.9917787029866, 1.9164566258777, 0.666523049409598, 2.0097879402751, 2.39305114808199, 0.432895721363295, 2.9705050789681, 0)[20250519_010240.]: c(NA, 15.9342296238928, 7.6658265035108, 1.77739479842559, 4.01957588055021, 3.82888183693119, 0.57719429515106, 3.39486294739212, 0) [20250519_010240.]: Entered 'hyperbolic_regression'-Function [20250519_010240.]: 'hyperbolic_regression': minmax = FALSE [20250519_010240.]: Entered 'cubic_regression'-Function [20250519_010240.]: 'cubic_regression': minmax = FALSE [20250519_010240.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.290944901553815, 11.0412427310408, 25.4081501338029, 36.5243709582085, 50.7348808712519, 65.3135195123017, 75.5342699494589, 83.2411225127701, 100)c(0.290944901553815, 1.4587572689592, 0.408150133802899, 0.9756290417915, 0.734880871251903, 2.8135195123017, 0.534269949458903, 4.2588774872299, 0)c(NA, 11.6700581516736, 1.63260053521159, 2.60167744477733, 1.46976174250381, 4.50163121968271, 0.71235993261187, 4.86728855683417, 0) [20250519_010240.]: Logging df_agg: row_means [20250519_010240.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010240.]: c(0.290944901553815, 11.0412427310408, 25.4081501338029, 36.5243709582085, 50.7348808712519, 65.3135195123017, 75.5342699494589, 83.2411225127701, 100)[20250519_010240.]: c(0.290944901553815, 1.4587572689592, 0.408150133802899, 0.9756290417915, 0.734880871251903, 2.8135195123017, 0.534269949458903, 4.2588774872299, 0)[20250519_010240.]: c(NA, 11.6700581516736, 1.63260053521159, 2.60167744477733, 1.46976174250381, 4.50163121968271, 0.71235993261187, 4.86728855683417, 0) [20250519_010240.]: Entered 'hyperbolic_regression'-Function [20250519_010240.]: 'hyperbolic_regression': minmax = FALSE [20250519_010240.]: Entered 'cubic_regression'-Function [20250519_010240.]: 'cubic_regression': minmax = FALSE [20250519_010241.]: ### Starting with plotting ### [20250519_010241.]: Creating BiasCorrected (hyperbolic) plot No. 1 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG1_corrected_h.png [20250519_010241.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -657.85070009309 , a = 985.271206705952 , b = -657.85070009309 , d = 903.457967400565 [20250519_010241.]: # CpG-site: CpG#1 Cubic: Using a = 5.1847317144537e-06 , b = -0.00180593132520811 , c = 1.11404026275692 , d = -0.858597345049636 [20250519_010243.]: Creating BiasCorrected (hyperbolic) plot No. 2 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG2_corrected_h.png [20250519_010243.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = 3399.63077807263 , a = 30276.8974003894 , b = 3399.63077807263 , d = 30353.3579426379 [20250519_010243.]: # CpG-site: CpG#2 Cubic: Using a = -2.58097552031906e-05 , b = 0.00383753288450718 , c = 0.851607751869747 , d = 0.95712734045721 [20250519_010244.]: Creating BiasCorrected (hyperbolic) plot No. 3 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG3_corrected_h.png [20250519_010244.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = -945.763900328712 , a = 1333.61328542459 , b = -945.763900328712 , d = 1252.95190710086 [20250519_010244.]: # CpG-site: CpG#3 Cubic: Using a = -1.13948418252831e-05 , b = 0.000937174314708123 , c = 0.998631105115713 , d = -0.383346041194064 [20250519_010246.]: Creating BiasCorrected (hyperbolic) plot No. 4 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG4_corrected_h.png [20250519_010246.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = -754.050808709341 , a = 1637.26986382636 , b = -754.050808709341 , d = 1558.27566152171 [20250519_010246.]: # CpG-site: CpG#4 Cubic: Using a = -3.25777472656452e-06 , b = -0.000129598485618069 , c = 1.03077245158202 , d = -0.370342672245111 [20250519_010248.]: Creating BiasCorrected (hyperbolic) plot No. 5 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG5_corrected_h.png [20250519_010248.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = -1917.44470782279 , a = -5621.43051220384 , b = -1917.44470782279 , d = -5734.45004174505 [20250519_010248.]: # CpG-site: CpG#5 Cubic: Using a = -3.16782847952728e-05 , b = 0.00493527243554485 , c = 0.800194551229801 , d = 1.38653114075915 [20250519_010250.]: Creating BiasCorrected (hyperbolic) plot No. 6 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG6_corrected_h.png [20250519_010250.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = -1255.66539296107 , a = 13775.5132875201 , b = -1255.66539296107 , d = 13690.4025642689 [20250519_010250.]: # CpG-site: CpG#6 Cubic: Using a = -3.70201699726522e-06 , b = 0.000482210843999232 , c = 0.98529311552838 , d = 0.0293572853154747 [20250519_010252.]: Creating BiasCorrected (hyperbolic) plot No. 7 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG7_corrected_h.png [20250519_010252.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -1065.05219601462 , a = 722.085443879134 , b = -1065.05219601462 , d = 634.403611801521 [20250519_010252.]: # CpG-site: CpG#7 Cubic: Using a = -1.04101209223167e-06 , b = -0.00130151331776721 , c = 1.1239953084199 , d = -1.59858561306358 [20250519_010254.]: Creating BiasCorrected (hyperbolic) plot No. 8 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG8_corrected_h.png [20250519_010254.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = 68.5259802694693 , a = 4784.81913306904 , b = 68.5259802694693 , d = 4739.03101269703 [20250519_010254.]: # CpG-site: CpG#8 Cubic: Using a = -4.65730461761547e-05 , b = 0.00676525443932464 , c = 0.747405842988874 , d = 1.5233701793389 [20250519_010255.]: Creating BiasCorrected (hyperbolic) plot No. 9 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG9_corrected_h.png [20250519_010255.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -1022.70071430348 , a = 1273.73160369723 , b = -1022.70071430348 , d = 1181.86620562781 [20250519_010255.]: # CpG-site: CpG#9 Cubic: Using a = 4.24791805783875e-06 , b = -0.00144169825546529 , c = 1.09837792836035 , d = -0.978987254622136 [20250519_010257.]: Creating BiasCorrected (hyperbolic) plot No. 10 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_rowmeans_corrected_h.png [20250519_010257.]: # CpG-site: row_means Hyperbolic: Using bias_weight = -1166.73385465972 , a = 1740.64188131784 , b = -1166.73385465972 , d = 1653.86486365297 [20250519_010257.]: # CpG-site: row_means Cubic: Using a = -8.71187897501959e-06 , b = 0.000715556896368103 , c = 1.00248226010187 , d = -0.420549765740683 [20250519_010259.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG1_corrected_h.png [20250519_010301.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG2_corrected_h.png [20250519_010303.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG3_corrected_h.png [20250519_010304.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG4_corrected_h.png [20250519_010306.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG5_corrected_h.png [20250519_010308.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG6_corrected_h.png [20250519_010310.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG7_corrected_h.png [20250519_010311.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG8_corrected_h.png [20250519_010313.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG9_corrected_h.png [20250519_010315.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_rowmeans_corrected_h.png [20250519_010317.]: Entered 'solving_equations'-Function [20250519_010317.]: Solving cubic regression for CpG#1 Coefficients: -1.03617340067344Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_010317.]: Samplename: 0 Root: 1.334 --> Root in between the borders! Added to results. Coefficients: -8.34150673400678Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_010317.]: Samplename: 12.5 Root: 11.446 --> Root in between the borders! Added to results. Coefficients: -15.3881734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_010317.]: Samplename: 25 Root: 22.228 --> Root in between the borders! Added to results. Coefficients: -24.2801734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_010317.]: Samplename: 37.5 Root: 36.374 --> Root in between the borders! Added to results. Coefficients: -34.9006734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_010317.]: Samplename: 50 Root: 52.044 --> Root in between the borders! Added to results. Coefficients: -46.3541734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_010317.]: Samplename: 62.5 Root: 66.144 --> Root in between the borders! Added to results. Coefficients: -55.8931734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_010317.]: Samplename: 75 Root: 75.864 --> Root in between the borders! Added to results. Coefficients: -63.0981734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_010317.]: Samplename: 87.5 Root: 82.254 --> Root in between the borders! Added to results. Coefficients: -91.0461734006735Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_010317.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.877 --> '100 < root < 110' --> substitute 100 [20250519_010317.]: Solving cubic regression for CpG#2 Coefficients: -0.283329966329966Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_010317.]: Samplename: 0 Root: 0.549 --> Root in between the borders! Added to results. Coefficients: -6.33999663299663Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_010317.]: Samplename: 12.5 Root: 11.533 --> Root in between the borders! Added to results. Coefficients: -15.93932996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_010317.]: Samplename: 25 Root: 26.628 --> Root in between the borders! Added to results. Coefficients: -22.33732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_010317.]: Samplename: 37.5 Root: 35.509 --> Root in between the borders! Added to results. Coefficients: -32.22832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_010317.]: Samplename: 50 Root: 47.851 --> Root in between the borders! Added to results. Coefficients: -49.96332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_010317.]: Samplename: 62.5 Root: 66.893 --> Root in between the borders! Added to results. Coefficients: -58.96582996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_010317.]: Samplename: 75 Root: 75.431 --> Root in between the borders! Added to results. Coefficients: -68.8366632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_010317.]: Samplename: 87.5 Root: 84.118 --> Root in between the borders! Added to results. Coefficients: -90.57732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_010317.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.287 --> '100 < root < 110' --> substitute 100 [20250519_010317.]: Solving cubic regression for CpG#3 Coefficients: -0.90294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_010317.]: Samplename: 0 Root: 1.441 --> Root in between the borders! Added to results. Coefficients: -6.57294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_010317.]: Samplename: 12.5 Root: 10.568 --> Root in between the borders! Added to results. Coefficients: -15.4289478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_010317.]: Samplename: 25 Root: 24.796 --> Root in between the borders! Added to results. Coefficients: -22.6129478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_010317.]: Samplename: 37.5 Root: 35.952 --> Root in between the borders! Added to results. Coefficients: -32.7754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_010317.]: Samplename: 50 Root: 50.684 --> Root in between the borders! Added to results. Coefficients: -43.8889478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_010317.]: Samplename: 62.5 Root: 65.142 --> Root in between the borders! Added to results. Coefficients: -54.9754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_010317.]: Samplename: 75 Root: 77.905 --> Root in between the borders! Added to results. Coefficients: -57.6562811447812Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_010317.]: Samplename: 87.5 Root: 80.767 --> Root in between the borders! Added to results. Coefficients: -80.6649478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_010317.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.38 --> '100 < root < 110' --> substitute 100 [20250519_010317.]: Solving cubic regression for CpG#4 Coefficients: -0.597449494949524Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_010317.]: Samplename: 0 Root: 0.858 --> Root in between the borders! Added to results. Coefficients: -8.25278282828286Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_010317.]: Samplename: 12.5 Root: 12.086 --> Root in between the borders! Added to results. Coefficients: -15.8034494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_010317.]: Samplename: 25 Root: 23.316 --> Root in between the borders! Added to results. Coefficients: -25.5274494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_010317.]: Samplename: 37.5 Root: 37.383 --> Root in between the borders! Added to results. Coefficients: -33.6369494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_010317.]: Samplename: 50 Root: 48.353 --> Root in between the borders! Added to results. Coefficients: -50.2554494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_010317.]: Samplename: 62.5 Root: 68.082 --> Root in between the borders! Added to results. Coefficients: -56.5394494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_010317.]: Samplename: 75 Root: 74.615 --> Root in between the borders! Added to results. Coefficients: -65.5927828282829Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_010317.]: Samplename: 87.5 Root: 83.254 --> Root in between the borders! Added to results. Coefficients: -88.3214494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_010317.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.715 --> '100 < root < 110' --> substitute 100 [20250519_010317.]: Solving cubic regression for CpG#5 Coefficients: -0.623961279461278Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_010317.]: Samplename: 0 Root: 1.458 --> Root in between the borders! Added to results. Coefficients: -4.76796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_010317.]: Samplename: 12.5 Root: 10.347 --> Root in between the borders! Added to results. Coefficients: -12.7259612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_010317.]: Samplename: 25 Root: 24.815 --> Root in between the borders! Added to results. Coefficients: -21.1599612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_010317.]: Samplename: 37.5 Root: 37.902 --> Root in between the borders! Added to results. Coefficients: -30.6954612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_010317.]: Samplename: 50 Root: 50.977 --> Root in between the borders! Added to results. Coefficients: -39.6579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_010317.]: Samplename: 62.5 Root: 62.126 --> Root in between the borders! Added to results. Coefficients: -51.6829612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_010317.]: Samplename: 75 Root: 75.852 --> Root in between the borders! Added to results. Coefficients: -61.0146279461279Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_010317.]: Samplename: 87.5 Root: 85.767 --> Root in between the borders! Added to results. Coefficients: -76.0699612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_010317.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.743 --> '100 < root < 110' --> substitute 100 [20250519_010317.]: Solving cubic regression for CpG#6 Coefficients: -0.196072390572403Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_010317.]: Samplename: 0 Root: 0.349 --> Root in between the borders! Added to results. Coefficients: -6.73873905723907Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_010317.]: Samplename: 12.5 Root: 11.718 --> Root in between the borders! Added to results. Coefficients: -15.8880723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_010317.]: Samplename: 25 Root: 26.396 --> Root in between the borders! Added to results. Coefficients: -22.0000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_010317.]: Samplename: 37.5 Root: 35.301 --> Root in between the borders! Added to results. Coefficients: -33.4445723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_010317.]: Samplename: 50 Root: 50.134 --> Root in between the borders! Added to results. Coefficients: -46.9000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_010317.]: Samplename: 62.5 Root: 64.993 --> Root in between the borders! Added to results. Coefficients: -55.8320723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_010317.]: Samplename: 75 Root: 73.639 --> Root in between the borders! Added to results. Coefficients: -71.5454057239057Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_010317.]: Samplename: 87.5 Root: 87.043 --> Root in between the borders! Added to results. Coefficients: -89.6560723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_010317.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.329 --> '100 < root < 110' --> substitute 100 [20250519_010317.]: Solving cubic regression for CpG#7 Coefficients: -1.21495454545456Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_010317.]: Samplename: 0 Root: 2.13 --> Root in between the borders! Added to results. Coefficients: -5.39562121212123Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_010317.]: Samplename: 12.5 Root: 9.973 --> Root in between the borders! Added to results. Coefficients: -11.2649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_010317.]: Samplename: 25 Root: 22.206 --> Root in between the borders! Added to results. Coefficients: -17.4509545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_010317.]: Samplename: 37.5 Root: 35.814 --> Root in between the borders! Added to results. Coefficients: -26.0314545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_010317.]: Samplename: 50 Root: 53.28 --> Root in between the borders! Added to results. Coefficients: -33.9649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_010317.]: Samplename: 62.5 Root: 66.598 --> Root in between the borders! Added to results. Coefficients: -41.1689545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_010317.]: Samplename: 75 Root: 76.575 --> Root in between the borders! Added to results. Coefficients: -44.1356212121212Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_010317.]: Samplename: 87.5 Root: 80.219 --> Root in between the borders! Added to results. Coefficients: -67.2229545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_010317.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.506 --> '100 < root < 110' --> substitute 100 [20250519_010317.]: Solving cubic regression for CpG#8 Coefficients: -1.09618518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_010317.]: Samplename: 0 Root: 2.016 --> Root in between the borders! Added to results. Coefficients: -5.44685185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_010317.]: Samplename: 12.5 Root: 9.458 --> Root in between the borders! Added to results. Coefficients: -16.9301851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_010317.]: Samplename: 25 Root: 26.35 --> Root in between the borders! Added to results. Coefficients: -23.3501851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_010317.]: Samplename: 37.5 Root: 34.728 --> Root in between the borders! Added to results. Coefficients: -37.6251851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_010317.]: Samplename: 50 Root: 51.781 --> Root in between the borders! Added to results. Coefficients: -48.8261851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_010317.]: Samplename: 62.5 Root: 64.192 --> Root in between the borders! Added to results. Coefficients: -61.6676851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_010317.]: Samplename: 75 Root: 77.804 --> Root in between the borders! Added to results. Coefficients: -64.5101851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_010317.]: Samplename: 87.5 Root: 80.758 --> Root in between the borders! Added to results. Coefficients: -86.0601851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_010317.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.834 --> '100 < root < 110' --> substitute 100 [20250519_010317.]: Solving cubic regression for CpG#9 Coefficients: -0.989865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_010317.]: Samplename: 0 Root: 1.475 --> Root in between the borders! Added to results. Coefficients: -6.39586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_010317.]: Samplename: 12.5 Root: 10.12 --> Root in between the borders! Added to results. Coefficients: -14.7058653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_010317.]: Samplename: 25 Root: 24.844 --> Root in between the borders! Added to results. Coefficients: -20.6238653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_010317.]: Samplename: 37.5 Root: 35.327 --> Root in between the borders! Added to results. Coefficients: -31.3958653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_010317.]: Samplename: 50 Root: 51.855 --> Root in between the borders! Added to results. Coefficients: -42.6858653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_010317.]: Samplename: 62.5 Root: 65.265 --> Root in between the borders! Added to results. Coefficients: -52.9033653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_010317.]: Samplename: 75 Root: 74.915 --> Root in between the borders! Added to results. Coefficients: -65.492531986532Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_010317.]: Samplename: 87.5 Root: 84.67 --> Root in between the borders! Added to results. Coefficients: -92.9898653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_010317.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.082 --> '100 < root < 110' --> substitute 100 [20250519_010317.]: Solving cubic regression for row_means Coefficients: -0.771215488215478Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_010317.]: Samplename: 0 Root: 1.287 --> Root in between the borders! Added to results. Coefficients: -6.47247474747474Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_010317.]: Samplename: 12.5 Root: 10.847 --> Root in between the borders! Added to results. Coefficients: -14.8972154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_010317.]: Samplename: 25 Root: 24.737 --> Root in between the borders! Added to results. Coefficients: -22.1492154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_010317.]: Samplename: 37.5 Root: 36.02 --> Root in between the borders! Added to results. Coefficients: -32.5259932659933Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_010317.]: Samplename: 50 Root: 50.639 --> Root in between the borders! Added to results. Coefficients: -44.7218821548821Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_010317.]: Samplename: 62.5 Root: 65.497 --> Root in between the borders! Added to results. Coefficients: -54.4032154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_010317.]: Samplename: 75 Root: 75.751 --> Root in between the borders! Added to results. Coefficients: -62.4313636363636Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_010317.]: Samplename: 87.5 Root: 83.403 --> Root in between the borders! Added to results. Coefficients: -84.7343265993266Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_010317.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.573 --> '100 < root < 110' --> substitute 100 [20250519_010317.]: ### Starting with regression calculations ### [20250519_010317.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient [20250519_010317.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) [20250519_010317.]: Logging df_agg: CpG#1 [20250519_010317.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010317.]: c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)[20250519_010317.]: c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)[20250519_010317.]: c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) [20250519_010317.]: Entered 'hyperbolic_regression'-Function [20250519_010317.]: 'hyperbolic_regression': minmax = FALSE [20250519_010317.]: Entered 'cubic_regression'-Function [20250519_010317.]: 'cubic_regression': minmax = FALSE [20250519_010317.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) [20250519_010317.]: Logging df_agg: CpG#2 [20250519_010317.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010317.]: c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)[20250519_010317.]: c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)[20250519_010317.]: c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) [20250519_010317.]: Entered 'hyperbolic_regression'-Function [20250519_010317.]: 'hyperbolic_regression': minmax = FALSE [20250519_010318.]: Entered 'cubic_regression'-Function [20250519_010318.]: 'cubic_regression': minmax = FALSE [20250519_010318.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) [20250519_010318.]: Logging df_agg: CpG#3 [20250519_010318.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010318.]: c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)[20250519_010318.]: c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)[20250519_010318.]: c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) [20250519_010318.]: Entered 'hyperbolic_regression'-Function [20250519_010318.]: 'hyperbolic_regression': minmax = FALSE [20250519_010318.]: Entered 'cubic_regression'-Function [20250519_010318.]: 'cubic_regression': minmax = FALSE [20250519_010318.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) [20250519_010318.]: Logging df_agg: CpG#4 [20250519_010318.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010318.]: c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)[20250519_010318.]: c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)[20250519_010318.]: c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) [20250519_010318.]: Entered 'hyperbolic_regression'-Function [20250519_010318.]: 'hyperbolic_regression': minmax = FALSE [20250519_010318.]: Entered 'cubic_regression'-Function [20250519_010318.]: 'cubic_regression': minmax = FALSE [20250519_010318.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) [20250519_010318.]: Logging df_agg: CpG#5 [20250519_010318.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010318.]: c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)[20250519_010318.]: c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)[20250519_010318.]: c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) [20250519_010318.]: Entered 'hyperbolic_regression'-Function [20250519_010318.]: 'hyperbolic_regression': minmax = FALSE [20250519_010318.]: Entered 'cubic_regression'-Function [20250519_010318.]: 'cubic_regression': minmax = FALSE [20250519_010318.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) [20250519_010318.]: Logging df_agg: CpG#6 [20250519_010318.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010318.]: c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)[20250519_010318.]: c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)[20250519_010318.]: c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) [20250519_010318.]: Entered 'hyperbolic_regression'-Function [20250519_010318.]: 'hyperbolic_regression': minmax = FALSE [20250519_010318.]: Entered 'cubic_regression'-Function [20250519_010318.]: 'cubic_regression': minmax = FALSE [20250519_010318.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) [20250519_010318.]: Logging df_agg: CpG#7 [20250519_010318.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010318.]: c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)[20250519_010318.]: c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)[20250519_010318.]: c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) [20250519_010318.]: Entered 'hyperbolic_regression'-Function [20250519_010318.]: 'hyperbolic_regression': minmax = FALSE [20250519_010319.]: Entered 'cubic_regression'-Function [20250519_010319.]: 'cubic_regression': minmax = FALSE [20250519_010319.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) [20250519_010319.]: Logging df_agg: CpG#8 [20250519_010319.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010319.]: c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)[20250519_010319.]: c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)[20250519_010319.]: c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) [20250519_010319.]: Entered 'hyperbolic_regression'-Function [20250519_010319.]: 'hyperbolic_regression': minmax = FALSE [20250519_010319.]: Entered 'cubic_regression'-Function [20250519_010319.]: 'cubic_regression': minmax = FALSE [20250519_010319.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) [20250519_010319.]: Logging df_agg: CpG#9 [20250519_010319.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010319.]: c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)[20250519_010319.]: c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)[20250519_010319.]: c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) [20250519_010319.]: Entered 'hyperbolic_regression'-Function [20250519_010319.]: 'hyperbolic_regression': minmax = FALSE [20250519_010319.]: Entered 'cubic_regression'-Function [20250519_010319.]: 'cubic_regression': minmax = FALSE [20250519_010319.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) [20250519_010319.]: Logging df_agg: row_means [20250519_010319.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010319.]: c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)[20250519_010319.]: c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)[20250519_010319.]: c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) [20250519_010319.]: Entered 'hyperbolic_regression'-Function [20250519_010319.]: 'hyperbolic_regression': minmax = FALSE [20250519_010319.]: Entered 'cubic_regression'-Function [20250519_010319.]: 'cubic_regression': minmax = FALSE [20250519_010320.]: ### Starting with plotting ### [20250519_010320.]: Creating BiasCorrected (cubic) plot No. 1 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG1_corrected_c.png [20250519_010320.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -1164.78037112998 , a = 2039.49073397227 , b = -1164.78037112998 , d = 1955.827713817 [20250519_010320.]: # CpG-site: CpG#1 Cubic: Using a = -3.42821196240366e-05 , b = 0.00462173155013798 , c = 0.850326258865826 , d = 0.500073874211125 [20250519_010322.]: Creating BiasCorrected (cubic) plot No. 2 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG2_corrected_c.png [20250519_010322.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = -464.14479041434 , a = 4988.85430873436 , b = -464.14479041434 , d = 4925.32709283236 [20250519_010322.]: # CpG-site: CpG#2 Cubic: Using a = -1.40811796906314e-05 , b = 0.00190849431754485 , c = 0.933455870941473 , d = 0.363096210099674 [20250519_010324.]: Creating BiasCorrected (cubic) plot No. 3 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG3_corrected_c.png [20250519_010324.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = -745.616753604673 , a = 2026.5285182947 , b = -745.616753604673 , d = 1953.27811759735 [20250519_010324.]: # CpG-site: CpG#3 Cubic: Using a = -2.78835051857186e-05 , b = 0.00366863707889089 , c = 0.880665171220893 , d = 0.510779308833653 [20250519_010325.]: Creating BiasCorrected (cubic) plot No. 4 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG4_corrected_c.png [20250519_010325.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = -833.077868829112 , a = 2871.13688289992 , b = -833.077868829112 , d = 2796.8115417971 [20250519_010325.]: # CpG-site: CpG#4 Cubic: Using a = -2.30028865643e-05 , b = 0.00309017851940692 , c = 0.897073107777645 , d = 0.443455761841866 [20250519_010327.]: Creating BiasCorrected (cubic) plot No. 5 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG5_corrected_c.png [20250519_010327.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = 222.093251203934 , a = 12477.4109611091 , b = 222.093251203934 , d = 12432.0779293841 [20250519_010327.]: # CpG-site: CpG#5 Cubic: Using a = -1.28632696989134e-05 , b = 0.00184822285299014 , c = 0.931001046631176 , d = 0.437963095185599 [20250519_010329.]: Creating BiasCorrected (cubic) plot No. 6 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG6_corrected_c.png [20250519_010329.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = -700.460688613084 , a = 21299.059407053 , b = -700.460688613084 , d = 21234.4652853188 [20250519_010329.]: # CpG-site: CpG#6 Cubic: Using a = -5.838335151375e-06 , b = 0.000828447163218526 , c = 0.970047764475166 , d = 0.158074701412251 [20250519_010331.]: Creating BiasCorrected (cubic) plot No. 7 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG7_corrected_c.png [20250519_010331.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -1144.91191228206 , a = 1477.91098401837 , b = -1144.91191228206 , d = 1394.69940178972 [20250519_010331.]: # CpG-site: CpG#7 Cubic: Using a = -4.54220535614047e-05 , b = 0.00607993130565131 , c = 0.805411493452389 , d = 0.615125382790254 [20250519_010333.]: Creating BiasCorrected (cubic) plot No. 8 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG8_corrected_c.png [20250519_010333.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = -349.37367288786 , a = 2377.38756952122 , b = -349.37367288786 , d = 2315.21494778767 [20250519_010333.]: # CpG-site: CpG#8 Cubic: Using a = -2.64622306669639e-05 , b = 0.00353621593528015 , c = 0.877916844709074 , d = 0.698684606493051 [20250519_010335.]: Creating BiasCorrected (cubic) plot No. 9 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG9_corrected_c.png [20250519_010335.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -1536.91699417279 , a = 3745.17619112231 , b = -1536.91699417279 , d = 3656.26801968932 [20250519_010335.]: # CpG-site: CpG#9 Cubic: Using a = -2.70976130611725e-05 , b = 0.00378478247182342 , c = 0.871825263051793 , d = 0.455475740974113 [20250519_010336.]: Creating BiasCorrected (cubic) plot No. 10 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_rowmeans_corrected_c.png [20250519_010336.]: # CpG-site: row_means Hyperbolic: Using bias_weight = -820.643805549713 , a = 3198.46619445775 , b = -820.643805549713 , d = 3124.21083989374 [20250519_010336.]: # CpG-site: row_means Cubic: Using a = -2.34328362100009e-05 , b = 0.00319139073176266 , c = 0.891842168032129 , d = 0.493517689787135 [20250519_010338.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG1_corrected_c.png [20250519_010340.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG2_corrected_c.png [20250519_010342.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG3_corrected_c.png [20250519_010344.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG4_corrected_c.png [20250519_010345.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG5_corrected_c.png [20250519_010347.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG6_corrected_c.png [20250519_010349.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG7_corrected_c.png [20250519_010351.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG8_corrected_c.png [20250519_010353.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG9_corrected_c.png [20250519_010354.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_rowmeans_corrected_c.png [20250519_010356.]: Entered 'solving_equations'-Function [20250519_010356.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 79.867338305743 [20250519_010356.]: Samplename: Sample#1 Root: 79.867 --> Root in between the borders! Added to results. Hyperbolic solved: 29.7900144307253 [20250519_010356.]: Samplename: Sample#10 Root: 29.79 --> Root in between the borders! Added to results. Hyperbolic solved: 41.6525328583179 [20250519_010356.]: Samplename: Sample#2 Root: 41.653 --> Root in between the borders! Added to results. Hyperbolic solved: 57.4651978180159 [20250519_010356.]: Samplename: Sample#3 Root: 57.465 --> Root in between the borders! Added to results. Hyperbolic solved: 9.20072297150156 [20250519_010356.]: Samplename: Sample#4 Root: 9.201 --> Root in between the borders! Added to results. Hyperbolic solved: 21.8059605790798 [20250519_010356.]: Samplename: Sample#5 Root: 21.806 --> Root in between the borders! Added to results. Hyperbolic solved: 23.0837964625959 [20250519_010356.]: Samplename: Sample#6 Root: 23.084 --> Root in between the borders! Added to results. Hyperbolic solved: 45.5034148455809 [20250519_010356.]: Samplename: Sample#7 Root: 45.503 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6987854360424 [20250519_010356.]: Samplename: Sample#8 Root: 85.699 --> Root in between the borders! Added to results. Hyperbolic solved: -3.66510576608508 [20250519_010356.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -3.665 --> '-10 < root < 0' --> substitute 0 [20250519_010356.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 76.7227930253137 [20250519_010356.]: Samplename: Sample#1 Root: 76.723 --> Root in between the borders! Added to results. Hyperbolic solved: 30.9924274267338 [20250519_010356.]: Samplename: Sample#10 Root: 30.992 --> Root in between the borders! Added to results. Hyperbolic solved: 42.6810881616502 [20250519_010356.]: Samplename: Sample#2 Root: 42.681 --> Root in between the borders! Added to results. Hyperbolic solved: 58.93379071467 [20250519_010356.]: Samplename: Sample#3 Root: 58.934 --> Root in between the borders! Added to results. Hyperbolic solved: 4.98020463922415 [20250519_010356.]: Samplename: Sample#4 Root: 4.98 --> Root in between the borders! Added to results. Hyperbolic solved: 20.2480263316954 [20250519_010356.]: Samplename: Sample#5 Root: 20.248 --> Root in between the borders! Added to results. Hyperbolic solved: 17.8133085422138 [20250519_010356.]: Samplename: Sample#6 Root: 17.813 --> Root in between the borders! Added to results. Hyperbolic solved: 40.917674420917 [20250519_010356.]: Samplename: Sample#7 Root: 40.918 --> Root in between the borders! Added to results. Hyperbolic solved: 86.045071534744 [20250519_010356.]: Samplename: Sample#8 Root: 86.045 --> Root in between the borders! Added to results. Hyperbolic solved: 3.1157977862424 [20250519_010356.]: Samplename: Sample#9 Root: 3.116 --> Root in between the borders! Added to results. [20250519_010356.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 74.9349278251272 [20250519_010356.]: Samplename: Sample#1 Root: 74.935 --> Root in between the borders! Added to results. Hyperbolic solved: 27.6844390885298 [20250519_010356.]: Samplename: Sample#10 Root: 27.684 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8520223561161 [20250519_010356.]: Samplename: Sample#2 Root: 41.852 --> Root in between the borders! Added to results. Hyperbolic solved: 55.8325219766746 [20250519_010356.]: Samplename: Sample#3 Root: 55.833 --> Root in between the borders! Added to results. Hyperbolic solved: 8.03518759540723 [20250519_010356.]: Samplename: Sample#4 Root: 8.035 --> Root in between the borders! Added to results. Hyperbolic solved: 24.1066316660805 [20250519_010356.]: Samplename: Sample#5 Root: 24.107 --> Root in between the borders! Added to results. Hyperbolic solved: 26.2419826081677 [20250519_010356.]: Samplename: Sample#6 Root: 26.242 --> Root in between the borders! Added to results. Hyperbolic solved: 44.0944957314077 [20250519_010356.]: Samplename: Sample#7 Root: 44.094 --> Root in between the borders! Added to results. Hyperbolic solved: 85.8279384942248 [20250519_010356.]: Samplename: Sample#8 Root: 85.828 --> Root in between the borders! Added to results. Hyperbolic solved: -0.66649088715512 [20250519_010356.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.666 --> '-10 < root < 0' --> substitute 0 [20250519_010356.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 76.349525731503 [20250519_010356.]: Samplename: Sample#1 Root: 76.35 --> Root in between the borders! Added to results. Hyperbolic solved: 28.2568543207918 [20250519_010356.]: Samplename: Sample#10 Root: 28.257 --> Root in between the borders! Added to results. Hyperbolic solved: 43.408980967851 [20250519_010356.]: Samplename: Sample#2 Root: 43.409 --> Root in between the borders! Added to results. Hyperbolic solved: 58.5435202418955 [20250519_010356.]: Samplename: Sample#3 Root: 58.544 --> Root in between the borders! Added to results. Hyperbolic solved: 10.308707716736 [20250519_010356.]: Samplename: Sample#4 Root: 10.309 --> Root in between the borders! Added to results. Hyperbolic solved: 22.1830453155629 [20250519_010356.]: Samplename: Sample#5 Root: 22.183 --> Root in between the borders! Added to results. Hyperbolic solved: 27.133776120676 [20250519_010356.]: Samplename: Sample#6 Root: 27.134 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8321067699793 [20250519_010356.]: Samplename: Sample#7 Root: 41.832 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6890182703099 [20250519_010356.]: Samplename: Sample#8 Root: 85.689 --> Root in between the borders! Added to results. Hyperbolic solved: 2.42232661674155 [20250519_010356.]: Samplename: Sample#9 Root: 2.422 --> Root in between the borders! Added to results. [20250519_010356.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 72.8126911247073 [20250519_010356.]: Samplename: Sample#1 Root: 72.813 --> Root in between the borders! Added to results. Hyperbolic solved: 26.4206935777278 [20250519_010356.]: Samplename: Sample#10 Root: 26.421 --> Root in between the borders! Added to results. Hyperbolic solved: 44.2738761535648 [20250519_010356.]: Samplename: Sample#2 Root: 44.274 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5324382038438 [20250519_010356.]: Samplename: Sample#3 Root: 53.532 --> Root in between the borders! Added to results. Hyperbolic solved: 10.0373723654377 [20250519_010356.]: Samplename: Sample#4 Root: 10.037 --> Root in between the borders! Added to results. Hyperbolic solved: 22.8733116049306 [20250519_010356.]: Samplename: Sample#5 Root: 22.873 --> Root in between the borders! Added to results. Hyperbolic solved: 25.9417915879453 [20250519_010356.]: Samplename: Sample#6 Root: 25.942 --> Root in between the borders! Added to results. Hyperbolic solved: 42.7945071576182 [20250519_010356.]: Samplename: Sample#7 Root: 42.795 --> Root in between the borders! Added to results. Hyperbolic solved: 88.8741214991654 [20250519_010356.]: Samplename: Sample#8 Root: 88.874 --> Root in between the borders! Added to results. Hyperbolic solved: 2.34647622155857 [20250519_010356.]: Samplename: Sample#9 Root: 2.346 --> Root in between the borders! Added to results. [20250519_010356.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 79.278059253184 [20250519_010356.]: Samplename: Sample#1 Root: 79.278 --> Root in between the borders! Added to results. Hyperbolic solved: 30.2012459144106 [20250519_010356.]: Samplename: Sample#10 Root: 30.201 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8474392901329 [20250519_010356.]: Samplename: Sample#2 Root: 41.847 --> Root in between the borders! Added to results. Hyperbolic solved: 56.8423515999057 [20250519_010356.]: Samplename: Sample#3 Root: 56.842 --> Root in between the borders! Added to results. Hyperbolic solved: 8.87856073331453 [20250519_010356.]: Samplename: Sample#4 Root: 8.879 --> Root in between the borders! Added to results. Hyperbolic solved: 18.6901596391315 [20250519_010356.]: Samplename: Sample#5 Root: 18.69 --> Root in between the borders! Added to results. Hyperbolic solved: 29.9309263719602 [20250519_010356.]: Samplename: Sample#6 Root: 29.931 --> Root in between the borders! Added to results. Hyperbolic solved: 42.814855924799 [20250519_010356.]: Samplename: Sample#7 Root: 42.815 --> Root in between the borders! Added to results. Hyperbolic solved: 86.75018307028 [20250519_010356.]: Samplename: Sample#8 Root: 86.75 --> Root in between the borders! Added to results. Hyperbolic solved: 1.51516233882239 [20250519_010356.]: Samplename: Sample#9 Root: 1.515 --> Root in between the borders! Added to results. [20250519_010356.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 78.2565541874121 [20250519_010356.]: Samplename: Sample#1 Root: 78.257 --> Root in between the borders! Added to results. Hyperbolic solved: 25.488740177574 [20250519_010356.]: Samplename: Sample#10 Root: 25.489 --> Root in between the borders! Added to results. Hyperbolic solved: 47.3712173704701 [20250519_010356.]: Samplename: Sample#2 Root: 47.371 --> Root in between the borders! Added to results. Hyperbolic solved: 58.3142577916519 [20250519_010356.]: Samplename: Sample#3 Root: 58.314 --> Root in between the borders! Added to results. Hyperbolic solved: 11.7212348600366 [20250519_010356.]: Samplename: Sample#4 Root: 11.721 --> Root in between the borders! Added to results. Hyperbolic solved: 25.3797494985646 [20250519_010356.]: Samplename: Sample#5 Root: 25.38 --> Root in between the borders! Added to results. Hyperbolic solved: 29.4095117400265 [20250519_010356.]: Samplename: Sample#6 Root: 29.41 --> Root in between the borders! Added to results. Hyperbolic solved: 44.5754992753069 [20250519_010356.]: Samplename: Sample#7 Root: 44.575 --> Root in between the borders! Added to results. Hyperbolic solved: 85.9628719316752 [20250519_010356.]: Samplename: Sample#8 Root: 85.963 --> Root in between the borders! Added to results. Hyperbolic solved: -4.16453560967523 [20250519_010356.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -4.165 --> '-10 < root < 0' --> substitute 0 [20250519_010356.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 72.9696611539955 [20250519_010356.]: Samplename: Sample#1 Root: 72.97 --> Root in between the borders! Added to results. Hyperbolic solved: 27.8250961443825 [20250519_010356.]: Samplename: Sample#10 Root: 27.825 --> Root in between the borders! Added to results. Hyperbolic solved: 34.9068396054724 [20250519_010356.]: Samplename: Sample#2 Root: 34.907 --> Root in between the borders! Added to results. Hyperbolic solved: 59.163744843639 [20250519_010356.]: Samplename: Sample#3 Root: 59.164 --> Root in between the borders! Added to results. Hyperbolic solved: 9.64644348381889 [20250519_010356.]: Samplename: Sample#4 Root: 9.646 --> Root in between the borders! Added to results. Hyperbolic solved: 19.3522608313684 [20250519_010356.]: Samplename: Sample#5 Root: 19.352 --> Root in between the borders! Added to results. Hyperbolic solved: 38.5571445759356 [20250519_010356.]: Samplename: Sample#6 Root: 38.557 --> Root in between the borders! Added to results. Hyperbolic solved: 44.7759635921689 [20250519_010356.]: Samplename: Sample#7 Root: 44.776 --> Root in between the borders! Added to results. Hyperbolic solved: 84.8599672002532 [20250519_010356.]: Samplename: Sample#8 Root: 84.86 --> Root in between the borders! Added to results. Hyperbolic solved: -2.05045066787228 [20250519_010356.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.05 --> '-10 < root < 0' --> substitute 0 [20250519_010356.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 81.0416669543132 [20250519_010356.]: Samplename: Sample#1 Root: 81.042 --> Root in between the borders! Added to results. Hyperbolic solved: 26.6441665578561 [20250519_010356.]: Samplename: Sample#10 Root: 26.644 --> Root in between the borders! Added to results. Hyperbolic solved: 45.7046185029368 [20250519_010356.]: Samplename: Sample#2 Root: 45.705 --> Root in between the borders! Added to results. Hyperbolic solved: 56.9767969678006 [20250519_010356.]: Samplename: Sample#3 Root: 56.977 --> Root in between the borders! Added to results. Hyperbolic solved: 6.81231488238752 [20250519_010356.]: Samplename: Sample#4 Root: 6.812 --> Root in between the borders! Added to results. Hyperbolic solved: 22.928478377057 [20250519_010356.]: Samplename: Sample#5 Root: 22.928 --> Root in between the borders! Added to results. Hyperbolic solved: 38.8653192337298 [20250519_010356.]: Samplename: Sample#6 Root: 38.865 --> Root in between the borders! Added to results. Hyperbolic solved: 43.6855618249121 [20250519_010356.]: Samplename: Sample#7 Root: 43.686 --> Root in between the borders! Added to results. Hyperbolic solved: 88.0155362134065 [20250519_010356.]: Samplename: Sample#8 Root: 88.016 --> Root in between the borders! Added to results. Hyperbolic solved: -3.35609615796437 [20250519_010356.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -3.356 --> '-10 < root < 0' --> substitute 0 [20250519_010356.]: Solving hyperbolic regression for row_means Hyperbolic solved: 77.0692788889392 [20250519_010356.]: Samplename: Sample#1 Root: 77.069 --> Root in between the borders! Added to results. Hyperbolic solved: 28.3620037732087 [20250519_010356.]: Samplename: Sample#10 Root: 28.362 --> Root in between the borders! Added to results. Hyperbolic solved: 42.5026157763218 [20250519_010356.]: Samplename: Sample#2 Root: 42.503 --> Root in between the borders! Added to results. Hyperbolic solved: 57.2972029249363 [20250519_010356.]: Samplename: Sample#3 Root: 57.297 --> Root in between the borders! Added to results. Hyperbolic solved: 8.82704268325745 [20250519_010356.]: Samplename: Sample#4 Root: 8.827 --> Root in between the borders! Added to results. Hyperbolic solved: 21.8102595595999 [20250519_010356.]: Samplename: Sample#5 Root: 21.81 --> Root in between the borders! Added to results. Hyperbolic solved: 28.7228654114699 [20250519_010356.]: Samplename: Sample#6 Root: 28.723 --> Root in between the borders! Added to results. Hyperbolic solved: 43.4105084715017 [20250519_010356.]: Samplename: Sample#7 Root: 43.411 --> Root in between the borders! Added to results. Hyperbolic solved: 86.4143551575745 [20250519_010356.]: Samplename: Sample#8 Root: 86.414 --> Root in between the borders! Added to results. Hyperbolic solved: -0.237016010782264 [20250519_010356.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.237 --> '-10 < root < 0' --> substitute 0 [20250519_010356.]: on_start: using future::plan("sequential") [20250519_010356.]: Entered 'clean_dt'-Function [20250519_010356.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_010356.]: got experimental data [20250519_010356.]: Entered 'clean_dt'-Function [20250519_010356.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_010356.]: got calibration data [20250519_010356.]: ### Starting with regression calculations ### [20250519_010356.]: Entered 'regression_type1'-Function [20250519_010356.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_010357.]: Logging df_agg: CpG#1 [20250519_010357.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010357.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_010357.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_010357.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_010357.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_010357.]: Entered 'hyperbolic_regression'-Function [20250519_010357.]: 'hyperbolic_regression': minmax = FALSE [20250519_010357.]: Entered 'cubic_regression'-Function [20250519_010357.]: 'cubic_regression': minmax = FALSE [20250519_010357.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_010357.]: Logging df_agg: CpG#2 [20250519_010357.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010357.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_010357.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_010357.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_010357.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_010357.]: Entered 'hyperbolic_regression'-Function [20250519_010357.]: 'hyperbolic_regression': minmax = FALSE [20250519_010357.]: Entered 'cubic_regression'-Function [20250519_010357.]: 'cubic_regression': minmax = FALSE [20250519_010357.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_010357.]: Logging df_agg: CpG#3 [20250519_010357.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010357.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_010357.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_010357.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_010357.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_010357.]: Entered 'hyperbolic_regression'-Function [20250519_010357.]: 'hyperbolic_regression': minmax = FALSE [20250519_010357.]: Entered 'cubic_regression'-Function [20250519_010357.]: 'cubic_regression': minmax = FALSE [20250519_010357.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_010357.]: Logging df_agg: CpG#4 [20250519_010357.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010357.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_010357.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_010357.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_010357.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_010357.]: Entered 'hyperbolic_regression'-Function [20250519_010357.]: 'hyperbolic_regression': minmax = FALSE [20250519_010357.]: Entered 'cubic_regression'-Function [20250519_010357.]: 'cubic_regression': minmax = FALSE [20250519_010357.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_010357.]: Logging df_agg: CpG#5 [20250519_010357.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010357.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_010357.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_010357.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_010357.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_010357.]: Entered 'hyperbolic_regression'-Function [20250519_010357.]: 'hyperbolic_regression': minmax = FALSE [20250519_010357.]: Entered 'cubic_regression'-Function [20250519_010357.]: 'cubic_regression': minmax = FALSE [20250519_010357.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_010357.]: Logging df_agg: CpG#6 [20250519_010357.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010357.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_010357.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_010357.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_010357.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_010357.]: Entered 'hyperbolic_regression'-Function [20250519_010357.]: 'hyperbolic_regression': minmax = FALSE [20250519_010357.]: Entered 'cubic_regression'-Function [20250519_010357.]: 'cubic_regression': minmax = FALSE [20250519_010357.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_010357.]: Logging df_agg: CpG#7 [20250519_010357.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010357.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_010357.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_010357.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_010357.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_010357.]: Entered 'hyperbolic_regression'-Function [20250519_010357.]: 'hyperbolic_regression': minmax = FALSE [20250519_010357.]: Entered 'cubic_regression'-Function [20250519_010357.]: 'cubic_regression': minmax = FALSE [20250519_010357.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_010357.]: Logging df_agg: CpG#8 [20250519_010357.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010357.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_010357.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_010357.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_010357.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_010357.]: Entered 'hyperbolic_regression'-Function [20250519_010357.]: 'hyperbolic_regression': minmax = FALSE [20250519_010357.]: Entered 'cubic_regression'-Function [20250519_010357.]: 'cubic_regression': minmax = FALSE [20250519_010357.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_010357.]: Logging df_agg: CpG#9 [20250519_010357.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010357.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_010357.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_010357.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_010357.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_010357.]: Entered 'hyperbolic_regression'-Function [20250519_010357.]: 'hyperbolic_regression': minmax = FALSE [20250519_010357.]: Entered 'cubic_regression'-Function [20250519_010357.]: 'cubic_regression': minmax = FALSE [20250519_010357.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723122, 2.47760998435345)c(5.94911111111111, 0.84962962962963, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703704, 19.6995555555555, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_010357.]: Logging df_agg: row_means [20250519_010357.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010357.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_010357.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723122, 2.47760998435345)[20250519_010357.]: c(5.94911111111111, 0.84962962962963, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_010357.]: c(NA, 6.79703703703704, 19.6995555555555, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_010357.]: Entered 'hyperbolic_regression'-Function [20250519_010357.]: 'hyperbolic_regression': minmax = FALSE [20250519_010357.]: Entered 'cubic_regression'-Function [20250519_010357.]: 'cubic_regression': minmax = FALSE [20250519_010358.]: ### Starting with plotting ### [20250519_010358.]: Creating plot No. 1 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG1.png [20250519_010358.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -123249135.40483 , a = 49589952.0989949 , b = -123249135.40483 , d = 59280789.0121927 [20250519_010358.]: # CpG-site: CpG#1 Cubic: Using a = 6.53413423120091e-05 , b = -0.0055806968734969 , c = 0.784061853455188 , d = 1.93182659932656 [20250519_010400.]: Creating plot No. 2 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG2.png [20250519_010400.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = 167929575.171327 , a = 42299444.0962795 , b = 167929575.171327 , d = 47897274.2220611 [20250519_010400.]: # CpG-site: CpG#2 Cubic: Using a = 8.04237934904601e-06 , b = 0.00293158941798942 , c = 0.514821742825076 , d = 9.27667003367003 [20250519_010402.]: Creating plot No. 3 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG3.png [20250519_010402.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = 31004745.3756238 , a = 31050253.3779659 , b = 31004745.3756238 , d = 40665231.758814 [20250519_010402.]: # CpG-site: CpG#3 Cubic: Using a = 2.30555869809204e-05 , b = -0.000797009331409346 , c = 0.62783129228796 , d = 3.88705218855218 [20250519_010404.]: Creating plot No. 4 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG4.png [20250519_010404.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = 128022258.276155 , a = 42779497.5538086 , b = 128022258.276155 , d = 50999439.9227065 [20250519_010404.]: # CpG-site: CpG#4 Cubic: Using a = 3.2166356902357e-05 , b = -0.0015913142857143 , c = 0.697398364598366 , d = 6.17055050505048 [20250519_010406.]: Creating plot No. 5 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG5.png [20250519_010406.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = 29447513.6184781 , a = 41864399.8673718 , b = 29447513.6184781 , d = 55382346.8470806 [20250519_010406.]: # CpG-site: CpG#5 Cubic: Using a = -4.48459708193036e-06 , b = 0.00375371236171235 , c = 0.422446384479718 , d = 5.17203872053872 [20250519_010408.]: Creating plot No. 6 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG6.png [20250519_010408.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = 17150080.5960938 , a = 40932834.1078748 , b = 17150080.5960938 , d = 46723471.6686748 [20250519_010408.]: # CpG-site: CpG#6 Cubic: Using a = 2.38852884399552e-05 , b = 0.000918637037037021 , c = 0.561022132435467 , d = 6.1479276094276 [20250519_010410.]: Creating plot No. 7 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG7.png [20250519_010410.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -57204506.2862076 , a = 29554652.6579632 , b = -57204506.2862076 , d = 48548425.6483363 [20250519_010410.]: # CpG-site: CpG#7 Cubic: Using a = 4.98010505050505e-05 , b = -0.00436152150072151 , c = 0.579588917748918 , d = 1.72104545454544 [20250519_010412.]: Creating plot No. 8 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG8.png [20250519_010412.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = 263871870.982016 , a = 38159683.1948542 , b = 263871870.982016 , d = 45284547.6704654 [20250519_010412.]: # CpG-site: CpG#8 Cubic: Using a = -1.50060965207632e-05 , b = 0.00447978143338143 , c = 0.534897737694404 , d = 9.56981481481482 [20250519_010415.]: Creating plot No. 9 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG9.png [20250519_010415.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -224404013.660658 , a = 51672526.9851193 , b = -224404013.660658 , d = 60201205.3472543 [20250519_010415.]: # CpG-site: CpG#9 Cubic: Using a = 7.72300426487093e-05 , b = -0.00542281173641174 , c = 0.67899229597563 , d = 2.72413468013467 [20250519_010417.]: Creating plot No. 10 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_rowmeans.png [20250519_010417.]: # CpG-site: row_means Hyperbolic: Using bias_weight = 40333655.1288316 , a = 44937623.5310126 , b = 40333655.1288316 , d = 55678831.3245349 [20250519_010417.]: # CpG-site: row_means Cubic: Using a = 2.88923726150392e-05 , b = -0.0006299592752926 , c = 0.600117857944524 , d = 5.17789562289563 [20250519_010419.]: Entered 'solving_equations'-Function [20250519_010419.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 6.03337011705089 [20250519_010419.]: Samplename: 0 Root: 6.033 --> Root in between the borders! Added to results. Hyperbolic solved: 14.7663097441095 [20250519_010419.]: Samplename: 12.5 Root: 14.766 --> Root in between the borders! Added to results. Hyperbolic solved: 23.1900365713551 [20250519_010419.]: Samplename: 25 Root: 23.19 --> Root in between the borders! Added to results. Hyperbolic solved: 33.8197153352119 [20250519_010419.]: Samplename: 37.5 Root: 33.82 --> Root in between the borders! Added to results. Hyperbolic solved: 46.5156833662696 [20250519_010419.]: Samplename: 50 Root: 46.516 --> Root in between the borders! Added to results. Hyperbolic solved: 60.2074431300908 [20250519_010419.]: Samplename: 62.5 Root: 60.207 --> Root in between the borders! Added to results. Hyperbolic solved: 71.6105733239506 [20250519_010419.]: Samplename: 75 Root: 71.611 --> Root in between the borders! Added to results. Hyperbolic solved: 80.2235915426204 [20250519_010419.]: Samplename: 87.5 Root: 80.224 --> Root in between the borders! Added to results. Hyperbolic solved: 113.633279788745 [20250519_010419.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20250519_010419.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 6.85513576175294 [20250519_010419.]: Samplename: 0 Root: 6.855 --> Root in between the borders! Added to results. Hyperbolic solved: 13.7133339963707 [20250519_010419.]: Samplename: 12.5 Root: 13.713 --> Root in between the borders! Added to results. Hyperbolic solved: 24.5830349243156 [20250519_010419.]: Samplename: 25 Root: 24.583 --> Root in between the borders! Added to results. Hyperbolic solved: 31.8277435330554 [20250519_010419.]: Samplename: 37.5 Root: 31.828 --> Root in between the borders! Added to results. Hyperbolic solved: 43.0277185945539 [20250519_010419.]: Samplename: 50 Root: 43.028 --> Root in between the borders! Added to results. Hyperbolic solved: 63.1097818281222 [20250519_010419.]: Samplename: 62.5 Root: 63.11 --> Root in between the borders! Added to results. Hyperbolic solved: 73.3036858899805 [20250519_010419.]: Samplename: 75 Root: 73.304 --> Root in between the borders! Added to results. Hyperbolic solved: 84.4808447799272 [20250519_010419.]: Samplename: 87.5 Root: 84.481 --> Root in between the borders! Added to results. Hyperbolic solved: 109.0987325611 [20250519_010419.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 109.099 --> '100 < root < 110' --> substitute 100 [20250519_010419.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 5.27473119209009 [20250519_010419.]: Samplename: 0 Root: 5.275 --> Root in between the borders! Added to results. Hyperbolic solved: 12.7004989900866 [20250519_010419.]: Samplename: 12.5 Root: 12.7 --> Root in between the borders! Added to results. Hyperbolic solved: 24.2988465005425 [20250519_010419.]: Samplename: 25 Root: 24.299 --> Root in between the borders! Added to results. Hyperbolic solved: 33.7074476249853 [20250519_010419.]: Samplename: 37.5 Root: 33.707 --> Root in between the borders! Added to results. Hyperbolic solved: 47.0168800224423 [20250519_010419.]: Samplename: 50 Root: 47.017 --> Root in between the borders! Added to results. Hyperbolic solved: 61.5718102366584 [20250519_010419.]: Samplename: 62.5 Root: 61.572 --> Root in between the borders! Added to results. Hyperbolic solved: 76.091389954119 [20250519_010419.]: Samplename: 75 Root: 76.091 --> Root in between the borders! Added to results. Hyperbolic solved: 79.6023799465703 [20250519_010419.]: Samplename: 87.5 Root: 79.602 --> Root in between the borders! Added to results. Hyperbolic solved: 109.736019452694 [20250519_010419.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 109.736 --> '100 < root < 110' --> substitute 100 [20250519_010419.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 5.07584235183922 [20250519_010419.]: Samplename: 0 Root: 5.076 --> Root in between the borders! Added to results. Hyperbolic solved: 14.2021272601476 [20250519_010419.]: Samplename: 12.5 Root: 14.202 --> Root in between the borders! Added to results. Hyperbolic solved: 23.203637284743 [20250519_010419.]: Samplename: 25 Root: 23.204 --> Root in between the borders! Added to results. Hyperbolic solved: 34.7960863609446 [20250519_010419.]: Samplename: 37.5 Root: 34.796 --> Root in between the borders! Added to results. Hyperbolic solved: 44.4638162018573 [20250519_010419.]: Samplename: 50 Root: 44.464 --> Root in between the borders! Added to results. Hyperbolic solved: 64.2755507391393 [20250519_010419.]: Samplename: 62.5 Root: 64.276 --> Root in between the borders! Added to results. Hyperbolic solved: 71.7670214785718 [20250519_010419.]: Samplename: 75 Root: 71.767 --> Root in between the borders! Added to results. Hyperbolic solved: 82.5599568822802 [20250519_010419.]: Samplename: 87.5 Root: 82.56 --> Root in between the borders! Added to results. Hyperbolic solved: 109.655967198106 [20250519_010419.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 109.656 --> '100 < root < 110' --> substitute 100 [20250519_010419.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 6.96411772281142 [20250519_010419.]: Samplename: 0 Root: 6.964 --> Root in between the borders! Added to results. Hyperbolic solved: 12.4462106087166 [20250519_010419.]: Samplename: 12.5 Root: 12.446 --> Root in between the borders! Added to results. Hyperbolic solved: 22.9738427992134 [20250519_010419.]: Samplename: 25 Root: 22.974 --> Root in between the borders! Added to results. Hyperbolic solved: 34.1311793989123 [20250519_010419.]: Samplename: 37.5 Root: 34.131 --> Root in between the borders! Added to results. Hyperbolic solved: 46.7456955019114 [20250519_010419.]: Samplename: 50 Root: 46.746 --> Root in between the borders! Added to results. Hyperbolic solved: 58.6021949543013 [20250519_010419.]: Samplename: 62.5 Root: 58.602 --> Root in between the borders! Added to results. Hyperbolic solved: 74.5100864344263 [20250519_010419.]: Samplename: 75 Root: 74.51 --> Root in between the borders! Added to results. Hyperbolic solved: 86.8549692943716 [20250519_010419.]: Samplename: 87.5 Root: 86.855 --> Root in between the borders! Added to results. Hyperbolic solved: 106.771715286513 [20250519_010419.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 106.772 --> '100 < root < 110' --> substitute 100 [20250519_010419.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 6.82248549455073 [20250519_010419.]: Samplename: 0 Root: 6.822 --> Root in between the borders! Added to results. Hyperbolic solved: 14.2907258003629 [20250519_010419.]: Samplename: 12.5 Root: 14.291 --> Root in between the borders! Added to results. Hyperbolic solved: 24.7343949718638 [20250519_010419.]: Samplename: 25 Root: 24.734 --> Root in between the borders! Added to results. Hyperbolic solved: 31.7110486196861 [20250519_010419.]: Samplename: 37.5 Root: 31.711 --> Root in between the borders! Added to results. Hyperbolic solved: 44.7745870553137 [20250519_010419.]: Samplename: 50 Root: 44.775 --> Root in between the borders! Added to results. Hyperbolic solved: 60.1336283719223 [20250519_010419.]: Samplename: 62.5 Root: 60.134 --> Root in between the borders! Added to results. Hyperbolic solved: 70.3292384995337 [20250519_010419.]: Samplename: 75 Root: 70.329 --> Root in between the borders! Added to results. Hyperbolic solved: 88.2655480529845 [20250519_010419.]: Samplename: 87.5 Root: 88.266 --> Root in between the borders! Added to results. Hyperbolic solved: 108.938360440145 [20250519_010419.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 108.938 --> '100 < root < 110' --> substitute 100 [20250519_010419.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 6.75841824784972 [20250519_010419.]: Samplename: 0 Root: 6.758 --> Root in between the borders! Added to results. Hyperbolic solved: 13.6258602138932 [20250519_010419.]: Samplename: 12.5 Root: 13.626 --> Root in between the borders! Added to results. Hyperbolic solved: 23.2672219789518 [20250519_010419.]: Samplename: 25 Root: 23.267 --> Root in between the borders! Added to results. Hyperbolic solved: 33.4287658578254 [20250519_010419.]: Samplename: 37.5 Root: 33.429 --> Root in between the borders! Added to results. Hyperbolic solved: 47.5236851621158 [20250519_010419.]: Samplename: 50 Root: 47.524 --> Root in between the borders! Added to results. Hyperbolic solved: 60.5558050598499 [20250519_010419.]: Samplename: 62.5 Root: 60.556 --> Root in between the borders! Added to results. Hyperbolic solved: 72.3896034879297 [20250519_010419.]: Samplename: 75 Root: 72.39 --> Root in between the borders! Added to results. Hyperbolic solved: 77.2628610417933 [20250519_010419.]: Samplename: 87.5 Root: 77.263 --> Root in between the borders! Added to results. Hyperbolic solved: 115.187789384315 [20250519_010419.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20250519_010419.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 5.74253131510683 [20250519_010419.]: Samplename: 0 Root: 5.743 --> Root in between the borders! Added to results. Hyperbolic solved: 10.9055216254216 [20250519_010419.]: Samplename: 12.5 Root: 10.906 --> Root in between the borders! Added to results. Hyperbolic solved: 24.5329419436721 [20250519_010419.]: Samplename: 25 Root: 24.533 --> Root in between the borders! Added to results. Hyperbolic solved: 32.1516426249373 [20250519_010419.]: Samplename: 37.5 Root: 32.152 --> Root in between the borders! Added to results. Hyperbolic solved: 49.0919870496865 [20250519_010419.]: Samplename: 50 Root: 49.092 --> Root in between the borders! Added to results. Hyperbolic solved: 62.3843810658735 [20250519_010419.]: Samplename: 62.5 Root: 62.384 --> Root in between the borders! Added to results. Hyperbolic solved: 77.6235905386846 [20250519_010419.]: Samplename: 75 Root: 77.624 --> Root in between the borders! Added to results. Hyperbolic solved: 80.9968312605936 [20250519_010419.]: Samplename: 87.5 Root: 80.997 --> Root in between the borders! Added to results. Hyperbolic solved: 106.570581137258 [20250519_010419.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 106.571 --> '100 < root < 110' --> substitute 100 [20250519_010419.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 8.66981641228949 [20250519_010419.]: Samplename: 0 Root: 8.67 --> Root in between the borders! Added to results. Hyperbolic solved: 14.9680920996829 [20250519_010419.]: Samplename: 12.5 Root: 14.968 --> Root in between the borders! Added to results. Hyperbolic solved: 24.6496838227806 [20250519_010419.]: Samplename: 25 Root: 24.65 --> Root in between the borders! Added to results. Hyperbolic solved: 31.5444702961411 [20250519_010419.]: Samplename: 37.5 Root: 31.544 --> Root in between the borders! Added to results. Hyperbolic solved: 44.0944303975514 [20250519_010419.]: Samplename: 50 Root: 44.094 --> Root in between the borders! Added to results. Hyperbolic solved: 57.2478940106457 [20250519_010419.]: Samplename: 62.5 Root: 57.248 --> Root in between the borders! Added to results. Hyperbolic solved: 69.1518417879612 [20250519_010419.]: Samplename: 75 Root: 69.152 --> Root in between the borders! Added to results. Hyperbolic solved: 83.8189177560508 [20250519_010419.]: Samplename: 87.5 Root: 83.819 --> Root in between the borders! Added to results. Hyperbolic solved: 115.854857900091 [20250519_010419.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20250519_010419.]: Solving hyperbolic regression for row_means Hyperbolic solved: 6.47354965864506 [20250519_010419.]: Samplename: 0 Root: 6.474 --> Root in between the borders! Added to results. Hyperbolic solved: 13.5375540918194 [20250519_010419.]: Samplename: 12.5 Root: 13.538 --> Root in between the borders! Added to results. Hyperbolic solved: 23.976024251167 [20250519_010419.]: Samplename: 25 Root: 23.976 --> Root in between the borders! Added to results. Hyperbolic solved: 32.9614416466039 [20250519_010419.]: Samplename: 37.5 Root: 32.961 --> Root in between the borders! Added to results. Hyperbolic solved: 45.8185453601259 [20250519_010419.]: Samplename: 50 Root: 45.819 --> Root in between the borders! Added to results. Hyperbolic solved: 60.9295837234236 [20250519_010419.]: Samplename: 62.5 Root: 60.93 --> Root in between the borders! Added to results. Hyperbolic solved: 72.9250251350637 [20250519_010419.]: Samplename: 75 Root: 72.925 --> Root in between the borders! Added to results. Hyperbolic solved: 82.8721280175692 [20250519_010419.]: Samplename: 87.5 Root: 82.872 --> Root in between the borders! Added to results. Hyperbolic solved: 110.506149316569 [20250519_010419.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA [20250519_010419.]: ### Starting with regression calculations ### [20250519_010419.]: Entered 'regression_type1'-Function [20250519_010419.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)c(6.03337011705089, 14.7663097441095, 23.1900365713551, 33.8197153352119, 46.5156833662696, 60.2074431300908, 71.6105733239506, 80.2235915426204)c(6.03337011705089, 2.2663097441095, 1.8099634286449, 3.6802846647881, 3.4843166337304, 2.2925568699092, 3.3894266760494, 7.2764084573796)c(NA, 18.130477952876, 7.2398537145796, 9.81409243943492, 6.9686332674608, 3.66809099185472, 4.51923556806586, 8.3158953798624) [20250519_010419.]: Logging df_agg: CpG#1 [20250519_010419.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)[20250519_010419.]: c(6.03337011705089, 14.7663097441095, 23.1900365713551, 33.8197153352119, 46.5156833662696, 60.2074431300908, 71.6105733239506, 80.2235915426204)[20250519_010419.]: c(6.03337011705089, 2.2663097441095, 1.8099634286449, 3.6802846647881, 3.4843166337304, 2.2925568699092, 3.3894266760494, 7.2764084573796)[20250519_010419.]: c(NA, 18.130477952876, 7.2398537145796, 9.81409243943492, 6.9686332674608, 3.66809099185472, 4.51923556806586, 8.3158953798624) [20250519_010419.]: Entered 'hyperbolic_regression'-Function [20250519_010419.]: 'hyperbolic_regression': minmax = FALSE [20250519_010419.]: Entered 'cubic_regression'-Function [20250519_010419.]: 'cubic_regression': minmax = FALSE [20250519_010419.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.85513576175294, 13.7133339963707, 24.5830349243156, 31.8277435330554, 43.0277185945539, 63.1097818281222, 73.3036858899805, 84.4808447799272, 100)c(6.85513576175294, 1.2133339963707, 0.416965075684399, 5.6722564669446, 6.9722814054461, 0.609781828122202, 1.6963141100195, 3.0191552200728, 0)c(NA, 9.7066719709656, 1.66786030273759, 15.1260172451856, 13.9445628108922, 0.975650924995523, 2.26175214669267, 3.45046310865463, 0) [20250519_010419.]: Logging df_agg: CpG#2 [20250519_010419.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010419.]: c(6.85513576175294, 13.7133339963707, 24.5830349243156, 31.8277435330554, 43.0277185945539, 63.1097818281222, 73.3036858899805, 84.4808447799272, 100)[20250519_010419.]: c(6.85513576175294, 1.2133339963707, 0.416965075684399, 5.6722564669446, 6.9722814054461, 0.609781828122202, 1.6963141100195, 3.0191552200728, 0)[20250519_010419.]: c(NA, 9.7066719709656, 1.66786030273759, 15.1260172451856, 13.9445628108922, 0.975650924995523, 2.26175214669267, 3.45046310865463, 0) [20250519_010419.]: Entered 'hyperbolic_regression'-Function [20250519_010419.]: 'hyperbolic_regression': minmax = FALSE [20250519_010419.]: Entered 'cubic_regression'-Function [20250519_010419.]: 'cubic_regression': minmax = FALSE [20250519_010419.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.27473119209009, 12.7004989900866, 24.2988465005425, 33.7074476249853, 47.0168800224423, 61.5718102366584, 76.091389954119, 79.6023799465703, 100)c(5.27473119209009, 0.200498990086601, 0.701153499457501, 3.7925523750147, 2.9831199775577, 0.928189763341599, 1.09138995411899, 7.8976200534297, 0)c(NA, 1.60399192069281, 2.80461399783, 10.1134730000392, 5.9662399551154, 1.48510362134656, 1.45518660549199, 9.02585148963395, 0) [20250519_010419.]: Logging df_agg: CpG#3 [20250519_010419.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010419.]: c(5.27473119209009, 12.7004989900866, 24.2988465005425, 33.7074476249853, 47.0168800224423, 61.5718102366584, 76.091389954119, 79.6023799465703, 100)[20250519_010419.]: c(5.27473119209009, 0.200498990086601, 0.701153499457501, 3.7925523750147, 2.9831199775577, 0.928189763341599, 1.09138995411899, 7.8976200534297, 0)[20250519_010419.]: c(NA, 1.60399192069281, 2.80461399783, 10.1134730000392, 5.9662399551154, 1.48510362134656, 1.45518660549199, 9.02585148963395, 0) [20250519_010419.]: Entered 'hyperbolic_regression'-Function [20250519_010419.]: 'hyperbolic_regression': minmax = FALSE [20250519_010419.]: Entered 'cubic_regression'-Function [20250519_010419.]: 'cubic_regression': minmax = FALSE [20250519_010419.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.07584235183922, 14.2021272601476, 23.203637284743, 34.7960863609446, 44.4638162018573, 64.2755507391393, 71.7670214785718, 82.5599568822802, 100)c(5.07584235183922, 1.7021272601476, 1.796362715257, 2.7039136390554, 5.5361837981427, 1.77555073913931, 3.23297852142819, 4.9400431177198, 0)c(NA, 13.6170180811808, 7.185450861028, 7.2104363708144, 11.0723675962854, 2.84088118262289, 4.31063802857093, 5.64576356310834, 0) [20250519_010419.]: Logging df_agg: CpG#4 [20250519_010419.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010419.]: c(5.07584235183922, 14.2021272601476, 23.203637284743, 34.7960863609446, 44.4638162018573, 64.2755507391393, 71.7670214785718, 82.5599568822802, 100)[20250519_010419.]: c(5.07584235183922, 1.7021272601476, 1.796362715257, 2.7039136390554, 5.5361837981427, 1.77555073913931, 3.23297852142819, 4.9400431177198, 0)[20250519_010419.]: c(NA, 13.6170180811808, 7.185450861028, 7.2104363708144, 11.0723675962854, 2.84088118262289, 4.31063802857093, 5.64576356310834, 0) [20250519_010419.]: Entered 'hyperbolic_regression'-Function [20250519_010419.]: 'hyperbolic_regression': minmax = FALSE [20250519_010419.]: Entered 'cubic_regression'-Function [20250519_010419.]: 'cubic_regression': minmax = FALSE [20250519_010420.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.96411772281142, 12.4462106087166, 22.9738427992134, 34.1311793989123, 46.7456955019114, 58.6021949543013, 74.5100864344263, 86.8549692943716, 100)c(6.96411772281142, 0.0537893912834004, 2.0261572007866, 3.3688206010877, 3.2543044980886, 3.8978050456987, 0.489913565573701, 0.6450307056284, 0)c(NA, 0.430315130267203, 8.1046288031464, 8.98352160290053, 6.5086089961772, 6.23648807311793, 0.653218087431602, 0.7371779492896, 0) [20250519_010420.]: Logging df_agg: CpG#5 [20250519_010420.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010420.]: c(6.96411772281142, 12.4462106087166, 22.9738427992134, 34.1311793989123, 46.7456955019114, 58.6021949543013, 74.5100864344263, 86.8549692943716, 100)[20250519_010420.]: c(6.96411772281142, 0.0537893912834004, 2.0261572007866, 3.3688206010877, 3.2543044980886, 3.8978050456987, 0.489913565573701, 0.6450307056284, 0)[20250519_010420.]: c(NA, 0.430315130267203, 8.1046288031464, 8.98352160290053, 6.5086089961772, 6.23648807311793, 0.653218087431602, 0.7371779492896, 0) [20250519_010420.]: Entered 'hyperbolic_regression'-Function [20250519_010420.]: 'hyperbolic_regression': minmax = FALSE [20250519_010420.]: Entered 'cubic_regression'-Function [20250519_010420.]: 'cubic_regression': minmax = FALSE [20250519_010420.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.82248549455073, 14.2907258003629, 24.7343949718638, 31.7110486196861, 44.7745870553137, 60.1336283719223, 70.3292384995337, 88.2655480529845, 100)c(6.82248549455073, 1.7907258003629, 0.2656050281362, 5.7889513803139, 5.2254129446863, 2.3663716280777, 4.6707615004663, 0.765548052984499, 0)c(NA, 14.3258064029032, 1.0624201125448, 15.4372036808371, 10.4508258893726, 3.78619460492432, 6.22768200062173, 0.874912060553714, 0) [20250519_010420.]: Logging df_agg: CpG#6 [20250519_010420.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010420.]: c(6.82248549455073, 14.2907258003629, 24.7343949718638, 31.7110486196861, 44.7745870553137, 60.1336283719223, 70.3292384995337, 88.2655480529845, 100)[20250519_010420.]: c(6.82248549455073, 1.7907258003629, 0.2656050281362, 5.7889513803139, 5.2254129446863, 2.3663716280777, 4.6707615004663, 0.765548052984499, 0)[20250519_010420.]: c(NA, 14.3258064029032, 1.0624201125448, 15.4372036808371, 10.4508258893726, 3.78619460492432, 6.22768200062173, 0.874912060553714, 0) [20250519_010420.]: Entered 'hyperbolic_regression'-Function [20250519_010420.]: 'hyperbolic_regression': minmax = FALSE [20250519_010420.]: Entered 'cubic_regression'-Function [20250519_010420.]: 'cubic_regression': minmax = FALSE [20250519_010420.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)c(6.75841824784972, 13.6258602138932, 23.2672219789518, 33.4287658578254, 47.5236851621158, 60.5558050598499, 72.3896034879297, 77.2628610417933)c(6.75841824784972, 1.1258602138932, 1.7327780210482, 4.0712341421746, 2.4763148378842, 1.9441949401501, 2.6103965120703, 10.2371389582067)c(NA, 9.0068817111456, 6.9311120841928, 10.8566243791323, 4.95262967576841, 3.11071190424016, 3.48052868276039, 11.6995873808077) [20250519_010420.]: Logging df_agg: CpG#7 [20250519_010420.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)[20250519_010420.]: c(6.75841824784972, 13.6258602138932, 23.2672219789518, 33.4287658578254, 47.5236851621158, 60.5558050598499, 72.3896034879297, 77.2628610417933)[20250519_010420.]: c(6.75841824784972, 1.1258602138932, 1.7327780210482, 4.0712341421746, 2.4763148378842, 1.9441949401501, 2.6103965120703, 10.2371389582067)[20250519_010420.]: c(NA, 9.0068817111456, 6.9311120841928, 10.8566243791323, 4.95262967576841, 3.11071190424016, 3.48052868276039, 11.6995873808077) [20250519_010420.]: Entered 'hyperbolic_regression'-Function [20250519_010420.]: 'hyperbolic_regression': minmax = FALSE [20250519_010420.]: Entered 'cubic_regression'-Function [20250519_010420.]: 'cubic_regression': minmax = FALSE [20250519_010420.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.74253131510683, 10.9055216254216, 24.5329419436721, 32.1516426249373, 49.0919870496865, 62.3843810658735, 77.6235905386846, 80.9968312605936, 100)c(5.74253131510683, 1.5944783745784, 0.4670580563279, 5.3483573750627, 0.908012950313498, 0.115618934126502, 2.6235905386846, 6.50316873940641, 0)c(NA, 12.7558269966272, 1.8682322253116, 14.2622863335005, 1.816025900627, 0.184990294602403, 3.49812071824613, 7.43219284503589, 0) [20250519_010420.]: Logging df_agg: CpG#8 [20250519_010420.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010420.]: c(5.74253131510683, 10.9055216254216, 24.5329419436721, 32.1516426249373, 49.0919870496865, 62.3843810658735, 77.6235905386846, 80.9968312605936, 100)[20250519_010420.]: c(5.74253131510683, 1.5944783745784, 0.4670580563279, 5.3483573750627, 0.908012950313498, 0.115618934126502, 2.6235905386846, 6.50316873940641, 0)[20250519_010420.]: c(NA, 12.7558269966272, 1.8682322253116, 14.2622863335005, 1.816025900627, 0.184990294602403, 3.49812071824613, 7.43219284503589, 0) [20250519_010420.]: Entered 'hyperbolic_regression'-Function [20250519_010420.]: 'hyperbolic_regression': minmax = FALSE [20250519_010420.]: Entered 'cubic_regression'-Function [20250519_010420.]: 'cubic_regression': minmax = FALSE [20250519_010420.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)c(8.66981641228949, 14.9680920996829, 24.6496838227806, 31.5444702961411, 44.0944303975514, 57.2478940106457, 69.1518417879612, 83.8189177560508)c(8.66981641228949, 2.4680920996829, 0.350316177219401, 5.9555297038589, 5.9055696024486, 5.2521059893543, 5.8481582120388, 3.68108224394921)c(NA, 19.7447367974632, 1.4012647088776, 15.8814125436237, 11.8111392048972, 8.40336958296688, 7.7975442827184, 4.20695113594195) [20250519_010420.]: Logging df_agg: CpG#9 [20250519_010420.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)[20250519_010420.]: c(8.66981641228949, 14.9680920996829, 24.6496838227806, 31.5444702961411, 44.0944303975514, 57.2478940106457, 69.1518417879612, 83.8189177560508)[20250519_010420.]: c(8.66981641228949, 2.4680920996829, 0.350316177219401, 5.9555297038589, 5.9055696024486, 5.2521059893543, 5.8481582120388, 3.68108224394921)[20250519_010420.]: c(NA, 19.7447367974632, 1.4012647088776, 15.8814125436237, 11.8111392048972, 8.40336958296688, 7.7975442827184, 4.20695113594195) [20250519_010420.]: Entered 'hyperbolic_regression'-Function [20250519_010420.]: 'hyperbolic_regression': minmax = FALSE [20250519_010420.]: Entered 'cubic_regression'-Function [20250519_010420.]: 'cubic_regression': minmax = FALSE [20250519_010420.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)c(6.47354965864506, 13.5375540918194, 23.976024251167, 32.9614416466039, 45.8185453601259, 60.9295837234236, 72.9250251350637, 82.8721280175692)c(6.47354965864506, 1.0375540918194, 1.023975748833, 4.5385583533961, 4.1814546398741, 1.5704162765764, 2.07497486493629, 4.6278719824308)c(NA, 8.3004327345552, 4.095902995332, 12.1028222757229, 8.36290927974819, 2.51266604252224, 2.76663315324839, 5.28899655134949) [20250519_010420.]: Logging df_agg: row_means [20250519_010420.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)[20250519_010420.]: c(6.47354965864506, 13.5375540918194, 23.976024251167, 32.9614416466039, 45.8185453601259, 60.9295837234236, 72.9250251350637, 82.8721280175692)[20250519_010420.]: c(6.47354965864506, 1.0375540918194, 1.023975748833, 4.5385583533961, 4.1814546398741, 1.5704162765764, 2.07497486493629, 4.6278719824308)[20250519_010420.]: c(NA, 8.3004327345552, 4.095902995332, 12.1028222757229, 8.36290927974819, 2.51266604252224, 2.76663315324839, 5.28899655134949) [20250519_010420.]: Entered 'hyperbolic_regression'-Function [20250519_010420.]: 'hyperbolic_regression': minmax = FALSE [20250519_010420.]: Entered 'cubic_regression'-Function [20250519_010420.]: 'cubic_regression': minmax = FALSE [20250519_010420.]: ### Starting with plotting ### [20250519_010420.]: Creating BiasCorrected (hyperbolic) plot No. 1 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG1_corrected_h.png [20250519_010420.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = 170413234.729972 , a = 44157086.707734 , b = 170413234.729972 , d = 49999790.9017601 [20250519_010420.]: # CpG-site: CpG#1 Cubic: Using a = -8.02902858014277e-05 , b = 0.0119973831380665 , c = 0.410459267079284 , d = 6.65096545514267 [20250519_010422.]: Creating BiasCorrected (hyperbolic) plot No. 2 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG2_corrected_h.png [20250519_010422.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = 63519198.3583519 , a = 42702123.5062977 , b = 63519198.3583519 , d = 44879882.4102916 [20250519_010422.]: # CpG-site: CpG#2 Cubic: Using a = -4.57917988885898e-05 , b = 0.00978975326683099 , c = 0.400365566888902 , d = 7.17765330717532 [20250519_010424.]: Creating BiasCorrected (hyperbolic) plot No. 3 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG3_corrected_h.png [20250519_010424.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = 60249784.2121432 , a = 37717764.1245422 , b = 60249784.2121432 , d = 39783429.2823954 [20250519_010424.]: # CpG-site: CpG#3 Cubic: Using a = -2.8548625146914e-05 , b = 0.00587956303749884 , c = 0.626869802626483 , d = 4.78057876315863 [20250519_010426.]: Creating BiasCorrected (hyperbolic) plot No. 4 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG4_corrected_h.png [20250519_010426.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = 66787089.0359508 , a = 42175722.8129271 , b = 66787089.0359508 , d = 44465523.7860812 [20250519_010426.]: # CpG-site: CpG#4 Cubic: Using a = -1.99135996570308e-05 , b = 0.00496936895290719 , c = 0.637632604471187 , d = 5.04633670060351 [20250519_010428.]: Creating BiasCorrected (hyperbolic) plot No. 5 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG5_corrected_h.png [20250519_010428.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = 57501729.1236441 , a = 52618093.853152 , b = 57501729.1236441 , d = 54589535.3828564 [20250519_010428.]: # CpG-site: CpG#5 Cubic: Using a = -4.67907948499236e-05 , b = 0.00978122250124343 , c = 0.42296364991001 , d = 6.61748738798833 [20250519_010429.]: Creating BiasCorrected (hyperbolic) plot No. 6 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG6_corrected_h.png [20250519_010429.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = 62067256.5784193 , a = 42512767.1502548 , b = 62067256.5784193 , d = 44640742.1604705 [20250519_010429.]: # CpG-site: CpG#6 Cubic: Using a = -2.66665530276184e-05 , b = 0.00740474965950072 , c = 0.461019694351388 , d = 7.23067904029629 [20250519_010431.]: Creating BiasCorrected (hyperbolic) plot No. 7 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG7_corrected_h.png [20250519_010431.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = 162158036.04427 , a = 37148305.7394653 , b = 162158036.04427 , d = 42707972.2066412 [20250519_010431.]: # CpG-site: CpG#7 Cubic: Using a = -0.000133937721266082 , b = 0.0182624909642956 , c = 0.234528921247686 , d = 7.29090555727988 [20250519_010433.]: Creating BiasCorrected (hyperbolic) plot No. 8 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG8_corrected_h.png [20250519_010433.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = 54899408.7962443 , a = 51832403.6152701 , b = 54899408.7962443 , d = 53714624.7099072 [20250519_010433.]: # CpG-site: CpG#8 Cubic: Using a = -5.74524696829791e-05 , b = 0.00998862677056794 , c = 0.502915955578275 , d = 4.90625922077111 [20250519_010435.]: Creating BiasCorrected (hyperbolic) plot No. 9 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG9_corrected_h.png [20250519_010435.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = 170227220.836014 , a = 37110575.5394583 , b = 170227220.836014 , d = 42946902.7624502 [20250519_010435.]: # CpG-site: CpG#9 Cubic: Using a = -1.38503636743099e-05 , b = 0.00591895875024497 , c = 0.445742549227169 , d = 8.73330013353405 [20250519_010437.]: Creating BiasCorrected (hyperbolic) plot No. 10 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_rowmeans_corrected_h.png [20250519_010437.]: # CpG-site: row_means Hyperbolic: Using bias_weight = 131846991.813373 , a = 45678337.0783695 , b = 131846991.813373 , d = 50198768.4229237 [20250519_010437.]: # CpG-site: row_means Cubic: Using a = -7.67916401132796e-05 , b = 0.0124890194887903 , c = 0.369098504101218 , d = 6.83740913189534 [20250519_010438.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG1_corrected_h.png [20250519_010440.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG2_corrected_h.png [20250519_010442.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG3_corrected_h.png [20250519_010444.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG4_corrected_h.png [20250519_010445.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG5_corrected_h.png [20250519_010447.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG6_corrected_h.png [20250519_010449.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG7_corrected_h.png [20250519_010451.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG8_corrected_h.png [20250519_010452.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG9_corrected_h.png [20250519_010454.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_rowmeans_corrected_h.png [20250519_010456.]: Entered 'solving_equations'-Function [20250519_010456.]: Solving cubic regression for CpG#1 Coefficients: -1.03617340067344Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_010456.]: Samplename: 0 Root: 1.334 --> Root in between the borders! Added to results. Coefficients: -8.34150673400678Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_010456.]: Samplename: 12.5 Root: 11.446 --> Root in between the borders! Added to results. Coefficients: -15.3881734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_010456.]: Samplename: 25 Root: 22.228 --> Root in between the borders! Added to results. Coefficients: -24.2801734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_010456.]: Samplename: 37.5 Root: 36.374 --> Root in between the borders! Added to results. Coefficients: -34.9006734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_010456.]: Samplename: 50 Root: 52.044 --> Root in between the borders! Added to results. Coefficients: -46.3541734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_010456.]: Samplename: 62.5 Root: 66.144 --> Root in between the borders! Added to results. Coefficients: -55.8931734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_010456.]: Samplename: 75 Root: 75.864 --> Root in between the borders! Added to results. Coefficients: -63.0981734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_010456.]: Samplename: 87.5 Root: 82.254 --> Root in between the borders! Added to results. Coefficients: -91.0461734006735Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_010456.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.877 --> '100 < root < 110' --> substitute 100 [20250519_010456.]: Solving cubic regression for CpG#2 Coefficients: -0.283329966329966Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_010456.]: Samplename: 0 Root: 0.549 --> Root in between the borders! Added to results. Coefficients: -6.33999663299663Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_010456.]: Samplename: 12.5 Root: 11.533 --> Root in between the borders! Added to results. Coefficients: -15.93932996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_010456.]: Samplename: 25 Root: 26.628 --> Root in between the borders! Added to results. Coefficients: -22.33732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_010456.]: Samplename: 37.5 Root: 35.509 --> Root in between the borders! Added to results. Coefficients: -32.22832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_010456.]: Samplename: 50 Root: 47.851 --> Root in between the borders! Added to results. Coefficients: -49.96332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_010456.]: Samplename: 62.5 Root: 66.893 --> Root in between the borders! Added to results. Coefficients: -58.96582996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_010456.]: Samplename: 75 Root: 75.431 --> Root in between the borders! Added to results. Coefficients: -68.8366632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_010456.]: Samplename: 87.5 Root: 84.118 --> Root in between the borders! Added to results. Coefficients: -90.57732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_010456.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.287 --> '100 < root < 110' --> substitute 100 [20250519_010456.]: Solving cubic regression for CpG#3 Coefficients: -0.90294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_010456.]: Samplename: 0 Root: 1.441 --> Root in between the borders! Added to results. Coefficients: -6.57294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_010456.]: Samplename: 12.5 Root: 10.568 --> Root in between the borders! Added to results. Coefficients: -15.4289478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_010456.]: Samplename: 25 Root: 24.796 --> Root in between the borders! Added to results. Coefficients: -22.6129478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_010456.]: Samplename: 37.5 Root: 35.952 --> Root in between the borders! Added to results. Coefficients: -32.7754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_010456.]: Samplename: 50 Root: 50.684 --> Root in between the borders! Added to results. Coefficients: -43.8889478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_010456.]: Samplename: 62.5 Root: 65.142 --> Root in between the borders! Added to results. Coefficients: -54.9754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_010456.]: Samplename: 75 Root: 77.905 --> Root in between the borders! Added to results. Coefficients: -57.6562811447812Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_010456.]: Samplename: 87.5 Root: 80.767 --> Root in between the borders! Added to results. Coefficients: -80.6649478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_010456.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.38 --> '100 < root < 110' --> substitute 100 [20250519_010456.]: Solving cubic regression for CpG#4 Coefficients: -0.597449494949524Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_010456.]: Samplename: 0 Root: 0.858 --> Root in between the borders! Added to results. Coefficients: -8.25278282828286Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_010456.]: Samplename: 12.5 Root: 12.086 --> Root in between the borders! Added to results. Coefficients: -15.8034494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_010456.]: Samplename: 25 Root: 23.316 --> Root in between the borders! Added to results. Coefficients: -25.5274494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_010456.]: Samplename: 37.5 Root: 37.383 --> Root in between the borders! Added to results. Coefficients: -33.6369494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_010456.]: Samplename: 50 Root: 48.353 --> Root in between the borders! Added to results. Coefficients: -50.2554494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_010456.]: Samplename: 62.5 Root: 68.082 --> Root in between the borders! Added to results. Coefficients: -56.5394494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_010456.]: Samplename: 75 Root: 74.615 --> Root in between the borders! Added to results. Coefficients: -65.5927828282829Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_010456.]: Samplename: 87.5 Root: 83.254 --> Root in between the borders! Added to results. Coefficients: -88.3214494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_010456.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.715 --> '100 < root < 110' --> substitute 100 [20250519_010456.]: Solving cubic regression for CpG#5 Coefficients: -0.623961279461278Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_010456.]: Samplename: 0 Root: 1.458 --> Root in between the borders! Added to results. Coefficients: -4.76796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_010456.]: Samplename: 12.5 Root: 10.347 --> Root in between the borders! Added to results. Coefficients: -12.7259612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_010456.]: Samplename: 25 Root: 24.815 --> Root in between the borders! Added to results. Coefficients: -21.1599612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_010456.]: Samplename: 37.5 Root: 37.902 --> Root in between the borders! Added to results. Coefficients: -30.6954612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_010456.]: Samplename: 50 Root: 50.977 --> Root in between the borders! Added to results. Coefficients: -39.6579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_010456.]: Samplename: 62.5 Root: 62.126 --> Root in between the borders! Added to results. Coefficients: -51.6829612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_010456.]: Samplename: 75 Root: 75.852 --> Root in between the borders! Added to results. Coefficients: -61.0146279461279Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_010456.]: Samplename: 87.5 Root: 85.767 --> Root in between the borders! Added to results. Coefficients: -76.0699612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_010456.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.743 --> '100 < root < 110' --> substitute 100 [20250519_010456.]: Solving cubic regression for CpG#6 Coefficients: -0.196072390572403Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_010456.]: Samplename: 0 Root: 0.349 --> Root in between the borders! Added to results. Coefficients: -6.73873905723907Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_010456.]: Samplename: 12.5 Root: 11.718 --> Root in between the borders! Added to results. Coefficients: -15.8880723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_010456.]: Samplename: 25 Root: 26.396 --> Root in between the borders! Added to results. Coefficients: -22.0000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_010456.]: Samplename: 37.5 Root: 35.301 --> Root in between the borders! Added to results. Coefficients: -33.4445723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_010456.]: Samplename: 50 Root: 50.134 --> Root in between the borders! Added to results. Coefficients: -46.9000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_010456.]: Samplename: 62.5 Root: 64.993 --> Root in between the borders! Added to results. Coefficients: -55.8320723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_010456.]: Samplename: 75 Root: 73.639 --> Root in between the borders! Added to results. Coefficients: -71.5454057239057Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_010456.]: Samplename: 87.5 Root: 87.043 --> Root in between the borders! Added to results. Coefficients: -89.6560723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_010456.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.329 --> '100 < root < 110' --> substitute 100 [20250519_010456.]: Solving cubic regression for CpG#7 Coefficients: -1.21495454545456Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_010456.]: Samplename: 0 Root: 2.13 --> Root in between the borders! Added to results. Coefficients: -5.39562121212123Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_010456.]: Samplename: 12.5 Root: 9.973 --> Root in between the borders! Added to results. Coefficients: -11.2649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_010456.]: Samplename: 25 Root: 22.206 --> Root in between the borders! Added to results. Coefficients: -17.4509545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_010456.]: Samplename: 37.5 Root: 35.814 --> Root in between the borders! Added to results. Coefficients: -26.0314545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_010456.]: Samplename: 50 Root: 53.28 --> Root in between the borders! Added to results. Coefficients: -33.9649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_010456.]: Samplename: 62.5 Root: 66.598 --> Root in between the borders! Added to results. Coefficients: -41.1689545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_010456.]: Samplename: 75 Root: 76.575 --> Root in between the borders! Added to results. Coefficients: -44.1356212121212Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_010456.]: Samplename: 87.5 Root: 80.219 --> Root in between the borders! Added to results. Coefficients: -67.2229545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_010456.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.506 --> '100 < root < 110' --> substitute 100 [20250519_010456.]: Solving cubic regression for CpG#8 Coefficients: -1.09618518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_010456.]: Samplename: 0 Root: 2.016 --> Root in between the borders! Added to results. Coefficients: -5.44685185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_010456.]: Samplename: 12.5 Root: 9.458 --> Root in between the borders! Added to results. Coefficients: -16.9301851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_010456.]: Samplename: 25 Root: 26.35 --> Root in between the borders! Added to results. Coefficients: -23.3501851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_010456.]: Samplename: 37.5 Root: 34.728 --> Root in between the borders! Added to results. Coefficients: -37.6251851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_010456.]: Samplename: 50 Root: 51.781 --> Root in between the borders! Added to results. Coefficients: -48.8261851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_010456.]: Samplename: 62.5 Root: 64.192 --> Root in between the borders! Added to results. Coefficients: -61.6676851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_010456.]: Samplename: 75 Root: 77.804 --> Root in between the borders! Added to results. Coefficients: -64.5101851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_010456.]: Samplename: 87.5 Root: 80.758 --> Root in between the borders! Added to results. Coefficients: -86.0601851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_010456.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.834 --> '100 < root < 110' --> substitute 100 [20250519_010456.]: Solving cubic regression for CpG#9 Coefficients: -0.989865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_010456.]: Samplename: 0 Root: 1.475 --> Root in between the borders! Added to results. Coefficients: -6.39586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_010456.]: Samplename: 12.5 Root: 10.12 --> Root in between the borders! Added to results. Coefficients: -14.7058653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_010456.]: Samplename: 25 Root: 24.844 --> Root in between the borders! Added to results. Coefficients: -20.6238653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_010456.]: Samplename: 37.5 Root: 35.327 --> Root in between the borders! Added to results. Coefficients: -31.3958653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_010456.]: Samplename: 50 Root: 51.855 --> Root in between the borders! Added to results. Coefficients: -42.6858653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_010456.]: Samplename: 62.5 Root: 65.265 --> Root in between the borders! Added to results. Coefficients: -52.9033653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_010456.]: Samplename: 75 Root: 74.915 --> Root in between the borders! Added to results. Coefficients: -65.492531986532Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_010456.]: Samplename: 87.5 Root: 84.67 --> Root in between the borders! Added to results. Coefficients: -92.9898653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_010456.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.082 --> '100 < root < 110' --> substitute 100 [20250519_010456.]: Solving cubic regression for row_means Coefficients: -0.771215488215478Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_010456.]: Samplename: 0 Root: 1.287 --> Root in between the borders! Added to results. Coefficients: -6.47247474747474Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_010456.]: Samplename: 12.5 Root: 10.847 --> Root in between the borders! Added to results. Coefficients: -14.8972154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_010456.]: Samplename: 25 Root: 24.737 --> Root in between the borders! Added to results. Coefficients: -22.1492154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_010456.]: Samplename: 37.5 Root: 36.02 --> Root in between the borders! Added to results. Coefficients: -32.5259932659933Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_010456.]: Samplename: 50 Root: 50.639 --> Root in between the borders! Added to results. Coefficients: -44.7218821548821Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_010456.]: Samplename: 62.5 Root: 65.497 --> Root in between the borders! Added to results. Coefficients: -54.4032154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_010456.]: Samplename: 75 Root: 75.751 --> Root in between the borders! Added to results. Coefficients: -62.4313636363636Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_010456.]: Samplename: 87.5 Root: 83.403 --> Root in between the borders! Added to results. Coefficients: -84.7343265993266Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 [20250519_010456.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.573 --> '100 < root < 110' --> substitute 100 [20250519_010456.]: ### Starting with regression calculations ### [20250519_010456.]: Entered 'regression_type1'-Function [20250519_010456.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) [20250519_010456.]: Logging df_agg: CpG#1 [20250519_010456.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010456.]: c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)[20250519_010456.]: c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)[20250519_010456.]: c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) [20250519_010456.]: Entered 'hyperbolic_regression'-Function [20250519_010456.]: 'hyperbolic_regression': minmax = FALSE [20250519_010456.]: Entered 'cubic_regression'-Function [20250519_010456.]: 'cubic_regression': minmax = FALSE [20250519_010456.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) [20250519_010456.]: Logging df_agg: CpG#2 [20250519_010456.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010456.]: c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)[20250519_010456.]: c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)[20250519_010456.]: c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) [20250519_010456.]: Entered 'hyperbolic_regression'-Function [20250519_010456.]: 'hyperbolic_regression': minmax = FALSE [20250519_010456.]: Entered 'cubic_regression'-Function [20250519_010456.]: 'cubic_regression': minmax = FALSE [20250519_010456.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) [20250519_010456.]: Logging df_agg: CpG#3 [20250519_010456.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010456.]: c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)[20250519_010456.]: c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)[20250519_010456.]: c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) [20250519_010456.]: Entered 'hyperbolic_regression'-Function [20250519_010456.]: 'hyperbolic_regression': minmax = FALSE [20250519_010456.]: Entered 'cubic_regression'-Function [20250519_010456.]: 'cubic_regression': minmax = FALSE [20250519_010456.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) [20250519_010456.]: Logging df_agg: CpG#4 [20250519_010456.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010456.]: c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)[20250519_010456.]: c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)[20250519_010456.]: c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) [20250519_010456.]: Entered 'hyperbolic_regression'-Function [20250519_010456.]: 'hyperbolic_regression': minmax = FALSE [20250519_010456.]: Entered 'cubic_regression'-Function [20250519_010456.]: 'cubic_regression': minmax = FALSE [20250519_010456.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) [20250519_010457.]: Logging df_agg: CpG#5 [20250519_010457.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010457.]: c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)[20250519_010457.]: c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)[20250519_010457.]: c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) [20250519_010457.]: Entered 'hyperbolic_regression'-Function [20250519_010457.]: 'hyperbolic_regression': minmax = FALSE [20250519_010457.]: Entered 'cubic_regression'-Function [20250519_010457.]: 'cubic_regression': minmax = FALSE [20250519_010457.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) [20250519_010457.]: Logging df_agg: CpG#6 [20250519_010457.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010457.]: c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)[20250519_010457.]: c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)[20250519_010457.]: c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) [20250519_010457.]: Entered 'hyperbolic_regression'-Function [20250519_010457.]: 'hyperbolic_regression': minmax = FALSE [20250519_010457.]: Entered 'cubic_regression'-Function [20250519_010457.]: 'cubic_regression': minmax = FALSE [20250519_010457.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) [20250519_010457.]: Logging df_agg: CpG#7 [20250519_010457.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010457.]: c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)[20250519_010457.]: c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)[20250519_010457.]: c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) [20250519_010457.]: Entered 'hyperbolic_regression'-Function [20250519_010457.]: 'hyperbolic_regression': minmax = FALSE [20250519_010457.]: Entered 'cubic_regression'-Function [20250519_010457.]: 'cubic_regression': minmax = FALSE [20250519_010457.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) [20250519_010457.]: Logging df_agg: CpG#8 [20250519_010457.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010457.]: c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)[20250519_010457.]: c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)[20250519_010457.]: c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) [20250519_010457.]: Entered 'hyperbolic_regression'-Function [20250519_010457.]: 'hyperbolic_regression': minmax = FALSE [20250519_010457.]: Entered 'cubic_regression'-Function [20250519_010457.]: 'cubic_regression': minmax = FALSE [20250519_010457.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) [20250519_010457.]: Logging df_agg: CpG#9 [20250519_010457.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010457.]: c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)[20250519_010457.]: c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)[20250519_010457.]: c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) [20250519_010457.]: Entered 'hyperbolic_regression'-Function [20250519_010457.]: 'hyperbolic_regression': minmax = FALSE [20250519_010457.]: Entered 'cubic_regression'-Function [20250519_010457.]: 'cubic_regression': minmax = FALSE [20250519_010457.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) [20250519_010457.]: Logging df_agg: row_means [20250519_010457.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010457.]: c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)[20250519_010457.]: c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)[20250519_010457.]: c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) [20250519_010457.]: Entered 'hyperbolic_regression'-Function [20250519_010457.]: 'hyperbolic_regression': minmax = FALSE [20250519_010457.]: Entered 'cubic_regression'-Function [20250519_010457.]: 'cubic_regression': minmax = FALSE [20250519_010457.]: ### Starting with plotting ### [20250519_010457.]: Creating BiasCorrected (cubic) plot No. 1 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG1_corrected_c.png [20250519_010457.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -1164.7742156272 , a = 2039.5018241568 , b = -1164.7742156272 , d = 1955.83895682798 [20250519_010457.]: # CpG-site: CpG#1 Cubic: Using a = -3.42821196240366e-05 , b = 0.00462173155013798 , c = 0.850326258865826 , d = 0.500073874211125 [20250519_010459.]: Creating BiasCorrected (cubic) plot No. 2 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG2_corrected_c.png [20250519_010459.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = -464.155645361511 , a = 4988.81163391504 , b = -464.155645361511 , d = 4925.28408403839 [20250519_010459.]: # CpG-site: CpG#2 Cubic: Using a = -1.40811796906314e-05 , b = 0.00190849431754485 , c = 0.933455870941473 , d = 0.363096210099674 [20250519_010501.]: Creating BiasCorrected (cubic) plot No. 3 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG3_corrected_c.png [20250519_010501.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = -745.602352161431 , a = 2026.54103071808 , b = -745.602352161431 , d = 1953.2909386646 [20250519_010501.]: # CpG-site: CpG#3 Cubic: Using a = -2.78835051857186e-05 , b = 0.00366863707889089 , c = 0.880665171220893 , d = 0.510779308833653 [20250519_010503.]: Creating BiasCorrected (cubic) plot No. 4 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG4_corrected_c.png [20250519_010503.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = -833.06710731958 , a = 2871.12609903594 , b = -833.06710731958 , d = 2796.80085807845 [20250519_010503.]: # CpG-site: CpG#4 Cubic: Using a = -2.30028865643e-05 , b = 0.00309017851940692 , c = 0.897073107777645 , d = 0.443455761841866 [20250519_010505.]: Creating BiasCorrected (cubic) plot No. 5 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG5_corrected_c.png [20250519_010505.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = 221.879400146024 , a = 12476.5710927987 , b = 221.879400146024 , d = 12431.2329555778 [20250519_010505.]: # CpG-site: CpG#5 Cubic: Using a = -1.28632696989134e-05 , b = 0.00184822285299014 , c = 0.931001046631176 , d = 0.437963095185599 [20250519_010506.]: Creating BiasCorrected (cubic) plot No. 6 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG6_corrected_c.png [20250519_010506.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = -700.537394936109 , a = 21298.052707184 , b = -700.537394936109 , d = 21233.4563350159 [20250519_010506.]: # CpG-site: CpG#6 Cubic: Using a = -5.838335151375e-06 , b = 0.000828447163218526 , c = 0.970047764475166 , d = 0.158074701412251 [20250519_010508.]: Creating BiasCorrected (cubic) plot No. 7 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG7_corrected_c.png [20250519_010508.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -1144.91075475684 , a = 1477.92088633595 , b = -1144.91075475684 , d = 1394.70940380214 [20250519_010508.]: # CpG-site: CpG#7 Cubic: Using a = -4.54220535614047e-05 , b = 0.00607993130565131 , c = 0.805411493452389 , d = 0.615125382790254 [20250519_010510.]: Creating BiasCorrected (cubic) plot No. 8 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG8_corrected_c.png [20250519_010510.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = -349.322022819744 , a = 2377.42555416743 , b = -349.322022819744 , d = 2315.25402084122 [20250519_010510.]: # CpG-site: CpG#8 Cubic: Using a = -2.64622306669639e-05 , b = 0.00353621593528015 , c = 0.877916844709074 , d = 0.698684606493051 [20250519_010512.]: Creating BiasCorrected (cubic) plot No. 9 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG9_corrected_c.png [20250519_010512.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -1536.91807795025 , a = 3745.12601816225 , b = -1536.91807795025 , d = 3656.21766932252 [20250519_010512.]: # CpG-site: CpG#9 Cubic: Using a = -2.70976130611725e-05 , b = 0.00378478247182342 , c = 0.871825263051793 , d = 0.455475740974113 [20250519_010514.]: Creating BiasCorrected (cubic) plot No. 10 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_rowmeans_corrected_c.png [20250519_010514.]: # CpG-site: row_means Hyperbolic: Using bias_weight = -820.655299479511 , a = 3198.47118291101 , b = -820.655299479511 , d = 3124.21569118411 [20250519_010514.]: # CpG-site: row_means Cubic: Using a = -2.34328362100009e-05 , b = 0.00319139073176266 , c = 0.891842168032129 , d = 0.493517689787135 [20250519_010515.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG1_corrected_c.png [20250519_010517.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG2_corrected_c.png [20250519_010519.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG3_corrected_c.png [20250519_010521.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG4_corrected_c.png [20250519_010522.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG5_corrected_c.png [20250519_010524.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG6_corrected_c.png [20250519_010526.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG7_corrected_c.png [20250519_010528.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG8_corrected_c.png [20250519_010529.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG9_corrected_c.png [20250519_010531.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_rowmeans_corrected_c.png [20250519_010533.]: Entered 'solving_equations'-Function [20250519_010533.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 77.9602582684049 [20250519_010533.]: Samplename: Sample#1 Root: 77.96 --> Root in between the borders! Added to results. Hyperbolic solved: 27.1420961794087 [20250519_010533.]: Samplename: Sample#10 Root: 27.142 --> Root in between the borders! Added to results. Hyperbolic solved: 36.7640366116659 [20250519_010533.]: Samplename: Sample#2 Root: 36.764 --> Root in between the borders! Added to results. Hyperbolic solved: 51.6231208412737 [20250519_010533.]: Samplename: Sample#3 Root: 51.623 --> Root in between the borders! Added to results. Hyperbolic solved: 12.873561614208 [20250519_010533.]: Samplename: Sample#4 Root: 12.874 --> Root in between the borders! Added to results. Hyperbolic solved: 21.2773642339947 [20250519_010533.]: Samplename: Sample#5 Root: 21.277 --> Root in between the borders! Added to results. Hyperbolic solved: 22.1858835788516 [20250519_010533.]: Samplename: Sample#6 Root: 22.186 --> Root in between the borders! Added to results. Hyperbolic solved: 40.1506639226714 [20250519_010533.]: Samplename: Sample#7 Root: 40.151 --> Root in between the borders! Added to results. Hyperbolic solved: 86.1385429083643 [20250519_010533.]: Samplename: Sample#8 Root: 86.139 --> Root in between the borders! Added to results. Hyperbolic solved: 5.22287566816154 [20250519_010533.]: Samplename: Sample#9 Root: 5.223 --> Root in between the borders! Added to results. [20250519_010533.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 74.4935883316308 [20250519_010533.]: Samplename: Sample#1 Root: 74.494 --> Root in between the borders! Added to results. Hyperbolic solved: 28.4239304939459 [20250519_010533.]: Samplename: Sample#10 Root: 28.424 --> Root in between the borders! Added to results. Hyperbolic solved: 38.422868577211 [20250519_010533.]: Samplename: Sample#2 Root: 38.423 --> Root in between the borders! Added to results. Hyperbolic solved: 54.1812729208768 [20250519_010533.]: Samplename: Sample#3 Root: 54.181 --> Root in between the borders! Added to results. Hyperbolic solved: 9.35760412501846 [20250519_010533.]: Samplename: Sample#4 Root: 9.358 --> Root in between the borders! Added to results. Hyperbolic solved: 20.0638687855045 [20250519_010533.]: Samplename: Sample#5 Root: 20.064 --> Root in between the borders! Added to results. Hyperbolic solved: 18.2691112345675 [20250519_010533.]: Samplename: Sample#6 Root: 18.269 --> Root in between the borders! Added to results. Hyperbolic solved: 36.8485384997942 [20250519_010533.]: Samplename: Sample#7 Root: 36.849 --> Root in between the borders! Added to results. Hyperbolic solved: 86.7213727244426 [20250519_010533.]: Samplename: Sample#8 Root: 86.721 --> Root in between the borders! Added to results. Hyperbolic solved: 8.13467837636389 [20250519_010533.]: Samplename: Sample#9 Root: 8.135 --> Root in between the borders! Added to results. [20250519_010533.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 72.9754823399013 [20250519_010533.]: Samplename: Sample#1 Root: 72.975 --> Root in between the borders! Added to results. Hyperbolic solved: 26.09308106721 [20250519_010533.]: Samplename: Sample#10 Root: 26.093 --> Root in between the borders! Added to results. Hyperbolic solved: 38.5204516929959 [20250519_010533.]: Samplename: Sample#2 Root: 38.52 --> Root in between the borders! Added to results. Hyperbolic solved: 52.0558039343263 [20250519_010533.]: Samplename: Sample#3 Root: 52.056 --> Root in between the borders! Added to results. Hyperbolic solved: 10.6836235174275 [20250519_010533.]: Samplename: Sample#4 Root: 10.684 --> Root in between the borders! Added to results. Hyperbolic solved: 23.1384875877206 [20250519_010533.]: Samplename: Sample#5 Root: 23.138 --> Root in between the borders! Added to results. Hyperbolic solved: 24.8934322445107 [20250519_010533.]: Samplename: Sample#6 Root: 24.893 --> Root in between the borders! Added to results. Hyperbolic solved: 40.6015033285401 [20250519_010533.]: Samplename: Sample#7 Root: 40.602 --> Root in between the borders! Added to results. Hyperbolic solved: 86.3873125270529 [20250519_010533.]: Samplename: Sample#8 Root: 86.387 --> Root in between the borders! Added to results. Hyperbolic solved: 4.44964604860886 [20250519_010533.]: Samplename: Sample#9 Root: 4.45 --> Root in between the borders! Added to results. [20250519_010533.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 74.4016735507272 [20250519_010533.]: Samplename: Sample#1 Root: 74.402 --> Root in between the borders! Added to results. Hyperbolic solved: 26.4295897789912 [20250519_010533.]: Samplename: Sample#10 Root: 26.43 --> Root in between the borders! Added to results. Hyperbolic solved: 39.6008425259518 [20250519_010533.]: Samplename: Sample#2 Root: 39.601 --> Root in between the borders! Added to results. Hyperbolic solved: 54.4093205134833 [20250519_010533.]: Samplename: Sample#3 Root: 54.409 --> Root in between the borders! Added to results. Hyperbolic solved: 12.6125973016001 [20250519_010533.]: Samplename: Sample#4 Root: 12.613 --> Root in between the borders! Added to results. Hyperbolic solved: 21.5537046072918 [20250519_010533.]: Samplename: Sample#5 Root: 21.554 --> Root in between the borders! Added to results. Hyperbolic solved: 25.5116357756709 [20250519_010533.]: Samplename: Sample#6 Root: 25.512 --> Root in between the borders! Added to results. Hyperbolic solved: 38.1579452647512 [20250519_010533.]: Samplename: Sample#7 Root: 38.158 --> Root in between the borders! Added to results. Hyperbolic solved: 86.2182702652395 [20250519_010533.]: Samplename: Sample#8 Root: 86.218 --> Root in between the borders! Added to results. Hyperbolic solved: 7.06911234650237 [20250519_010533.]: Samplename: Sample#9 Root: 7.069 --> Root in between the borders! Added to results. [20250519_010533.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 70.2481376198381 [20250519_010533.]: Samplename: Sample#1 Root: 70.248 --> Root in between the borders! Added to results. Hyperbolic solved: 24.9396715205194 [20250519_010533.]: Samplename: Sample#10 Root: 24.94 --> Root in between the borders! Added to results. Hyperbolic solved: 40.4614457485807 [20250519_010533.]: Samplename: Sample#2 Root: 40.461 --> Root in between the borders! Added to results. Hyperbolic solved: 49.3815751365795 [20250519_010533.]: Samplename: Sample#3 Root: 49.382 --> Root in between the borders! Added to results. Hyperbolic solved: 12.353607679335 [20250519_010533.]: Samplename: Sample#4 Root: 12.354 --> Root in between the borders! Added to results. Hyperbolic solved: 22.0901459497739 [20250519_010533.]: Samplename: Sample#5 Root: 22.09 --> Root in between the borders! Added to results. Hyperbolic solved: 24.5507390437226 [20250519_010533.]: Samplename: Sample#6 Root: 24.551 --> Root in between the borders! Added to results. Hyperbolic solved: 39.0947007538418 [20250519_010533.]: Samplename: Sample#7 Root: 39.095 --> Root in between the borders! Added to results. Hyperbolic solved: 90.4841321892294 [20250519_010533.]: Samplename: Sample#8 Root: 90.484 --> Root in between the borders! Added to results. Hyperbolic solved: 6.91649336851909 [20250519_010533.]: Samplename: Sample#9 Root: 6.916 --> Root in between the borders! Added to results. [20250519_010533.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 77.4558189028663 [20250519_010533.]: Samplename: Sample#1 Root: 77.456 --> Root in between the borders! Added to results. Hyperbolic solved: 27.5880652239037 [20250519_010533.]: Samplename: Sample#10 Root: 27.588 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2657605119474 [20250519_010533.]: Samplename: Sample#2 Root: 37.266 --> Root in between the borders! Added to results. Hyperbolic solved: 51.469303759821 [20250519_010533.]: Samplename: Sample#3 Root: 51.469 --> Root in between the borders! Added to results. Hyperbolic solved: 12.3197919642261 [20250519_010533.]: Samplename: Sample#4 Root: 12.32 --> Root in between the borders! Added to results. Hyperbolic solved: 18.9916700132306 [20250519_010533.]: Samplename: Sample#5 Root: 18.992 --> Root in between the borders! Added to results. Hyperbolic solved: 27.3757521451495 [20250519_010533.]: Samplename: Sample#6 Root: 27.376 --> Root in between the borders! Added to results. Hyperbolic solved: 38.1192529388954 [20250519_010533.]: Samplename: Sample#7 Root: 38.119 --> Root in between the borders! Added to results. Hyperbolic solved: 87.7229687796537 [20250519_010533.]: Samplename: Sample#8 Root: 87.723 --> Root in between the borders! Added to results. Hyperbolic solved: 7.66260533640959 [20250519_010533.]: Samplename: Sample#9 Root: 7.663 --> Root in between the borders! Added to results. [20250519_010533.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 75.6858966925732 [20250519_010533.]: Samplename: Sample#1 Root: 75.686 --> Root in between the borders! Added to results. Hyperbolic solved: 23.9111476853231 [20250519_010533.]: Samplename: Sample#10 Root: 23.911 --> Root in between the borders! Added to results. Hyperbolic solved: 41.6834100561109 [20250519_010533.]: Samplename: Sample#2 Root: 41.683 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3038531152796 [20250519_010533.]: Samplename: Sample#3 Root: 52.304 --> Root in between the borders! Added to results. Hyperbolic solved: 14.584082616843 [20250519_010533.]: Samplename: Sample#4 Root: 14.584 --> Root in between the borders! Added to results. Hyperbolic solved: 23.8322996387272 [20250519_010533.]: Samplename: Sample#5 Root: 23.832 --> Root in between the borders! Added to results. Hyperbolic solved: 26.8055282397054 [20250519_010533.]: Samplename: Sample#6 Root: 26.806 --> Root in between the borders! Added to results. Hyperbolic solved: 39.1748201958722 [20250519_010533.]: Samplename: Sample#7 Root: 39.175 --> Root in between the borders! Added to results. Hyperbolic solved: 86.4968654540458 [20250519_010533.]: Samplename: Sample#8 Root: 86.497 --> Root in between the borders! Added to results. Hyperbolic solved: 5.20445580813888 [20250519_010533.]: Samplename: Sample#9 Root: 5.204 --> Root in between the borders! Added to results. [20250519_010533.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 71.4358559327679 [20250519_010533.]: Samplename: Sample#1 Root: 71.436 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5978231834828 [20250519_010533.]: Samplename: Sample#10 Root: 26.598 --> Root in between the borders! Added to results. Hyperbolic solved: 32.9687797883659 [20250519_010533.]: Samplename: Sample#2 Root: 32.969 --> Root in between the borders! Added to results. Hyperbolic solved: 56.5920244960613 [20250519_010533.]: Samplename: Sample#3 Root: 56.592 --> Root in between the borders! Added to results. Hyperbolic solved: 11.2061554830111 [20250519_010533.]: Samplename: Sample#4 Root: 11.206 --> Root in between the borders! Added to results. Hyperbolic solved: 19.2580019302788 [20250519_010533.]: Samplename: Sample#5 Root: 19.258 --> Root in between the borders! Added to results. Hyperbolic solved: 36.3407423784248 [20250519_010533.]: Samplename: Sample#6 Root: 36.341 --> Root in between the borders! Added to results. Hyperbolic solved: 42.2292176199449 [20250519_010533.]: Samplename: Sample#7 Root: 42.229 --> Root in between the borders! Added to results. Hyperbolic solved: 85.1479651236815 [20250519_010533.]: Samplename: Sample#8 Root: 85.148 --> Root in between the borders! Added to results. Hyperbolic solved: 1.97353330914679 [20250519_010533.]: Samplename: Sample#9 Root: 1.974 --> Root in between the borders! Added to results. [20250519_010533.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 78.362574368733 [20250519_010533.]: Samplename: Sample#1 Root: 78.363 --> Root in between the borders! Added to results. Hyperbolic solved: 24.4725957151698 [20250519_010533.]: Samplename: Sample#10 Root: 24.473 --> Root in between the borders! Added to results. Hyperbolic solved: 38.5471090507298 [20250519_010533.]: Samplename: Sample#2 Root: 38.547 --> Root in between the borders! Added to results. Hyperbolic solved: 48.850764118569 [20250519_010533.]: Samplename: Sample#3 Root: 48.851 --> Root in between the borders! Added to results. Hyperbolic solved: 13.0341042345297 [20250519_010533.]: Samplename: Sample#4 Root: 13.034 --> Root in between the borders! Added to results. Hyperbolic solved: 22.1156929084775 [20250519_010533.]: Samplename: Sample#5 Root: 22.116 --> Root in between the borders! Added to results. Hyperbolic solved: 33.0730206710577 [20250519_010533.]: Samplename: Sample#6 Root: 33.073 --> Root in between the borders! Added to results. Hyperbolic solved: 36.8757560868803 [20250519_010533.]: Samplename: Sample#7 Root: 36.876 --> Root in between the borders! Added to results. Hyperbolic solved: 89.6667208986789 [20250519_010533.]: Samplename: Sample#8 Root: 89.667 --> Root in between the borders! Added to results. Hyperbolic solved: 8.10593089892949 [20250519_010533.]: Samplename: Sample#9 Root: 8.106 --> Root in between the borders! Added to results. [20250519_010533.]: Solving hyperbolic regression for row_means Hyperbolic solved: 74.8404708557895 [20250519_010533.]: Samplename: Sample#1 Root: 74.84 --> Root in between the borders! Added to results. Hyperbolic solved: 26.2806125509682 [20250519_010533.]: Samplename: Sample#10 Root: 26.281 --> Root in between the borders! Added to results. Hyperbolic solved: 38.1683813928189 [20250519_010533.]: Samplename: Sample#2 Root: 38.168 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3554454867083 [20250519_010533.]: Samplename: Sample#3 Root: 52.355 --> Root in between the borders! Added to results. Hyperbolic solved: 12.0337443526331 [20250519_010533.]: Samplename: Sample#4 Root: 12.034 --> Root in between the borders! Added to results. Hyperbolic solved: 21.2452112654646 [20250519_010533.]: Samplename: Sample#5 Root: 21.245 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5661392150144 [20250519_010533.]: Samplename: Sample#6 Root: 26.566 --> Root in between the borders! Added to results. Hyperbolic solved: 38.9839362125804 [20250519_010533.]: Samplename: Sample#7 Root: 38.984 --> Root in between the borders! Added to results. Hyperbolic solved: 87.2201089452416 [20250519_010533.]: Samplename: Sample#8 Root: 87.22 --> Root in between the borders! Added to results. Hyperbolic solved: 6.14149092380673 [20250519_010533.]: Samplename: Sample#9 Root: 6.141 --> Root in between the borders! Added to results. [20250519_010533.]: Entered 'clean_dt'-Function [20250519_010533.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_010533.]: got experimental data [20250519_010533.]: Entered 'clean_dt'-Function [20250519_010533.]: Importing data of type 2: Many loci in one sample (e.g., next-gen seq or microarray data) [20250519_010533.]: got experimental data [20250519_010533.]: Entered 'clean_dt'-Function [20250519_010533.]: Entered 'clean_dt'-Function [20250519_010533.]: Entered 'clean_dt'-Function [20250519_010533.]: Entered 'clean_dt'-Function [20250519_010533.]: Entered 'clean_dt'-Function [20250519_010533.]: Entered 'clean_dt'-Function [20250519_010533.]: Entered 'clean_dt'-Function [20250519_010533.]: Entered 'clean_dt'-Function [20250519_010534.]: Entered 'clean_dt'-Function [20250519_010534.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_010534.]: got calibration data ### ERROR 54: first column cannot be parsed to numeric ### [20250519_010534.]: Entered 'clean_dt'-Function [20250519_010534.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_010534.]: got calibration data ### ERROR 54: first column cannot be parsed to numeric ### [20250519_010534.]: Entered 'clean_dt'-Function [20250519_010534.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_010534.]: got calibration data [20250519_010534.]: ### ERROR ### The data provided contains less than four calibration steps. At least four distinct calibration steps are required to perform bias correction. [20250519_010534.]: Entered 'clean_dt'-Function [20250519_010534.]: Importing data of type 2: Many loci in one sample (e.g., next-gen seq or microarray data) [20250519_010534.]: got calibration data [20250519_010534.]: ### ERROR ### The data provided contains locus ids with heterogeneous counts of CpG-sites. [20250519_010534.]: Entered 'clean_dt'-Function [20250519_010534.]: Importing data of type 2: Many loci in one sample (e.g., next-gen seq or microarray data) [20250519_010534.]: got experimental data [20250519_010534.]: ### ERROR ### The data provided contains locus ids with heterogeneous counts of CpG-sites. [20250519_010534.]: Entered 'clean_dt'-Function [20250519_010534.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_010534.]: got calibration data [20250519_010534.]: Entered 'clean_dt'-Function [20250519_010534.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_010534.]: got calibration data [20250519_010534.]: Entered 'cubic_regression'-Function [20250519_010534.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010534.]: Entered 'cubic_regression'-Function [20250519_010534.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010534.]: Entered 'clean_dt'-Function [20250519_010534.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_010534.]: got calibration data [20250519_010534.]: Entered 'hyperbolic_regression'-Function [20250519_010534.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010534.]: Entered 'hyperbolic_regression'-Function [20250519_010534.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental [20250519_010534.]: on_start: using future::plan("sequential") [20250519_010534.]: Entered 'clean_dt'-Function [20250519_010534.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_010534.]: got experimental data [20250519_010534.]: Entered 'clean_dt'-Function [20250519_010534.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_010534.]: got calibration data [20250519_010534.]: ### Starting with regression calculations ### [20250519_010534.]: Entered 'regression_type1'-Function [20250519_010534.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_010534.]: Logging df_agg: CpG#1 [20250519_010534.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010534.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_010534.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_010534.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_010534.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_010534.]: Entered 'hyperbolic_regression'-Function [20250519_010534.]: 'hyperbolic_regression': minmax = FALSE [20250519_010534.]: Entered 'cubic_regression'-Function [20250519_010534.]: 'cubic_regression': minmax = FALSE [20250519_010534.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_010534.]: Logging df_agg: CpG#2 [20250519_010534.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010534.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_010534.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_010534.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_010534.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_010534.]: Entered 'hyperbolic_regression'-Function [20250519_010534.]: 'hyperbolic_regression': minmax = FALSE [20250519_010534.]: Entered 'cubic_regression'-Function [20250519_010534.]: 'cubic_regression': minmax = FALSE [20250519_010534.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_010534.]: Logging df_agg: CpG#3 [20250519_010534.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010534.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_010534.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_010534.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_010534.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_010534.]: Entered 'hyperbolic_regression'-Function [20250519_010534.]: 'hyperbolic_regression': minmax = FALSE [20250519_010534.]: Entered 'cubic_regression'-Function [20250519_010534.]: 'cubic_regression': minmax = FALSE [20250519_010534.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_010534.]: Logging df_agg: CpG#4 [20250519_010534.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010534.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_010534.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_010534.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_010534.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_010534.]: Entered 'hyperbolic_regression'-Function [20250519_010534.]: 'hyperbolic_regression': minmax = FALSE [20250519_010534.]: Entered 'cubic_regression'-Function [20250519_010534.]: 'cubic_regression': minmax = FALSE [20250519_010535.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_010535.]: Logging df_agg: CpG#5 [20250519_010535.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010535.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_010535.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_010535.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_010535.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_010535.]: Entered 'hyperbolic_regression'-Function [20250519_010535.]: 'hyperbolic_regression': minmax = FALSE [20250519_010535.]: Entered 'cubic_regression'-Function [20250519_010535.]: 'cubic_regression': minmax = FALSE [20250519_010535.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_010535.]: Logging df_agg: CpG#6 [20250519_010535.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010535.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_010535.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_010535.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_010535.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_010535.]: Entered 'hyperbolic_regression'-Function [20250519_010535.]: 'hyperbolic_regression': minmax = FALSE [20250519_010535.]: Entered 'cubic_regression'-Function [20250519_010535.]: 'cubic_regression': minmax = FALSE [20250519_010535.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_010535.]: Logging df_agg: CpG#7 [20250519_010535.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010535.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_010535.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_010535.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_010535.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_010535.]: Entered 'hyperbolic_regression'-Function [20250519_010535.]: 'hyperbolic_regression': minmax = FALSE [20250519_010535.]: Entered 'cubic_regression'-Function [20250519_010535.]: 'cubic_regression': minmax = FALSE [20250519_010535.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_010535.]: Logging df_agg: CpG#8 [20250519_010535.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010535.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_010535.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_010535.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_010535.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_010535.]: Entered 'hyperbolic_regression'-Function [20250519_010535.]: 'hyperbolic_regression': minmax = FALSE [20250519_010535.]: Entered 'cubic_regression'-Function [20250519_010535.]: 'cubic_regression': minmax = FALSE [20250519_010535.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_010535.]: Logging df_agg: CpG#9 [20250519_010535.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010535.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_010535.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_010535.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_010535.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_010535.]: Entered 'hyperbolic_regression'-Function [20250519_010535.]: 'hyperbolic_regression': minmax = FALSE [20250519_010535.]: Entered 'cubic_regression'-Function [20250519_010535.]: 'cubic_regression': minmax = FALSE [20250519_010535.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_010535.]: Logging df_agg: row_means [20250519_010535.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010535.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_010535.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_010535.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_010535.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_010535.]: Entered 'hyperbolic_regression'-Function [20250519_010535.]: 'hyperbolic_regression': minmax = FALSE [20250519_010535.]: Entered 'cubic_regression'-Function [20250519_010535.]: 'cubic_regression': minmax = FALSE [20250519_010535.]: ### Starting with plotting ### [20250519_010535.]: Creating plot No. 1 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Test_CpG1.png [20250519_010535.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -123249135.40483 , a = 49589952.0989949 , b = -123249135.40483 , d = 59280789.0121927 [20250519_010535.]: # CpG-site: CpG#1 Cubic: Using a = 6.53413423120091e-05 , b = -0.0055806968734969 , c = 0.784061853455188 , d = 1.93182659932656 [20250519_010538.]: Creating plot No. 2 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Test_CpG2.png [20250519_010538.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = 167929575.171327 , a = 42299444.0962795 , b = 167929575.171327 , d = 47897274.2220611 [20250519_010538.]: # CpG-site: CpG#2 Cubic: Using a = 8.04237934904601e-06 , b = 0.00293158941798942 , c = 0.514821742825076 , d = 9.27667003367003 [20250519_010540.]: Creating plot No. 3 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Test_CpG3.png [20250519_010540.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = 31004745.3756238 , a = 31050253.3779659 , b = 31004745.3756238 , d = 40665231.758814 [20250519_010540.]: # CpG-site: CpG#3 Cubic: Using a = 2.30555869809204e-05 , b = -0.000797009331409346 , c = 0.62783129228796 , d = 3.88705218855218 [20250519_010542.]: Creating plot No. 4 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Test_CpG4.png [20250519_010542.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = 128022258.276155 , a = 42779497.5538086 , b = 128022258.276155 , d = 50999439.9227065 [20250519_010542.]: # CpG-site: CpG#4 Cubic: Using a = 3.2166356902357e-05 , b = -0.0015913142857143 , c = 0.697398364598366 , d = 6.17055050505048 [20250519_010544.]: Creating plot No. 5 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Test_CpG5.png [20250519_010544.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = 29447513.6184781 , a = 41864399.8673718 , b = 29447513.6184781 , d = 55382346.8470806 [20250519_010544.]: # CpG-site: CpG#5 Cubic: Using a = -4.48459708193036e-06 , b = 0.00375371236171235 , c = 0.422446384479718 , d = 5.17203872053872 [20250519_010546.]: Creating plot No. 6 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Test_CpG6.png [20250519_010546.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = 17150080.5960938 , a = 40932834.1078748 , b = 17150080.5960938 , d = 46723471.6686748 [20250519_010546.]: # CpG-site: CpG#6 Cubic: Using a = 2.38852884399552e-05 , b = 0.000918637037037021 , c = 0.561022132435467 , d = 6.1479276094276 [20250519_010548.]: Creating plot No. 7 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Test_CpG7.png [20250519_010548.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -57204506.2862076 , a = 29554652.6579632 , b = -57204506.2862076 , d = 48548425.6483363 [20250519_010548.]: # CpG-site: CpG#7 Cubic: Using a = 4.98010505050505e-05 , b = -0.00436152150072151 , c = 0.579588917748918 , d = 1.72104545454544 [20250519_010550.]: Creating plot No. 8 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Test_CpG8.png [20250519_010550.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = 263871870.982016 , a = 38159683.1948542 , b = 263871870.982016 , d = 45284547.6704654 [20250519_010550.]: # CpG-site: CpG#8 Cubic: Using a = -1.50060965207632e-05 , b = 0.00447978143338143 , c = 0.534897737694404 , d = 9.56981481481482 [20250519_010552.]: Creating plot No. 9 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Test_CpG9.png [20250519_010552.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -224404013.660658 , a = 51672526.9851193 , b = -224404013.660658 , d = 60201205.3472543 [20250519_010552.]: # CpG-site: CpG#9 Cubic: Using a = 7.72300426487093e-05 , b = -0.00542281173641174 , c = 0.67899229597563 , d = 2.72413468013467 [20250519_010555.]: Creating plot No. 10 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Test_rowmeans.png [20250519_010555.]: # CpG-site: row_means Hyperbolic: Using bias_weight = 29838275.3876239 , a = 33244770.7608578 , b = 29838275.3876239 , d = 41191067.4974404 [20250519_010555.]: # CpG-site: row_means Cubic: Using a = 2.88923726150392e-05 , b = -0.000629959275292592 , c = 0.600117857944524 , d = 5.17789562289563 [20250519_010557.]: on_start: using future::plan("sequential") [20250519_010557.]: Entered 'clean_dt'-Function [20250519_010557.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_010557.]: got experimental data [20250519_010557.]: Entered 'clean_dt'-Function [20250519_010557.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) [20250519_010557.]: got calibration data [20250519_010557.]: ### Starting with regression calculations ### [20250519_010557.]: Entered 'regression_type1'-Function [20250519_010557.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_010557.]: Logging df_agg: CpG#1 [20250519_010557.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010557.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_010557.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_010557.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_010557.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) [20250519_010557.]: Entered 'hyperbolic_regression'-Function [20250519_010557.]: 'hyperbolic_regression': minmax = FALSE [20250519_010557.]: Entered 'cubic_regression'-Function [20250519_010557.]: 'cubic_regression': minmax = FALSE [20250519_010557.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_010557.]: Logging df_agg: CpG#2 [20250519_010557.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010557.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_010557.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_010557.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_010557.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) [20250519_010557.]: Entered 'hyperbolic_regression'-Function [20250519_010557.]: 'hyperbolic_regression': minmax = FALSE [20250519_010557.]: Entered 'cubic_regression'-Function [20250519_010557.]: 'cubic_regression': minmax = FALSE [20250519_010557.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_010557.]: Logging df_agg: CpG#3 [20250519_010557.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010557.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_010557.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_010557.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_010557.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) [20250519_010557.]: Entered 'hyperbolic_regression'-Function [20250519_010557.]: 'hyperbolic_regression': minmax = FALSE [20250519_010557.]: Entered 'cubic_regression'-Function [20250519_010557.]: 'cubic_regression': minmax = FALSE [20250519_010557.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_010557.]: Logging df_agg: CpG#4 [20250519_010557.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010557.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_010557.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_010557.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_010557.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) [20250519_010557.]: Entered 'hyperbolic_regression'-Function [20250519_010557.]: 'hyperbolic_regression': minmax = FALSE [20250519_010557.]: Entered 'cubic_regression'-Function [20250519_010557.]: 'cubic_regression': minmax = FALSE [20250519_010557.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_010557.]: Logging df_agg: CpG#5 [20250519_010557.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010557.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_010557.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_010557.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_010557.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) [20250519_010557.]: Entered 'hyperbolic_regression'-Function [20250519_010557.]: 'hyperbolic_regression': minmax = FALSE [20250519_010557.]: Entered 'cubic_regression'-Function [20250519_010557.]: 'cubic_regression': minmax = FALSE [20250519_010557.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_010557.]: Logging df_agg: CpG#6 [20250519_010557.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010557.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_010557.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_010557.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_010557.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) [20250519_010557.]: Entered 'hyperbolic_regression'-Function [20250519_010557.]: 'hyperbolic_regression': minmax = FALSE [20250519_010557.]: Entered 'cubic_regression'-Function [20250519_010557.]: 'cubic_regression': minmax = FALSE [20250519_010557.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_010557.]: Logging df_agg: CpG#7 [20250519_010557.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010557.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_010557.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_010557.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_010557.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) [20250519_010557.]: Entered 'hyperbolic_regression'-Function [20250519_010557.]: 'hyperbolic_regression': minmax = FALSE [20250519_010557.]: Entered 'cubic_regression'-Function [20250519_010557.]: 'cubic_regression': minmax = FALSE [20250519_010557.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_010557.]: Logging df_agg: CpG#8 [20250519_010557.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010557.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_010557.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_010557.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_010557.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) [20250519_010557.]: Entered 'hyperbolic_regression'-Function [20250519_010557.]: 'hyperbolic_regression': minmax = FALSE [20250519_010557.]: Entered 'cubic_regression'-Function [20250519_010557.]: 'cubic_regression': minmax = FALSE [20250519_010557.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_010557.]: Logging df_agg: CpG#9 [20250519_010557.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010557.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_010557.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_010557.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_010557.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) [20250519_010557.]: Entered 'hyperbolic_regression'-Function [20250519_010557.]: 'hyperbolic_regression': minmax = FALSE [20250519_010557.]: Entered 'cubic_regression'-Function [20250519_010557.]: 'cubic_regression': minmax = FALSE [20250519_010557.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_010557.]: Logging df_agg: row_means [20250519_010557.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010557.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_010557.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_010557.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_010557.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) [20250519_010557.]: Entered 'hyperbolic_regression'-Function [20250519_010557.]: 'hyperbolic_regression': minmax = FALSE [20250519_010557.]: Entered 'cubic_regression'-Function [20250519_010557.]: 'cubic_regression': minmax = FALSE [20250519_010558.]: Entered 'solving_equations'-Function [20250519_010558.]: Solving cubic regression for CpG#1 Coefficients: -1.03617340067344Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_010558.]: Samplename: 0 Root: 1.334 --> Root in between the borders! Added to results. Coefficients: -8.34150673400678Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_010558.]: Samplename: 12.5 Root: 11.446 --> Root in between the borders! Added to results. Coefficients: -15.3881734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_010558.]: Samplename: 25 Root: 22.228 --> Root in between the borders! Added to results. Coefficients: -24.2801734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_010558.]: Samplename: 37.5 Root: 36.374 --> Root in between the borders! Added to results. Coefficients: -34.9006734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_010558.]: Samplename: 50 Root: 52.044 --> Root in between the borders! Added to results. Coefficients: -46.3541734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_010558.]: Samplename: 62.5 Root: 66.144 --> Root in between the borders! Added to results. Coefficients: -55.8931734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_010558.]: Samplename: 75 Root: 75.864 --> Root in between the borders! Added to results. Coefficients: -63.0981734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_010558.]: Samplename: 87.5 Root: 82.254 --> Root in between the borders! Added to results. Coefficients: -91.0461734006735Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 [20250519_010558.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.877 --> '100 < root < 110' --> substitute 100 [20250519_010558.]: Solving cubic regression for CpG#2 Coefficients: -0.283329966329966Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_010558.]: Samplename: 0 Root: 0.549 --> Root in between the borders! Added to results. Coefficients: -6.33999663299663Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_010558.]: Samplename: 12.5 Root: 11.533 --> Root in between the borders! Added to results. Coefficients: -15.93932996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_010558.]: Samplename: 25 Root: 26.628 --> Root in between the borders! Added to results. Coefficients: -22.33732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_010558.]: Samplename: 37.5 Root: 35.509 --> Root in between the borders! Added to results. Coefficients: -32.22832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_010558.]: Samplename: 50 Root: 47.851 --> Root in between the borders! Added to results. Coefficients: -49.96332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_010558.]: Samplename: 62.5 Root: 66.893 --> Root in between the borders! Added to results. Coefficients: -58.96582996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_010558.]: Samplename: 75 Root: 75.431 --> Root in between the borders! Added to results. Coefficients: -68.8366632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_010558.]: Samplename: 87.5 Root: 84.118 --> Root in between the borders! Added to results. Coefficients: -90.57732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 [20250519_010558.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.287 --> '100 < root < 110' --> substitute 100 [20250519_010558.]: Solving cubic regression for CpG#3 Coefficients: -0.90294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_010558.]: Samplename: 0 Root: 1.441 --> Root in between the borders! Added to results. Coefficients: -6.57294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_010558.]: Samplename: 12.5 Root: 10.568 --> Root in between the borders! Added to results. Coefficients: -15.4289478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_010558.]: Samplename: 25 Root: 24.796 --> Root in between the borders! Added to results. Coefficients: -22.6129478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_010558.]: Samplename: 37.5 Root: 35.952 --> Root in between the borders! Added to results. Coefficients: -32.7754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_010558.]: Samplename: 50 Root: 50.684 --> Root in between the borders! Added to results. Coefficients: -43.8889478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_010558.]: Samplename: 62.5 Root: 65.142 --> Root in between the borders! Added to results. Coefficients: -54.9754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_010558.]: Samplename: 75 Root: 77.905 --> Root in between the borders! Added to results. Coefficients: -57.6562811447812Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_010558.]: Samplename: 87.5 Root: 80.767 --> Root in between the borders! Added to results. Coefficients: -80.6649478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 [20250519_010558.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.38 --> '100 < root < 110' --> substitute 100 [20250519_010558.]: Solving cubic regression for CpG#4 Coefficients: -0.597449494949524Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_010558.]: Samplename: 0 Root: 0.858 --> Root in between the borders! Added to results. Coefficients: -8.25278282828286Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_010558.]: Samplename: 12.5 Root: 12.086 --> Root in between the borders! Added to results. Coefficients: -15.8034494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_010558.]: Samplename: 25 Root: 23.316 --> Root in between the borders! Added to results. Coefficients: -25.5274494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_010558.]: Samplename: 37.5 Root: 37.383 --> Root in between the borders! Added to results. Coefficients: -33.6369494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_010558.]: Samplename: 50 Root: 48.353 --> Root in between the borders! Added to results. Coefficients: -50.2554494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_010558.]: Samplename: 62.5 Root: 68.082 --> Root in between the borders! Added to results. Coefficients: -56.5394494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_010558.]: Samplename: 75 Root: 74.615 --> Root in between the borders! Added to results. Coefficients: -65.5927828282829Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_010558.]: Samplename: 87.5 Root: 83.254 --> Root in between the borders! Added to results. Coefficients: -88.3214494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 [20250519_010558.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.715 --> '100 < root < 110' --> substitute 100 [20250519_010558.]: Solving cubic regression for CpG#5 Coefficients: -0.623961279461278Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_010558.]: Samplename: 0 Root: 1.458 --> Root in between the borders! Added to results. Coefficients: -4.76796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_010558.]: Samplename: 12.5 Root: 10.347 --> Root in between the borders! Added to results. Coefficients: -12.7259612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_010558.]: Samplename: 25 Root: 24.815 --> Root in between the borders! Added to results. Coefficients: -21.1599612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_010558.]: Samplename: 37.5 Root: 37.902 --> Root in between the borders! Added to results. Coefficients: -30.6954612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_010558.]: Samplename: 50 Root: 50.977 --> Root in between the borders! Added to results. Coefficients: -39.6579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_010558.]: Samplename: 62.5 Root: 62.126 --> Root in between the borders! Added to results. Coefficients: -51.6829612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_010558.]: Samplename: 75 Root: 75.852 --> Root in between the borders! Added to results. Coefficients: -61.0146279461279Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_010558.]: Samplename: 87.5 Root: 85.767 --> Root in between the borders! Added to results. Coefficients: -76.0699612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 [20250519_010558.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.743 --> '100 < root < 110' --> substitute 100 [20250519_010558.]: Solving cubic regression for CpG#6 Coefficients: -0.196072390572403Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_010558.]: Samplename: 0 Root: 0.349 --> Root in between the borders! Added to results. Coefficients: -6.73873905723907Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_010558.]: Samplename: 12.5 Root: 11.718 --> Root in between the borders! Added to results. Coefficients: -15.8880723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_010558.]: Samplename: 25 Root: 26.396 --> Root in between the borders! Added to results. Coefficients: -22.0000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_010558.]: Samplename: 37.5 Root: 35.301 --> Root in between the borders! Added to results. Coefficients: -33.4445723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_010558.]: Samplename: 50 Root: 50.134 --> Root in between the borders! Added to results. Coefficients: -46.9000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_010558.]: Samplename: 62.5 Root: 64.993 --> Root in between the borders! Added to results. Coefficients: -55.8320723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_010558.]: Samplename: 75 Root: 73.639 --> Root in between the borders! Added to results. Coefficients: -71.5454057239057Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_010558.]: Samplename: 87.5 Root: 87.043 --> Root in between the borders! Added to results. Coefficients: -89.6560723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 [20250519_010558.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.329 --> '100 < root < 110' --> substitute 100 [20250519_010558.]: Solving cubic regression for CpG#7 Coefficients: -1.21495454545456Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_010558.]: Samplename: 0 Root: 2.13 --> Root in between the borders! Added to results. Coefficients: -5.39562121212123Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_010558.]: Samplename: 12.5 Root: 9.973 --> Root in between the borders! Added to results. Coefficients: -11.2649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_010558.]: Samplename: 25 Root: 22.206 --> Root in between the borders! Added to results. Coefficients: -17.4509545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_010558.]: Samplename: 37.5 Root: 35.814 --> Root in between the borders! Added to results. Coefficients: -26.0314545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_010558.]: Samplename: 50 Root: 53.28 --> Root in between the borders! Added to results. Coefficients: -33.9649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_010558.]: Samplename: 62.5 Root: 66.598 --> Root in between the borders! Added to results. Coefficients: -41.1689545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_010558.]: Samplename: 75 Root: 76.575 --> Root in between the borders! Added to results. Coefficients: -44.1356212121212Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_010558.]: Samplename: 87.5 Root: 80.219 --> Root in between the borders! Added to results. Coefficients: -67.2229545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 [20250519_010558.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.506 --> '100 < root < 110' --> substitute 100 [20250519_010558.]: Solving cubic regression for CpG#8 Coefficients: -1.09618518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_010558.]: Samplename: 0 Root: 2.016 --> Root in between the borders! Added to results. Coefficients: -5.44685185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_010558.]: Samplename: 12.5 Root: 9.458 --> Root in between the borders! Added to results. Coefficients: -16.9301851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_010558.]: Samplename: 25 Root: 26.35 --> Root in between the borders! Added to results. Coefficients: -23.3501851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_010558.]: Samplename: 37.5 Root: 34.728 --> Root in between the borders! Added to results. Coefficients: -37.6251851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_010558.]: Samplename: 50 Root: 51.781 --> Root in between the borders! Added to results. Coefficients: -48.8261851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_010558.]: Samplename: 62.5 Root: 64.192 --> Root in between the borders! Added to results. Coefficients: -61.6676851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_010558.]: Samplename: 75 Root: 77.804 --> Root in between the borders! Added to results. Coefficients: -64.5101851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_010558.]: Samplename: 87.5 Root: 80.758 --> Root in between the borders! Added to results. Coefficients: -86.0601851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 [20250519_010558.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.834 --> '100 < root < 110' --> substitute 100 [20250519_010558.]: Solving cubic regression for CpG#9 Coefficients: -0.989865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_010558.]: Samplename: 0 Root: 1.475 --> Root in between the borders! Added to results. Coefficients: -6.39586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_010558.]: Samplename: 12.5 Root: 10.12 --> Root in between the borders! Added to results. Coefficients: -14.7058653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_010558.]: Samplename: 25 Root: 24.844 --> Root in between the borders! Added to results. Coefficients: -20.6238653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_010558.]: Samplename: 37.5 Root: 35.327 --> Root in between the borders! Added to results. Coefficients: -31.3958653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_010558.]: Samplename: 50 Root: 51.855 --> Root in between the borders! Added to results. Coefficients: -42.6858653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_010558.]: Samplename: 62.5 Root: 65.265 --> Root in between the borders! Added to results. Coefficients: -52.9033653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_010558.]: Samplename: 75 Root: 74.915 --> Root in between the borders! Added to results. Coefficients: -65.492531986532Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_010558.]: Samplename: 87.5 Root: 84.67 --> Root in between the borders! Added to results. Coefficients: -92.9898653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 [20250519_010558.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.082 --> '100 < root < 110' --> substitute 100 [20250519_010558.]: Solving cubic regression for row_means Coefficients: -0.771215488215478Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 [20250519_010558.]: Samplename: 0 Root: 1.287 --> Root in between the borders! Added to results. Coefficients: -6.47247474747474Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 [20250519_010558.]: Samplename: 12.5 Root: 10.847 --> Root in between the borders! Added to results. Coefficients: -14.8972154882155Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 [20250519_010558.]: Samplename: 25 Root: 24.737 --> Root in between the borders! Added to results. Coefficients: -22.1492154882155Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 [20250519_010558.]: Samplename: 37.5 Root: 36.02 --> Root in between the borders! Added to results. Coefficients: -32.5259932659933Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 [20250519_010558.]: Samplename: 50 Root: 50.639 --> Root in between the borders! Added to results. Coefficients: -44.7218821548821Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 [20250519_010558.]: Samplename: 62.5 Root: 65.497 --> Root in between the borders! Added to results. Coefficients: -54.4032154882155Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 [20250519_010558.]: Samplename: 75 Root: 75.751 --> Root in between the borders! Added to results. Coefficients: -62.4313636363636Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 [20250519_010558.]: Samplename: 87.5 Root: 83.403 --> Root in between the borders! Added to results. Coefficients: -84.7343265993266Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 [20250519_010558.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.573 --> '100 < root < 110' --> substitute 100 [20250519_010558.]: ### Starting with regression calculations ### [20250519_010558.]: Entered 'regression_type1'-Function [20250519_010558.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) [20250519_010558.]: Logging df_agg: CpG#1 [20250519_010558.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010558.]: c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)[20250519_010558.]: c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)[20250519_010558.]: c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) [20250519_010558.]: Entered 'hyperbolic_regression'-Function [20250519_010558.]: 'hyperbolic_regression': minmax = FALSE [20250519_010558.]: Entered 'cubic_regression'-Function [20250519_010558.]: 'cubic_regression': minmax = FALSE [20250519_010559.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) [20250519_010559.]: Logging df_agg: CpG#2 [20250519_010559.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010559.]: c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)[20250519_010559.]: c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)[20250519_010559.]: c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) [20250519_010559.]: Entered 'hyperbolic_regression'-Function [20250519_010559.]: 'hyperbolic_regression': minmax = FALSE [20250519_010559.]: Entered 'cubic_regression'-Function [20250519_010559.]: 'cubic_regression': minmax = FALSE [20250519_010559.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) [20250519_010559.]: Logging df_agg: CpG#3 [20250519_010559.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010559.]: c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)[20250519_010559.]: c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)[20250519_010559.]: c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) [20250519_010559.]: Entered 'hyperbolic_regression'-Function [20250519_010559.]: 'hyperbolic_regression': minmax = FALSE [20250519_010559.]: Entered 'cubic_regression'-Function [20250519_010559.]: 'cubic_regression': minmax = FALSE [20250519_010559.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) [20250519_010559.]: Logging df_agg: CpG#4 [20250519_010559.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010559.]: c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)[20250519_010559.]: c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)[20250519_010559.]: c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) [20250519_010559.]: Entered 'hyperbolic_regression'-Function [20250519_010559.]: 'hyperbolic_regression': minmax = FALSE [20250519_010559.]: Entered 'cubic_regression'-Function [20250519_010559.]: 'cubic_regression': minmax = FALSE [20250519_010559.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) [20250519_010559.]: Logging df_agg: CpG#5 [20250519_010559.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010559.]: c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)[20250519_010559.]: c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)[20250519_010559.]: c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) [20250519_010559.]: Entered 'hyperbolic_regression'-Function [20250519_010559.]: 'hyperbolic_regression': minmax = FALSE [20250519_010559.]: Entered 'cubic_regression'-Function [20250519_010559.]: 'cubic_regression': minmax = FALSE [20250519_010559.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) [20250519_010559.]: Logging df_agg: CpG#6 [20250519_010559.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010559.]: c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)[20250519_010559.]: c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)[20250519_010559.]: c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) [20250519_010559.]: Entered 'hyperbolic_regression'-Function [20250519_010559.]: 'hyperbolic_regression': minmax = FALSE [20250519_010559.]: Entered 'cubic_regression'-Function [20250519_010559.]: 'cubic_regression': minmax = FALSE [20250519_010559.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) [20250519_010559.]: Logging df_agg: CpG#7 [20250519_010559.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010559.]: c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)[20250519_010559.]: c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)[20250519_010559.]: c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) [20250519_010559.]: Entered 'hyperbolic_regression'-Function [20250519_010559.]: 'hyperbolic_regression': minmax = FALSE [20250519_010559.]: Entered 'cubic_regression'-Function [20250519_010559.]: 'cubic_regression': minmax = FALSE [20250519_010559.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) [20250519_010559.]: Logging df_agg: CpG#8 [20250519_010559.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010559.]: c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)[20250519_010559.]: c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)[20250519_010559.]: c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) [20250519_010559.]: Entered 'hyperbolic_regression'-Function [20250519_010559.]: 'hyperbolic_regression': minmax = FALSE [20250519_010559.]: Entered 'cubic_regression'-Function [20250519_010559.]: 'cubic_regression': minmax = FALSE [20250519_010559.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) [20250519_010559.]: Logging df_agg: CpG#9 [20250519_010559.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010559.]: c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)[20250519_010559.]: c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)[20250519_010559.]: c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) [20250519_010559.]: Entered 'hyperbolic_regression'-Function [20250519_010559.]: 'hyperbolic_regression': minmax = FALSE [20250519_010559.]: Entered 'cubic_regression'-Function [20250519_010559.]: 'cubic_regression': minmax = FALSE [20250519_010559.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) [20250519_010559.]: Logging df_agg: row_means [20250519_010559.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010559.]: c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)[20250519_010559.]: c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)[20250519_010559.]: c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) [20250519_010559.]: Entered 'hyperbolic_regression'-Function [20250519_010559.]: 'hyperbolic_regression': minmax = FALSE [20250519_010559.]: Entered 'cubic_regression'-Function [20250519_010559.]: 'cubic_regression': minmax = FALSE [20250519_010559.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Test_error_CpG1_corrected_h.png [20250519_010601.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Test_error_CpG2_corrected_h.png [20250519_010603.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Test_error_CpG3_corrected_h.png [20250519_010605.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Test_error_CpG4_corrected_h.png [20250519_010606.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Test_error_CpG5_corrected_h.png [20250519_010608.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Test_error_CpG6_corrected_h.png [20250519_010610.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Test_error_CpG7_corrected_h.png [20250519_010612.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Test_error_CpG8_corrected_h.png [20250519_010613.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Test_error_CpG9_corrected_h.png [20250519_010615.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Test_error_rowmeans_corrected_h.png [20250519_010617.]: on_start: using future::plan("sequential") [20250519_010619.]: on_start: using future::plan("sequential") [ FAIL 4 | WARN 209 | SKIP 12 | PASS 42 ] == Skipped tests (12) ========================================================== * On CRAN (12): 'test-algorithm_minmax_FALSE.R:113:5', 'test-algorithm_minmax_FALSE_re.R:173:5', 'test-algorithm_minmax_TRUE.R:114:5', 'test-algorithm_minmax_TRUE_re.R:175:5', 'test-clean_dt.R:19:5', 'test-clean_dt.R:61:5', 'test-create_aggregated.R:21:5', 'test-cubic.R:31:5', 'test-cubic.R:60:5', 'test-hyperbolic.R:32:5', 'test-hyperbolic.R:61:5', 'test-lints.R:12:5' == Failed tests ================================================================ -- Failure ('test-algorithm_minmax_FALSE.R:71:5'): algorithm test, type 1, minmax = FALSE -- regression_results$result_list (`actual`) not equal to regression_results_fast$result_list (`expected`). `actual$CpG#1$Coef_hyper$a`: -108.5680 `expected$CpG#1$Coef_hyper$a`: -108.5682 `actual$CpG#1$Coef_hyper$b`: -937.719 `expected$CpG#1$Coef_hyper$b`: -937.718 `actual$CpG#1$Coef_hyper$d`: -232.0571 `expected$CpG#1$Coef_hyper$d`: -232.0574 `actual$CpG#1$Coef_hyper$b1`: 0.5690716 `expected$CpG#1$Coef_hyper$b1`: 0.5690721 `actual$CpG#1$Coef_hyper$s`: 4.075791 `expected$CpG#1$Coef_hyper$s`: 4.075782 `actual$CpG#2$Coef_hyper$a`: -141.862264 `expected$CpG#2$Coef_hyper$a`: -141.862268 `actual$CpG#3$Coef_hyper$a`: -170.4245 `expected$CpG#3$Coef_hyper$a`: -170.4243 `actual$CpG#3$Coef_hyper$b`: -1477.4686 `expected$CpG#3$Coef_hyper$b`: -1477.4687 `actual$CpG#3$Coef_hyper$d`: -327.1902 `expected$CpG#3$Coef_hyper$d`: -327.1899 `actual$CpG#3$Coef_hyper$b1`: 0.6943673 `expected$CpG#3$Coef_hyper$b1`: 0.6943671 `actual$CpG#3$Coef_hyper$s`: 4.540975 `expected$CpG#3$Coef_hyper$s`: 4.540980 `actual$CpG#4$Coef_hyper$a`: -162.1698 `expected$CpG#4$Coef_hyper$a`: -162.1699 `actual$CpG#4$Coef_hyper$b`: -2115.8779 `expected$CpG#4$Coef_hyper$b`: -2115.8781 `actual$CpG#4$Coef_hyper$d`: -299.6623 `expected$CpG#4$Coef_hyper$d`: -299.6625 `actual$CpG#4$Coef_hyper$b1`: 0.6662910 `expected$CpG#4$Coef_hyper$b1`: 0.6662912 `actual$CpG#4$Coef_hyper$s`: 7.075768 `expected$CpG#4$Coef_hyper$s`: 7.075764 `actual$CpG#5$Coef_hyper$a`: -151.44007 `expected$CpG#5$Coef_hyper$a`: -151.44009 `actual$CpG#5$Coef_hyper$b`: -1383.32739 `expected$CpG#5$Coef_hyper$b`: -1383.32735 `actual$CpG#5$Coef_hyper$d`: -304.20025 `expected$CpG#5$Coef_hyper$d`: -304.20028 `actual$CpG#5$Coef_hyper$b1`: 0.67126918 `expected$CpG#5$Coef_hyper$b1`: 0.67126921 `actual$CpG#5$Coef_hyper$s`: 4.5750679 `expected$CpG#5$Coef_hyper$s`: 4.5750673 `actual$CpG#6$Coef_hyper$a`: -125.016146 `expected$CpG#6$Coef_hyper$a`: -125.016150 `actual$CpG#6$Coef_hyper$b`: -1550.59308 `expected$CpG#6$Coef_hyper$b`: -1550.59305 `actual$CpG#6$Coef_hyper$d`: -247.279679 `expected$CpG#6$Coef_hyper$d`: -247.279684 `actual$CpG#6$Coef_hyper$s`: 6.2900686 `expected$CpG#6$Coef_hyper$s`: 6.2900684 `actual$CpG#7$Coef_hyper$a`: -75.2725 `expected$CpG#7$Coef_hyper$a`: -75.2727 `actual$CpG#7$Coef_hyper$b`: -772.8442 `expected$CpG#7$Coef_hyper$b`: -772.8433 `actual$CpG#7$Coef_hyper$d`: -226.6731 `expected$CpG#7$Coef_hyper$d`: -226.6734 `actual$CpG#7$Coef_hyper$b1`: 0.5588361 `expected$CpG#7$Coef_hyper$b1`: 0.5588367 `actual$CpG#7$Coef_hyper$s`: 3.474320 `expected$CpG#7$Coef_hyper$s`: 3.474311 `actual$CpG#8$Coef_hyper$a`: -278.0173 `expected$CpG#8$Coef_hyper$a`: -278.0172 `actual$CpG#8$Coef_hyper$b`: -3872.7362 `expected$CpG#8$Coef_hyper$b`: -3872.7353 `actual$CpG#8$Coef_hyper$d`: -438.8943 `expected$CpG#8$Coef_hyper$d`: -438.8941 `actual$CpG#8$Coef_hyper$b1`: 0.77215469 `expected$CpG#8$Coef_hyper$b1`: 0.77215462 `actual$CpG#8$Coef_hyper$s`: 8.831458 `expected$CpG#8$Coef_hyper$s`: 8.831459 `actual$CpG#9$Coef_hyper$a`: -70.67961 `expected$CpG#9$Coef_hyper$a`: -70.67963 `actual$CpG#9$Coef_hyper$b`: -843.9255 `expected$CpG#9$Coef_hyper$b`: -843.9253 `actual$CpG#9$Coef_hyper$d`: -184.48215 `expected$CpG#9$Coef_hyper$d`: -184.48218 `actual$CpG#9$Coef_hyper$b1`: 0.45794215 `expected$CpG#9$Coef_hyper$b1`: 0.45794222 `actual$CpG#9$Coef_hyper$s`: 4.615973 `expected$CpG#9$Coef_hyper$s`: 4.615971 `actual$row_means$Coef_hyper$a`: -127.67503 `expected$row_means$Coef_hyper$a`: -127.67508 `actual$row_means$Coef_hyper$b`: -1525.7576 `expected$row_means$Coef_hyper$b`: -1525.7575 `actual$row_means$Coef_hyper$d`: -263.00305 `expected$row_means$Coef_hyper$d`: -263.00310 `actual$row_means$Coef_hyper$b1`: 0.61977627 `expected$row_means$Coef_hyper$b1`: 0.61977635 `actual$row_means$Coef_hyper$s`: 5.824068 `expected$row_means$Coef_hyper$s`: 5.824067 -- Failure ('test-algorithm_minmax_FALSE.R:88:5'): algorithm test, type 1, minmax = FALSE -- regression_results$result_list (`actual`) not equal to regression_results_minpack$result_list (`expected`). `actual$CpG#1$SSE_hyper`: 77 `expected$CpG#1$SSE_hyper`: 228 `actual$CpG#1$Coef_hyper$a`: -109 `expected$CpG#1$Coef_hyper$a`: 49589952 `actual$CpG#1$Coef_hyper$b`: -938 `expected$CpG#1$Coef_hyper$b`: -123249135 `actual$CpG#1$Coef_hyper$d`: -232 `expected$CpG#1$Coef_hyper$d`: 59280789 `actual$CpG#1$Coef_hyper$R2`: 0.99 `expected$CpG#1$Coef_hyper$R2`: 0.97 `actual$CpG#1$Coef_hyper$b1`: 0.6 `expected$CpG#1$Coef_hyper$b1`: 1.0 `actual$CpG#1$Coef_hyper$s`: 4 `expected$CpG#1$Coef_hyper$s`: 2 `actual$CpG#2$SSE_hyper`: 46 `expected$CpG#2$SSE_hyper`: 175 `actual$CpG#2$Coef_hyper$a`: -142 `expected$CpG#2$Coef_hyper$a`: 42299444 `actual$CpG#2$Coef_hyper$b`: -2393 `expected$CpG#2$Coef_hyper$b`: 167929575 `actual$CpG#2$Coef_hyper$d`: -268 `expected$CpG#2$Coef_hyper$d`: 47897274 `actual$CpG#2$Coef_hyper$R2`: 0.99 `expected$CpG#2$Coef_hyper$R2`: 0.98 `actual$CpG#2$Coef_hyper$b1`: 0.6 `expected$CpG#2$Coef_hyper$b1`: 1.0 `actual$CpG#2$Coef_hyper$s`: 9 `expected$CpG#2$Coef_hyper$s`: 4 `actual$CpG#3$SSE_hyper`: 67 `expected$CpG#3$SSE_hyper`: 123 `actual$CpG#3$Coef_hyper$a`: -170 `expected$CpG#3$Coef_hyper$a`: 31050253 `actual$CpG#3$Coef_hyper$b`: -1477 `expected$CpG#3$Coef_hyper$b`: 31004745 `actual$CpG#3$Coef_hyper$d`: -327 `expected$CpG#3$Coef_hyper$d`: 40665232 `actual$CpG#3$Coef_hyper$R2`: 0.99 `expected$CpG#3$Coef_hyper$R2`: 0.98 `actual$CpG#3$Coef_hyper$b1`: 0.7 `expected$CpG#3$Coef_hyper$b1`: 1.0 `actual$CpG#3$Coef_hyper$s`: 5 `expected$CpG#3$Coef_hyper$s`: 1 `actual$CpG#4$SSE_hyper`: 58 `expected$CpG#4$SSE_hyper`: 141 `actual$CpG#4$Coef_hyper$a`: -162 `expected$CpG#4$Coef_hyper$a`: 42779498 `actual$CpG#4$Coef_hyper$b`: -2116 `expected$CpG#4$Coef_hyper$b`: 128022258 `actual$CpG#4$Coef_hyper$d`: -300 `expected$CpG#4$Coef_hyper$d`: 50999440 `actual$CpG#4$Coef_hyper$R2`: 0.99 `expected$CpG#4$Coef_hyper$R2`: 0.98 `actual$CpG#4$Coef_hyper$b1`: 0.7 `expected$CpG#4$Coef_hyper$b1`: 1.0 `actual$CpG#4$Coef_hyper$s`: 7 `expected$CpG#4$Coef_hyper$s`: 3 `actual$CpG#5$SSE_hyper`: 8 `expected$CpG#5$SSE_hyper`: 78 `actual$CpG#5$Coef_hyper$a`: -151 `expected$CpG#5$Coef_hyper$a`: 41864400 `actual$CpG#5$Coef_hyper$b`: -1383 `expected$CpG#5$Coef_hyper$b`: 29447514 `actual$CpG#5$Coef_hyper$d`: -304 `expected$CpG#5$Coef_hyper$d`: 55382347 `actual$CpG#5$Coef_hyper$R2`: 1.00 `expected$CpG#5$Coef_hyper$R2`: 0.99 `actual$CpG#5$Coef_hyper$b1`: 0.7 `expected$CpG#5$Coef_hyper$b1`: 1.0 `actual$CpG#5$Coef_hyper$s`: 5 `expected$CpG#5$Coef_hyper$s`: 1 `actual$CpG#6$SSE_hyper`: 12 `expected$CpG#6$SSE_hyper`: 168 `actual$CpG#6$Coef_hyper$a`: -125 `expected$CpG#6$Coef_hyper$a`: 40932834 `actual$CpG#6$Coef_hyper$b`: -1551 `expected$CpG#6$Coef_hyper$b`: 17150081 `actual$CpG#6$Coef_hyper$d`: -247 `expected$CpG#6$Coef_hyper$d`: 46723472 `actual$CpG#6$Coef_hyper$R2`: 1.00 `expected$CpG#6$Coef_hyper$R2`: 0.98 `actual$CpG#6$Coef_hyper$b1`: 0.6 `expected$CpG#6$Coef_hyper$b1`: 1.0 `actual$CpG#6$Coef_hyper$s`: 6 `expected$CpG#6$Coef_hyper$s`: 0 `actual$CpG#7$SSE_hyper`: 72 `expected$CpG#7$SSE_hyper`: 155 `actual$CpG#7$Coef_hyper$a`: -75 `expected$CpG#7$Coef_hyper$a`: 29554653 `actual$CpG#7$Coef_hyper$b`: -773 `expected$CpG#7$Coef_hyper$b`: -57204506 `actual$CpG#7$Coef_hyper$d`: -227 `expected$CpG#7$Coef_hyper$d`: 48548426 `actual$CpG#7$Coef_hyper$R2`: 0.98 `expected$CpG#7$Coef_hyper$R2`: 0.96 `actual$CpG#7$Coef_hyper$b1`: 0.6 `expected$CpG#7$Coef_hyper$b1`: 1.0 `actual$CpG#7$Coef_hyper$s`: 3 `expected$CpG#7$Coef_hyper$s`: 1 `actual$CpG#8$SSE_hyper`: 75 `expected$CpG#8$SSE_hyper`: 112 `actual$CpG#8$Coef_hyper$a`: -278 `expected$CpG#8$Coef_hyper$a`: 38159683 `actual$CpG#8$Coef_hyper$b`: -3873 `expected$CpG#8$Coef_hyper$b`: 263871871 `actual$CpG#8$Coef_hyper$d`: -439 `expected$CpG#8$Coef_hyper$d`: 45284548 `actual$CpG#8$Coef_hyper$R2`: 0.989 `expected$CpG#8$Coef_hyper$R2`: 0.983 `actual$CpG#8$Coef_hyper$b1`: 0.8 `expected$CpG#8$Coef_hyper$b1`: 1.0 `actual$CpG#8$Coef_hyper$s`: 9 `expected$CpG#8$Coef_hyper$s`: 6 `actual$CpG#9$SSE_hyper`: 33 `expected$CpG#9$SSE_hyper`: 352 `actual$CpG#9$Coef_hyper$a`: -71 `expected$CpG#9$Coef_hyper$a`: 51672527 `actual$CpG#9$Coef_hyper$b`: -844 `expected$CpG#9$Coef_hyper$b`: -224404014 `actual$CpG#9$Coef_hyper$d`: -184 `expected$CpG#9$Coef_hyper$d`: 60201205 `actual$CpG#9$Coef_hyper$R2`: 1.00 `expected$CpG#9$Coef_hyper$R2`: 0.95 `actual$CpG#9$Coef_hyper$b1`: 0.5 `expected$CpG#9$Coef_hyper$b1`: 1.0 `actual$CpG#9$Coef_hyper$s`: 4.6 `expected$CpG#9$Coef_hyper$s`: 3.7 `actual$row_means$SSE_hyper`: 35 `expected$row_means$SSE_hyper`: 144 `actual$row_means$Coef_hyper$a`: -128 `expected$row_means$Coef_hyper$a`: 33244771 `actual$row_means$Coef_hyper$b`: -1526 `expected$row_means$Coef_hyper$b`: 29838275 `actual$row_means$Coef_hyper$d`: -263 `expected$row_means$Coef_hyper$d`: 41191067 `actual$row_means$Coef_hyper$R2`: 0.99 `expected$row_means$Coef_hyper$R2`: 0.98 `actual$row_means$Coef_hyper$b1`: 0.6 `expected$row_means$Coef_hyper$b1`: 1.0 `actual$row_means$Coef_hyper$s`: 6 `expected$row_means$Coef_hyper$s`: 1 -- Failure ('test-algorithm_minmax_TRUE.R:71:5'): algorithm test, type 1, minmax = TRUE -- regression_results$result_list (`actual`) not equal to regression_results_fast$result_list (`expected`). `actual$CpG#1$Coef_hyper$b`: 0.5364372 `expected$CpG#1$Coef_hyper$b`: 0.5364373 actual$CpG#1$Coef_cubic$a != expected$CpG#1$Coef_cubic$a but don't know how to show the difference actual$CpG#1$Coef_cubic$b != expected$CpG#1$Coef_cubic$b but don't know how to show the difference actual$CpG#2$Coef_cubic$a != expected$CpG#2$Coef_cubic$a but don't know how to show the difference actual$CpG#2$Coef_cubic$b != expected$CpG#2$Coef_cubic$b but don't know how to show the difference actual$CpG#3$Coef_cubic$a != expected$CpG#3$Coef_cubic$a but don't know how to show the difference `actual$CpG#3$Coef_cubic$b`: -0.00173040 `expected$CpG#3$Coef_cubic$b`: -0.00173041 `actual$CpG#4$Coef_hyper$b`: 0.6263293 `expected$CpG#4$Coef_hyper$b`: 0.6263302 actual$CpG#4$Coef_cubic$a != expected$CpG#4$Coef_cubic$a but don't know how to show the difference actual$CpG#4$Coef_cubic$b != expected$CpG#4$Coef_cubic$b but don't know how to show the difference actual$CpG#5$Coef_cubic$a != expected$CpG#5$Coef_cubic$a but don't know how to show the difference actual$CpG#5$Coef_cubic$b != expected$CpG#5$Coef_cubic$b but don't know how to show the difference actual$CpG#6$Coef_cubic$a != expected$CpG#6$Coef_cubic$a but don't know how to show the difference actual$CpG#6$Coef_cubic$b != expected$CpG#6$Coef_cubic$b but don't know how to show the difference `actual$CpG#7$Coef_hyper$b`: 0.50578129 `expected$CpG#7$Coef_hyper$b`: 0.50578130 actual$CpG#7$Coef_cubic$a != expected$CpG#7$Coef_cubic$a but don't know how to show the difference actual$CpG#7$Coef_cubic$b != expected$CpG#7$Coef_cubic$b but don't know how to show the difference `actual$CpG#8$Coef_hyper$b`: 0.68763945 `expected$CpG#8$Coef_hyper$b`: 0.68763952 actual$CpG#8$Coef_cubic$a != expected$CpG#8$Coef_cubic$a but don't know how to show the difference actual$CpG#8$Coef_cubic$b != expected$CpG#8$Coef_cubic$b but don't know how to show the difference `actual$CpG#9$Coef_hyper$b`: 0.44888828 `expected$CpG#9$Coef_hyper$b`: 0.44888829 actual$CpG#9$Coef_cubic$a != expected$CpG#9$Coef_cubic$a but don't know how to show the difference actual$CpG#9$Coef_cubic$b != expected$CpG#9$Coef_cubic$b but don't know how to show the difference actual$row_means$Coef_cubic$a != expected$row_means$Coef_cubic$a but don't know how to show the difference actual$row_means$Coef_cubic$b != expected$row_means$Coef_cubic$b but don't know how to show the difference -- Failure ('test-algorithm_minmax_TRUE.R:88:5'): algorithm test, type 1, minmax = TRUE -- regression_results$result_list (`actual`) not equal to regression_results_minpack$result_list (`expected`). `actual$CpG#1$Coef_hyper$b`: 0.5364372 `expected$CpG#1$Coef_hyper$b`: 0.5364373 actual$CpG#1$Coef_cubic$a != expected$CpG#1$Coef_cubic$a but don't know how to show the difference actual$CpG#1$Coef_cubic$b != expected$CpG#1$Coef_cubic$b but don't know how to show the difference actual$CpG#2$Coef_cubic$a != expected$CpG#2$Coef_cubic$a but don't know how to show the difference actual$CpG#2$Coef_cubic$b != expected$CpG#2$Coef_cubic$b but don't know how to show the difference `actual$CpG#3$Coef_hyper$b`: 0.6287998 `expected$CpG#3$Coef_hyper$b`: 0.6287994 actual$CpG#3$Coef_cubic$a != expected$CpG#3$Coef_cubic$a but don't know how to show the difference actual$CpG#3$Coef_cubic$b != expected$CpG#3$Coef_cubic$b but don't know how to show the difference `actual$CpG#4$Coef_hyper$b`: 0.6263293 `expected$CpG#4$Coef_hyper$b`: 0.6263302 `actual$CpG#4$Coef_cubic$a`: 0.00004340 `expected$CpG#4$Coef_cubic$a`: 0.00004341 actual$CpG#4$Coef_cubic$b != expected$CpG#4$Coef_cubic$b but don't know how to show the difference `actual$CpG#5$Coef_hyper$b`: 0.63653152 `expected$CpG#5$Coef_hyper$b`: 0.63653153 actual$CpG#5$Coef_cubic$a != expected$CpG#5$Coef_cubic$a but don't know how to show the difference actual$CpG#5$Coef_cubic$b != expected$CpG#5$Coef_cubic$b but don't know how to show the difference actual$CpG#6$Coef_cubic$a != expected$CpG#6$Coef_cubic$a but don't know how to show the difference actual$CpG#6$Coef_cubic$b != expected$CpG#6$Coef_cubic$b but don't know how to show the difference `actual$CpG#7$Coef_hyper$b`: 0.5057813 `expected$CpG#7$Coef_hyper$b`: 0.5057809 actual$CpG#7$Coef_cubic$a != expected$CpG#7$Coef_cubic$a but don't know how to show the difference `actual$CpG#7$Coef_cubic$b`: -0.00511940 `expected$CpG#7$Coef_cubic$b`: -0.00511941 `actual$CpG#8$Coef_hyper$b`: 0.68763945 `expected$CpG#8$Coef_hyper$b`: 0.68763952 actual$CpG#8$Coef_cubic$a != expected$CpG#8$Coef_cubic$a but don't know how to show the difference actual$CpG#8$Coef_cubic$b != expected$CpG#8$Coef_cubic$b but don't know how to show the difference `actual$CpG#9$Coef_hyper$b`: 0.44888828 `expected$CpG#9$Coef_hyper$b`: 0.44888824 actual$CpG#9$Coef_cubic$b != expected$CpG#9$Coef_cubic$b but don't know how to show the difference actual$row_means$Coef_cubic$a != expected$row_means$Coef_cubic$a but don't know how to show the difference `actual$row_means$Coef_cubic$b`: -0.00123898 `expected$row_means$Coef_cubic$b`: -0.00123897 [ FAIL 4 | WARN 209 | SKIP 12 | PASS 42 ] Error: Test failures Execution halted Error in deferred_run(env) : could not find function "deferred_run" Calls: * checking for unstated dependencies in vignettes ... OK * checking package vignettes ... OK * checking re-building of vignette outputs ... OK * checking PDF version of manual ... OK * DONE Status: 1 ERROR See '/tmp/th798/17369309/R-release/1149/rBiasCorrection.Rcheck/00check.log' for details. [1] "2025-05-19 01:06:29 MST" > system(paste(c("diff -u", Rcheck.list), collapse=" ")) --- R_version_4.5.0_2025-04-11/release_1.17.2.Rcheck/00check.log 2025-05-19 00:57:58.159891256 -0700 +++ R_version_4.5.0_2025-04-11/master_1.17.99.c4ea09e099b1833fae0eacb50552bce6f051ed82.Rcheck/00check.log 2025-05-19 01:06:29.742881462 -0700 @@ -70,15 +70,15 @@ > local_edition(3) > > test_check("rBiasCorrection") - [20250519_005047.]: Entered 'clean_dt'-Function - [20250519_005047.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_005047.]: got experimental data - [20250519_005047.]: Entered 'clean_dt'-Function - [20250519_005047.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_005047.]: got calibration data - [20250519_005047.]: + [20250519_005923.]: Entered 'clean_dt'-Function + [20250519_005923.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_005923.]: got experimental data + [20250519_005923.]: Entered 'clean_dt'-Function + [20250519_005923.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_005923.]: got calibration data + [20250519_005923.]: ### Starting with regression calculations ### - [20250519_005047.]: Entered 'regression_type1'-Function + [20250519_005923.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : @@ -603,89 +603,89 @@ singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient - [20250519_005047.]: # CpG-site: CpG#1 + [20250519_005923.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_005047.]: Logging df_agg: CpG#1 - [20250519_005047.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005047.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005047.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005047.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005047.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_005047.]: Entered 'hyperbolic_regression'-Function - [20250519_005047.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005048.]: Entered 'cubic_regression'-Function - [20250519_005048.]: 'cubic_regression': minmax = FALSE - [20250519_005048.]: # CpG-site: CpG#2 + [20250519_005923.]: Logging df_agg: CpG#1 + [20250519_005923.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005923.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005923.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005923.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005923.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) + [20250519_005923.]: Entered 'hyperbolic_regression'-Function + [20250519_005923.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005924.]: Entered 'cubic_regression'-Function + [20250519_005924.]: 'cubic_regression': minmax = FALSE + [20250519_005924.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_005048.]: Logging df_agg: CpG#2 - [20250519_005048.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005048.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005048.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005048.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005048.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_005048.]: Entered 'hyperbolic_regression'-Function - [20250519_005048.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005048.]: Entered 'cubic_regression'-Function - [20250519_005048.]: 'cubic_regression': minmax = FALSE - [20250519_005048.]: # CpG-site: CpG#3 + [20250519_005924.]: Logging df_agg: CpG#2 + [20250519_005924.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005924.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005924.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005924.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005924.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) + [20250519_005924.]: Entered 'hyperbolic_regression'-Function + [20250519_005924.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005924.]: Entered 'cubic_regression'-Function + [20250519_005924.]: 'cubic_regression': minmax = FALSE + [20250519_005924.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_005048.]: Logging df_agg: CpG#3 - [20250519_005048.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005048.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005048.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005048.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005048.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_005048.]: Entered 'hyperbolic_regression'-Function - [20250519_005048.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005048.]: Entered 'cubic_regression'-Function - [20250519_005048.]: 'cubic_regression': minmax = FALSE - [20250519_005048.]: # CpG-site: CpG#4 + [20250519_005924.]: Logging df_agg: CpG#3 + [20250519_005924.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005924.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005924.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005924.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005924.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) + [20250519_005924.]: Entered 'hyperbolic_regression'-Function + [20250519_005924.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005924.]: Entered 'cubic_regression'-Function + [20250519_005924.]: 'cubic_regression': minmax = FALSE + [20250519_005924.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_005048.]: Logging df_agg: CpG#4 - [20250519_005048.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005048.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005048.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005048.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005048.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_005048.]: Entered 'hyperbolic_regression'-Function - [20250519_005048.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005048.]: Entered 'cubic_regression'-Function - [20250519_005048.]: 'cubic_regression': minmax = FALSE - [20250519_005049.]: # CpG-site: CpG#5 + [20250519_005924.]: Logging df_agg: CpG#4 + [20250519_005924.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005924.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005924.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005924.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005924.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) + [20250519_005924.]: Entered 'hyperbolic_regression'-Function + [20250519_005924.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005924.]: Entered 'cubic_regression'-Function + [20250519_005924.]: 'cubic_regression': minmax = FALSE + [20250519_005924.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_005049.]: Logging df_agg: CpG#5 - [20250519_005049.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005049.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005049.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005049.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005049.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_005049.]: Entered 'hyperbolic_regression'-Function - [20250519_005049.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005049.]: Entered 'cubic_regression'-Function - [20250519_005049.]: 'cubic_regression': minmax = FALSE - [20250519_005049.]: # CpG-site: CpG#6 + [20250519_005924.]: Logging df_agg: CpG#5 + [20250519_005924.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005924.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005924.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005924.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005924.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) + [20250519_005924.]: Entered 'hyperbolic_regression'-Function + [20250519_005924.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005925.]: Entered 'cubic_regression'-Function + [20250519_005925.]: 'cubic_regression': minmax = FALSE + [20250519_005925.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_005049.]: Logging df_agg: CpG#6 - [20250519_005049.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005049.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005049.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005049.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005049.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_005049.]: Entered 'hyperbolic_regression'-Function - [20250519_005049.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005049.]: Entered 'cubic_regression'-Function - [20250519_005049.]: 'cubic_regression': minmax = FALSE - [20250519_005049.]: # CpG-site: CpG#7 + [20250519_005925.]: Logging df_agg: CpG#6 + [20250519_005925.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005925.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005925.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005925.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005925.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) + [20250519_005925.]: Entered 'hyperbolic_regression'-Function + [20250519_005925.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005925.]: Entered 'cubic_regression'-Function + [20250519_005925.]: 'cubic_regression': minmax = FALSE + [20250519_005925.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_005049.]: Logging df_agg: CpG#7 - [20250519_005049.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005049.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005049.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005049.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005049.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_005049.]: Entered 'hyperbolic_regression'-Function - [20250519_005049.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005049.]: Entered 'cubic_regression'-Function - [20250519_005049.]: 'cubic_regression': minmax = FALSE - [20250519_005049.]: # CpG-site: CpG#8 + [20250519_005925.]: Logging df_agg: CpG#7 + [20250519_005925.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005925.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005925.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005925.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005925.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) + [20250519_005925.]: Entered 'hyperbolic_regression'-Function + [20250519_005925.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005925.]: Entered 'cubic_regression'-Function + [20250519_005925.]: 'cubic_regression': minmax = FALSE + [20250519_005925.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_005049.]: Logging df_agg: CpG#8 - [20250519_005049.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005049.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005049.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005049.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005049.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_005049.]: Entered 'hyperbolic_regression'-Function - [20250519_005049.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005050.]: Entered 'cubic_regression'-Function - [20250519_005050.]: 'cubic_regression': minmax = FALSE - [20250519_005050.]: # CpG-site: CpG#9 + [20250519_005925.]: Logging df_agg: CpG#8 + [20250519_005925.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005925.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005925.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005925.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005925.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) + [20250519_005925.]: Entered 'hyperbolic_regression'-Function + [20250519_005925.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005925.]: Entered 'cubic_regression'-Function + [20250519_005925.]: 'cubic_regression': minmax = FALSE + [20250519_005925.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_005050.]: Logging df_agg: CpG#9 - [20250519_005050.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005050.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005050.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005050.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005050.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_005050.]: Entered 'hyperbolic_regression'-Function - [20250519_005050.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005050.]: Entered 'cubic_regression'-Function - [20250519_005050.]: 'cubic_regression': minmax = FALSE - [20250519_005050.]: # CpG-site: row_means + [20250519_005925.]: Logging df_agg: CpG#9 + [20250519_005925.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005925.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005925.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005925.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005925.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) + [20250519_005925.]: Entered 'hyperbolic_regression'-Function + [20250519_005925.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005926.]: Entered 'cubic_regression'-Function + [20250519_005926.]: 'cubic_regression': minmax = FALSE + [20250519_005926.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_005050.]: Logging df_agg: row_means - [20250519_005050.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005050.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005050.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_005050.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005050.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_005050.]: Entered 'hyperbolic_regression'-Function - [20250519_005050.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005050.]: Entered 'cubic_regression'-Function - [20250519_005050.]: 'cubic_regression': minmax = FALSE - [20250519_005051.]: + [20250519_005926.]: Logging df_agg: row_means + [20250519_005926.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005926.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005926.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_005926.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005926.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) + [20250519_005926.]: Entered 'hyperbolic_regression'-Function + [20250519_005926.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005926.]: Entered 'cubic_regression'-Function + [20250519_005926.]: 'cubic_regression': minmax = FALSE + [20250519_005927.]: ### Starting with regression calculations ### - [20250519_005051.]: Entered 'regression_type1'-Function + [20250519_005927.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : @@ -1458,170 +1458,170 @@ step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient - [20250519_005051.]: # CpG-site: CpG#1 + [20250519_005927.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_005051.]: Logging df_agg: CpG#1 - [20250519_005051.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005051.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005051.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005051.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005051.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_005051.]: Entered 'hyperbolic_regression'-Function - [20250519_005051.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005051.]: Entered 'cubic_regression'-Function - [20250519_005051.]: 'cubic_regression': minmax = FALSE - [20250519_005051.]: # CpG-site: CpG#2 + [20250519_005927.]: Logging df_agg: CpG#1 + [20250519_005927.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005927.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005927.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005927.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005927.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) + [20250519_005927.]: Entered 'hyperbolic_regression'-Function + [20250519_005927.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005927.]: Entered 'cubic_regression'-Function + [20250519_005927.]: 'cubic_regression': minmax = FALSE + [20250519_005927.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_005051.]: Logging df_agg: CpG#2 - [20250519_005051.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005051.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005051.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005051.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005051.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_005051.]: Entered 'hyperbolic_regression'-Function - [20250519_005051.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005051.]: Entered 'cubic_regression'-Function - [20250519_005051.]: 'cubic_regression': minmax = FALSE - [20250519_005051.]: # CpG-site: CpG#3 + [20250519_005927.]: Logging df_agg: CpG#2 + [20250519_005927.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005927.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005927.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005927.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005927.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) + [20250519_005927.]: Entered 'hyperbolic_regression'-Function + [20250519_005927.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005927.]: Entered 'cubic_regression'-Function + [20250519_005927.]: 'cubic_regression': minmax = FALSE + [20250519_005927.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_005051.]: Logging df_agg: CpG#3 - [20250519_005051.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005051.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005051.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005051.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005051.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_005051.]: Entered 'hyperbolic_regression'-Function - [20250519_005051.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005052.]: Entered 'cubic_regression'-Function - [20250519_005052.]: 'cubic_regression': minmax = FALSE - [20250519_005052.]: # CpG-site: CpG#4 + [20250519_005927.]: Logging df_agg: CpG#3 + [20250519_005927.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005927.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005927.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005927.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005927.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) + [20250519_005927.]: Entered 'hyperbolic_regression'-Function + [20250519_005927.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005927.]: Entered 'cubic_regression'-Function + [20250519_005927.]: 'cubic_regression': minmax = FALSE + [20250519_005927.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_005052.]: Logging df_agg: CpG#4 - [20250519_005052.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005052.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005052.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005052.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005052.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_005052.]: Entered 'hyperbolic_regression'-Function - [20250519_005052.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005052.]: Entered 'cubic_regression'-Function - [20250519_005052.]: 'cubic_regression': minmax = FALSE - [20250519_005052.]: # CpG-site: CpG#5 + [20250519_005927.]: Logging df_agg: CpG#4 + [20250519_005927.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005927.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005927.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005927.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005927.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) + [20250519_005927.]: Entered 'hyperbolic_regression'-Function + [20250519_005927.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005928.]: Entered 'cubic_regression'-Function + [20250519_005928.]: 'cubic_regression': minmax = FALSE + [20250519_005928.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_005052.]: Logging df_agg: CpG#5 - [20250519_005052.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005052.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005052.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005052.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005052.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_005052.]: Entered 'hyperbolic_regression'-Function - [20250519_005052.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005052.]: Entered 'cubic_regression'-Function - [20250519_005052.]: 'cubic_regression': minmax = FALSE - [20250519_005052.]: # CpG-site: CpG#6 + [20250519_005928.]: Logging df_agg: CpG#5 + [20250519_005928.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005928.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005928.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005928.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005928.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) + [20250519_005928.]: Entered 'hyperbolic_regression'-Function + [20250519_005928.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005928.]: Entered 'cubic_regression'-Function + [20250519_005928.]: 'cubic_regression': minmax = FALSE + [20250519_005928.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_005052.]: Logging df_agg: CpG#6 - [20250519_005052.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005052.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005052.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005052.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005052.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_005052.]: Entered 'hyperbolic_regression'-Function - [20250519_005052.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005052.]: Entered 'cubic_regression'-Function - [20250519_005052.]: 'cubic_regression': minmax = FALSE - [20250519_005052.]: # CpG-site: CpG#7 + [20250519_005928.]: Logging df_agg: CpG#6 + [20250519_005928.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005928.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005928.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005928.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005928.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) + [20250519_005928.]: Entered 'hyperbolic_regression'-Function + [20250519_005928.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005928.]: Entered 'cubic_regression'-Function + [20250519_005928.]: 'cubic_regression': minmax = FALSE + [20250519_005928.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_005052.]: Logging df_agg: CpG#7 - [20250519_005052.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005052.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005052.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005052.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005052.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_005052.]: Entered 'hyperbolic_regression'-Function - [20250519_005052.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005052.]: Entered 'cubic_regression'-Function - [20250519_005052.]: 'cubic_regression': minmax = FALSE - [20250519_005052.]: # CpG-site: CpG#8 + [20250519_005928.]: Logging df_agg: CpG#7 + [20250519_005928.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005928.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005928.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005928.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005928.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) + [20250519_005928.]: Entered 'hyperbolic_regression'-Function + [20250519_005928.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005928.]: Entered 'cubic_regression'-Function + [20250519_005928.]: 'cubic_regression': minmax = FALSE + [20250519_005928.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_005052.]: Logging df_agg: CpG#8 - [20250519_005052.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005052.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005052.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005052.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005052.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_005052.]: Entered 'hyperbolic_regression'-Function - [20250519_005052.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005053.]: Entered 'cubic_regression'-Function - [20250519_005053.]: 'cubic_regression': minmax = FALSE - [20250519_005053.]: # CpG-site: CpG#9 + [20250519_005928.]: Logging df_agg: CpG#8 + [20250519_005928.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005928.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005928.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005928.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005928.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) + [20250519_005928.]: Entered 'hyperbolic_regression'-Function + [20250519_005928.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005929.]: Entered 'cubic_regression'-Function + [20250519_005929.]: 'cubic_regression': minmax = FALSE + [20250519_005929.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_005053.]: Logging df_agg: CpG#9 - [20250519_005053.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005053.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005053.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005053.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005053.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_005053.]: Entered 'hyperbolic_regression'-Function - [20250519_005053.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005053.]: Entered 'cubic_regression'-Function - [20250519_005053.]: 'cubic_regression': minmax = FALSE - [20250519_005053.]: # CpG-site: row_means + [20250519_005929.]: Logging df_agg: CpG#9 + [20250519_005929.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005929.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005929.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005929.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005929.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) + [20250519_005929.]: Entered 'hyperbolic_regression'-Function + [20250519_005929.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005929.]: Entered 'cubic_regression'-Function + [20250519_005929.]: 'cubic_regression': minmax = FALSE + [20250519_005929.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_005053.]: Logging df_agg: row_means - [20250519_005053.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005053.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005053.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_005053.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005053.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_005053.]: Entered 'hyperbolic_regression'-Function - [20250519_005053.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005053.]: Entered 'cubic_regression'-Function - [20250519_005053.]: 'cubic_regression': minmax = FALSE - [20250519_005055.]: + [20250519_005929.]: Logging df_agg: row_means + [20250519_005929.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005929.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005929.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_005929.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005929.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) + [20250519_005929.]: Entered 'hyperbolic_regression'-Function + [20250519_005929.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005929.]: Entered 'cubic_regression'-Function + [20250519_005929.]: 'cubic_regression': minmax = FALSE + [20250519_005931.]: ### Starting with regression calculations ### - [20250519_005055.]: Entered 'regression_type1'-Function - [20250519_005055.]: # CpG-site: CpG#1 + [20250519_005931.]: Entered 'regression_type1'-Function + [20250519_005931.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_005055.]: Logging df_agg: CpG#1 - [20250519_005055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005055.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005055.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005055.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005055.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_005055.]: Entered 'hyperbolic_regression'-Function - [20250519_005055.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005055.]: Entered 'cubic_regression'-Function - [20250519_005055.]: 'cubic_regression': minmax = FALSE - [20250519_005055.]: # CpG-site: CpG#2 + [20250519_005931.]: Logging df_agg: CpG#1 + [20250519_005931.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005931.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005931.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005931.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005931.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) + [20250519_005931.]: Entered 'hyperbolic_regression'-Function + [20250519_005931.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005931.]: Entered 'cubic_regression'-Function + [20250519_005931.]: 'cubic_regression': minmax = FALSE + [20250519_005931.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_005055.]: Logging df_agg: CpG#2 - [20250519_005055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005055.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005055.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005055.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005055.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_005055.]: Entered 'hyperbolic_regression'-Function - [20250519_005055.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005055.]: Entered 'cubic_regression'-Function - [20250519_005055.]: 'cubic_regression': minmax = FALSE - [20250519_005055.]: # CpG-site: CpG#3 + [20250519_005931.]: Logging df_agg: CpG#2 + [20250519_005931.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005931.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005931.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005931.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005931.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) + [20250519_005931.]: Entered 'hyperbolic_regression'-Function + [20250519_005931.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005931.]: Entered 'cubic_regression'-Function + [20250519_005931.]: 'cubic_regression': minmax = FALSE + [20250519_005931.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_005055.]: Logging df_agg: CpG#3 - [20250519_005055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005055.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005055.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005055.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005055.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_005055.]: Entered 'hyperbolic_regression'-Function - [20250519_005055.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005055.]: Entered 'cubic_regression'-Function - [20250519_005055.]: 'cubic_regression': minmax = FALSE - [20250519_005055.]: # CpG-site: CpG#4 + [20250519_005931.]: Logging df_agg: CpG#3 + [20250519_005931.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005931.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005931.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005931.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005931.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) + [20250519_005931.]: Entered 'hyperbolic_regression'-Function + [20250519_005931.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005931.]: Entered 'cubic_regression'-Function + [20250519_005931.]: 'cubic_regression': minmax = FALSE + [20250519_005931.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_005055.]: Logging df_agg: CpG#4 - [20250519_005055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005055.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005055.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005055.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005055.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_005055.]: Entered 'hyperbolic_regression'-Function - [20250519_005055.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005055.]: Entered 'cubic_regression'-Function - [20250519_005055.]: 'cubic_regression': minmax = FALSE - [20250519_005055.]: # CpG-site: CpG#5 + [20250519_005931.]: Logging df_agg: CpG#4 + [20250519_005931.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005931.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005931.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005931.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005931.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) + [20250519_005931.]: Entered 'hyperbolic_regression'-Function + [20250519_005931.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005931.]: Entered 'cubic_regression'-Function + [20250519_005931.]: 'cubic_regression': minmax = FALSE + [20250519_005931.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_005055.]: Logging df_agg: CpG#5 - [20250519_005055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005055.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005055.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005055.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005055.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_005055.]: Entered 'hyperbolic_regression'-Function - [20250519_005055.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005055.]: Entered 'cubic_regression'-Function - [20250519_005055.]: 'cubic_regression': minmax = FALSE - [20250519_005055.]: # CpG-site: CpG#6 + [20250519_005931.]: Logging df_agg: CpG#5 + [20250519_005931.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005931.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005931.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005931.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005931.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) + [20250519_005931.]: Entered 'hyperbolic_regression'-Function + [20250519_005931.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005931.]: Entered 'cubic_regression'-Function + [20250519_005931.]: 'cubic_regression': minmax = FALSE + [20250519_005931.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_005055.]: Logging df_agg: CpG#6 - [20250519_005055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005055.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005055.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005055.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005055.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_005055.]: Entered 'hyperbolic_regression'-Function - [20250519_005055.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005055.]: Entered 'cubic_regression'-Function - [20250519_005055.]: 'cubic_regression': minmax = FALSE - [20250519_005055.]: # CpG-site: CpG#7 + [20250519_005931.]: Logging df_agg: CpG#6 + [20250519_005931.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005931.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005931.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005931.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005931.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) + [20250519_005931.]: Entered 'hyperbolic_regression'-Function + [20250519_005931.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005931.]: Entered 'cubic_regression'-Function + [20250519_005931.]: 'cubic_regression': minmax = FALSE + [20250519_005931.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_005055.]: Logging df_agg: CpG#7 - [20250519_005055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005055.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005055.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005055.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005055.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_005055.]: Entered 'hyperbolic_regression'-Function - [20250519_005055.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005055.]: Entered 'cubic_regression'-Function - [20250519_005055.]: 'cubic_regression': minmax = FALSE - [20250519_005055.]: # CpG-site: CpG#8 + [20250519_005931.]: Logging df_agg: CpG#7 + [20250519_005931.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005931.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005931.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005931.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005931.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) + [20250519_005931.]: Entered 'hyperbolic_regression'-Function + [20250519_005931.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005931.]: Entered 'cubic_regression'-Function + [20250519_005931.]: 'cubic_regression': minmax = FALSE + [20250519_005931.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_005055.]: Logging df_agg: CpG#8 - [20250519_005055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005055.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005055.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005055.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005055.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_005055.]: Entered 'hyperbolic_regression'-Function - [20250519_005055.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005055.]: Entered 'cubic_regression'-Function - [20250519_005055.]: 'cubic_regression': minmax = FALSE - [20250519_005055.]: # CpG-site: CpG#9 + [20250519_005931.]: Logging df_agg: CpG#8 + [20250519_005931.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005931.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005931.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005931.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005931.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) + [20250519_005931.]: Entered 'hyperbolic_regression'-Function + [20250519_005931.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005931.]: Entered 'cubic_regression'-Function + [20250519_005931.]: 'cubic_regression': minmax = FALSE + [20250519_005931.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_005055.]: Logging df_agg: CpG#9 - [20250519_005055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005055.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005055.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005055.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005055.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_005055.]: Entered 'hyperbolic_regression'-Function - [20250519_005055.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005055.]: Entered 'cubic_regression'-Function - [20250519_005055.]: 'cubic_regression': minmax = FALSE - [20250519_005055.]: # CpG-site: row_means + [20250519_005931.]: Logging df_agg: CpG#9 + [20250519_005931.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005931.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005931.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005931.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005931.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) + [20250519_005931.]: Entered 'hyperbolic_regression'-Function + [20250519_005931.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005931.]: Entered 'cubic_regression'-Function + [20250519_005931.]: 'cubic_regression': minmax = FALSE + [20250519_005931.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_005055.]: Logging df_agg: row_means - [20250519_005055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005055.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005055.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_005055.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005055.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_005055.]: Entered 'hyperbolic_regression'-Function - [20250519_005055.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005055.]: Entered 'cubic_regression'-Function - [20250519_005055.]: 'cubic_regression': minmax = FALSE - [20250519_005057.]: Entered 'regression_type1'-Function + [20250519_005931.]: Logging df_agg: row_means + [20250519_005931.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005931.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005931.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_005931.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005931.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) + [20250519_005931.]: Entered 'hyperbolic_regression'-Function + [20250519_005931.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005931.]: Entered 'cubic_regression'-Function + [20250519_005931.]: 'cubic_regression': minmax = FALSE + [20250519_005933.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : @@ -2146,95 +2146,95 @@ singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient - [20250519_005057.]: # CpG-site: CpG#1 + [20250519_005933.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_005057.]: Logging df_agg: CpG#1 - [20250519_005057.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005057.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005057.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005057.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005057.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_005057.]: Entered 'hyperbolic_regression'-Function - [20250519_005057.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005057.]: Entered 'cubic_regression'-Function - [20250519_005057.]: 'cubic_regression': minmax = FALSE - [20250519_005057.]: # CpG-site: CpG#2 + [20250519_005933.]: Logging df_agg: CpG#1 + [20250519_005933.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005933.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005933.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005933.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005933.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) + [20250519_005933.]: Entered 'hyperbolic_regression'-Function + [20250519_005933.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005933.]: Entered 'cubic_regression'-Function + [20250519_005933.]: 'cubic_regression': minmax = FALSE + [20250519_005933.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_005057.]: Logging df_agg: CpG#2 - [20250519_005057.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005057.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005057.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005057.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005057.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_005057.]: Entered 'hyperbolic_regression'-Function - [20250519_005057.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005058.]: Entered 'cubic_regression'-Function - [20250519_005058.]: 'cubic_regression': minmax = FALSE - [20250519_005058.]: # CpG-site: CpG#3 + [20250519_005933.]: Logging df_agg: CpG#2 + [20250519_005933.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005933.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005933.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005933.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005933.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) + [20250519_005933.]: Entered 'hyperbolic_regression'-Function + [20250519_005933.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005934.]: Entered 'cubic_regression'-Function + [20250519_005934.]: 'cubic_regression': minmax = FALSE + [20250519_005934.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_005058.]: Logging df_agg: CpG#3 - [20250519_005058.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005058.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005058.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005058.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005058.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_005058.]: Entered 'hyperbolic_regression'-Function - [20250519_005058.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005058.]: Entered 'cubic_regression'-Function - [20250519_005058.]: 'cubic_regression': minmax = FALSE - [20250519_005058.]: # CpG-site: CpG#4 + [20250519_005934.]: Logging df_agg: CpG#3 + [20250519_005934.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005934.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005934.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005934.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005934.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) + [20250519_005934.]: Entered 'hyperbolic_regression'-Function + [20250519_005934.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005934.]: Entered 'cubic_regression'-Function + [20250519_005934.]: 'cubic_regression': minmax = FALSE + [20250519_005934.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_005058.]: Logging df_agg: CpG#4 - [20250519_005058.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005058.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005058.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005058.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005058.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_005058.]: Entered 'hyperbolic_regression'-Function - [20250519_005058.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005058.]: Entered 'cubic_regression'-Function - [20250519_005058.]: 'cubic_regression': minmax = FALSE - [20250519_005058.]: # CpG-site: CpG#5 + [20250519_005934.]: Logging df_agg: CpG#4 + [20250519_005934.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005934.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005934.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005934.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005934.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) + [20250519_005934.]: Entered 'hyperbolic_regression'-Function + [20250519_005934.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005934.]: Entered 'cubic_regression'-Function + [20250519_005934.]: 'cubic_regression': minmax = FALSE + [20250519_005934.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_005058.]: Logging df_agg: CpG#5 - [20250519_005058.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005058.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005058.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005058.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005058.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_005058.]: Entered 'hyperbolic_regression'-Function - [20250519_005058.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005058.]: Entered 'cubic_regression'-Function - [20250519_005058.]: 'cubic_regression': minmax = FALSE - [20250519_005058.]: # CpG-site: CpG#6 + [20250519_005934.]: Logging df_agg: CpG#5 + [20250519_005934.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005934.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005934.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005934.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005934.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) + [20250519_005934.]: Entered 'hyperbolic_regression'-Function + [20250519_005934.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005934.]: Entered 'cubic_regression'-Function + [20250519_005934.]: 'cubic_regression': minmax = FALSE + [20250519_005934.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_005058.]: Logging df_agg: CpG#6 - [20250519_005058.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005058.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005058.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005058.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005058.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_005058.]: Entered 'hyperbolic_regression'-Function - [20250519_005058.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005059.]: Entered 'cubic_regression'-Function - [20250519_005059.]: 'cubic_regression': minmax = FALSE - [20250519_005059.]: # CpG-site: CpG#7 + [20250519_005934.]: Logging df_agg: CpG#6 + [20250519_005934.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005934.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005934.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005934.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005934.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) + [20250519_005934.]: Entered 'hyperbolic_regression'-Function + [20250519_005934.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005934.]: Entered 'cubic_regression'-Function + [20250519_005934.]: 'cubic_regression': minmax = FALSE + [20250519_005935.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_005059.]: Logging df_agg: CpG#7 - [20250519_005059.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005059.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005059.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005059.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005059.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_005059.]: Entered 'hyperbolic_regression'-Function - [20250519_005059.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005059.]: Entered 'cubic_regression'-Function - [20250519_005059.]: 'cubic_regression': minmax = FALSE - [20250519_005059.]: # CpG-site: CpG#8 + [20250519_005935.]: Logging df_agg: CpG#7 + [20250519_005935.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005935.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005935.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005935.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005935.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) + [20250519_005935.]: Entered 'hyperbolic_regression'-Function + [20250519_005935.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005935.]: Entered 'cubic_regression'-Function + [20250519_005935.]: 'cubic_regression': minmax = FALSE + [20250519_005935.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_005059.]: Logging df_agg: CpG#8 - [20250519_005059.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005059.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005059.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005059.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005059.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_005059.]: Entered 'hyperbolic_regression'-Function - [20250519_005059.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005059.]: Entered 'cubic_regression'-Function - [20250519_005059.]: 'cubic_regression': minmax = FALSE - [20250519_005059.]: # CpG-site: CpG#9 + [20250519_005935.]: Logging df_agg: CpG#8 + [20250519_005935.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005935.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005935.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005935.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005935.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) + [20250519_005935.]: Entered 'hyperbolic_regression'-Function + [20250519_005935.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005935.]: Entered 'cubic_regression'-Function + [20250519_005935.]: 'cubic_regression': minmax = FALSE + [20250519_005935.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_005059.]: Logging df_agg: CpG#9 - [20250519_005059.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005059.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005059.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005059.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005059.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_005059.]: Entered 'hyperbolic_regression'-Function - [20250519_005059.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005059.]: Entered 'cubic_regression'-Function - [20250519_005059.]: 'cubic_regression': minmax = FALSE - [20250519_005059.]: # CpG-site: row_means + [20250519_005935.]: Logging df_agg: CpG#9 + [20250519_005935.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005935.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005935.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005935.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005935.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) + [20250519_005935.]: Entered 'hyperbolic_regression'-Function + [20250519_005935.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005935.]: Entered 'cubic_regression'-Function + [20250519_005935.]: 'cubic_regression': minmax = FALSE + [20250519_005935.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_005059.]: Logging df_agg: row_means - [20250519_005059.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005059.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005059.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_005059.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005059.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_005059.]: Entered 'hyperbolic_regression'-Function - [20250519_005059.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005100.]: Entered 'cubic_regression'-Function - [20250519_005100.]: 'cubic_regression': minmax = FALSE - [20250519_005101.]: Entered 'clean_dt'-Function - [20250519_005101.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_005101.]: got experimental data - [20250519_005101.]: Entered 'clean_dt'-Function - [20250519_005101.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_005101.]: got calibration data - [20250519_005101.]: + [20250519_005935.]: Logging df_agg: row_means + [20250519_005935.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005935.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005935.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_005935.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005935.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) + [20250519_005935.]: Entered 'hyperbolic_regression'-Function + [20250519_005935.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005935.]: Entered 'cubic_regression'-Function + [20250519_005935.]: 'cubic_regression': minmax = FALSE + [20250519_005936.]: Entered 'clean_dt'-Function + [20250519_005936.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_005936.]: got experimental data + [20250519_005936.]: Entered 'clean_dt'-Function + [20250519_005936.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_005936.]: got calibration data + [20250519_005936.]: ### Starting with regression calculations ### - [20250519_005101.]: Entered 'regression_type1'-Function + [20250519_005936.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : @@ -2759,87 +2759,87 @@ singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient - [20250519_005101.]: # CpG-site: CpG#1 + [20250519_005936.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_005101.]: Logging df_agg: CpG#1 - [20250519_005101.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005101.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005101.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005101.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005101.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_005101.]: Entered 'hyperbolic_regression'-Function - [20250519_005101.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005101.]: Entered 'cubic_regression'-Function - [20250519_005101.]: 'cubic_regression': minmax = FALSE - [20250519_005101.]: # CpG-site: CpG#2 + [20250519_005937.]: Logging df_agg: CpG#1 + [20250519_005937.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005937.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005937.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005937.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005937.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) + [20250519_005937.]: Entered 'hyperbolic_regression'-Function + [20250519_005937.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005937.]: Entered 'cubic_regression'-Function + [20250519_005937.]: 'cubic_regression': minmax = FALSE + [20250519_005937.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_005101.]: Logging df_agg: CpG#2 - [20250519_005101.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005101.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005101.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005101.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005101.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_005101.]: Entered 'hyperbolic_regression'-Function - [20250519_005101.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005101.]: Entered 'cubic_regression'-Function - [20250519_005101.]: 'cubic_regression': minmax = FALSE - [20250519_005101.]: # CpG-site: CpG#3 + [20250519_005937.]: Logging df_agg: CpG#2 + [20250519_005937.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005937.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005937.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005937.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005937.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) + [20250519_005937.]: Entered 'hyperbolic_regression'-Function + [20250519_005937.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005937.]: Entered 'cubic_regression'-Function + [20250519_005937.]: 'cubic_regression': minmax = FALSE + [20250519_005937.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_005101.]: Logging df_agg: CpG#3 - [20250519_005101.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005101.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005101.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005101.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005101.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_005101.]: Entered 'hyperbolic_regression'-Function - [20250519_005101.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005101.]: Entered 'cubic_regression'-Function - [20250519_005101.]: 'cubic_regression': minmax = FALSE - [20250519_005101.]: # CpG-site: CpG#4 + [20250519_005937.]: Logging df_agg: CpG#3 + [20250519_005937.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005937.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005937.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005937.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005937.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) + [20250519_005937.]: Entered 'hyperbolic_regression'-Function + [20250519_005937.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005937.]: Entered 'cubic_regression'-Function + [20250519_005937.]: 'cubic_regression': minmax = FALSE + [20250519_005937.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_005101.]: Logging df_agg: CpG#4 - [20250519_005101.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005101.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005101.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005101.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005101.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_005101.]: Entered 'hyperbolic_regression'-Function - [20250519_005101.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005102.]: Entered 'cubic_regression'-Function - [20250519_005102.]: 'cubic_regression': minmax = FALSE - [20250519_005102.]: # CpG-site: CpG#5 + [20250519_005937.]: Logging df_agg: CpG#4 + [20250519_005937.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005937.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005937.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005937.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005937.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) + [20250519_005937.]: Entered 'hyperbolic_regression'-Function + [20250519_005937.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005937.]: Entered 'cubic_regression'-Function + [20250519_005937.]: 'cubic_regression': minmax = FALSE + [20250519_005937.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_005102.]: Logging df_agg: CpG#5 - [20250519_005102.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005102.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005102.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005102.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005102.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_005102.]: Entered 'hyperbolic_regression'-Function - [20250519_005102.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005102.]: Entered 'cubic_regression'-Function - [20250519_005102.]: 'cubic_regression': minmax = FALSE - [20250519_005102.]: # CpG-site: CpG#6 + [20250519_005937.]: Logging df_agg: CpG#5 + [20250519_005937.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005937.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005937.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005937.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005937.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) + [20250519_005937.]: Entered 'hyperbolic_regression'-Function + [20250519_005937.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005938.]: Entered 'cubic_regression'-Function + [20250519_005938.]: 'cubic_regression': minmax = FALSE + [20250519_005938.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_005102.]: Logging df_agg: CpG#6 - [20250519_005102.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005102.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005102.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005102.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005102.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_005102.]: Entered 'hyperbolic_regression'-Function - [20250519_005102.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005102.]: Entered 'cubic_regression'-Function - [20250519_005102.]: 'cubic_regression': minmax = FALSE - [20250519_005102.]: # CpG-site: CpG#7 + [20250519_005938.]: Logging df_agg: CpG#6 + [20250519_005938.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005938.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005938.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005938.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005938.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) + [20250519_005938.]: Entered 'hyperbolic_regression'-Function + [20250519_005938.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005938.]: Entered 'cubic_regression'-Function + [20250519_005938.]: 'cubic_regression': minmax = FALSE + [20250519_005938.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_005102.]: Logging df_agg: CpG#7 - [20250519_005102.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005102.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005102.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005102.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005102.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_005102.]: Entered 'hyperbolic_regression'-Function - [20250519_005102.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005102.]: Entered 'cubic_regression'-Function - [20250519_005102.]: 'cubic_regression': minmax = FALSE - [20250519_005102.]: # CpG-site: CpG#8 + [20250519_005938.]: Logging df_agg: CpG#7 + [20250519_005938.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005938.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005938.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005938.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005938.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) + [20250519_005938.]: Entered 'hyperbolic_regression'-Function + [20250519_005938.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005938.]: Entered 'cubic_regression'-Function + [20250519_005938.]: 'cubic_regression': minmax = FALSE + [20250519_005938.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_005102.]: Logging df_agg: CpG#8 - [20250519_005102.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005102.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005102.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005102.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005102.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_005102.]: Entered 'hyperbolic_regression'-Function - [20250519_005102.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005103.]: Entered 'cubic_regression'-Function - [20250519_005103.]: 'cubic_regression': minmax = FALSE - [20250519_005103.]: # CpG-site: CpG#9 + [20250519_005938.]: Logging df_agg: CpG#8 + [20250519_005938.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005938.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005938.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005938.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005938.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) + [20250519_005938.]: Entered 'hyperbolic_regression'-Function + [20250519_005938.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005938.]: Entered 'cubic_regression'-Function + [20250519_005938.]: 'cubic_regression': minmax = FALSE + [20250519_005938.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_005103.]: Logging df_agg: CpG#9 - [20250519_005103.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005103.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005103.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005103.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005103.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_005103.]: Entered 'hyperbolic_regression'-Function - [20250519_005103.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005103.]: Entered 'cubic_regression'-Function - [20250519_005103.]: 'cubic_regression': minmax = FALSE - [20250519_005103.]: # CpG-site: row_means + [20250519_005938.]: Logging df_agg: CpG#9 + [20250519_005938.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005938.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005938.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005938.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005938.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) + [20250519_005938.]: Entered 'hyperbolic_regression'-Function + [20250519_005938.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005939.]: Entered 'cubic_regression'-Function + [20250519_005939.]: 'cubic_regression': minmax = FALSE + [20250519_005939.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_005103.]: Logging df_agg: row_means - [20250519_005103.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005103.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005103.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_005103.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005103.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_005103.]: Entered 'hyperbolic_regression'-Function - [20250519_005103.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005103.]: Entered 'cubic_regression'-Function - [20250519_005103.]: 'cubic_regression': minmax = FALSE - [20250519_005104.]: Entered 'regression_type1'-Function + [20250519_005939.]: Logging df_agg: row_means + [20250519_005939.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005939.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005939.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_005939.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005939.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) + [20250519_005939.]: Entered 'hyperbolic_regression'-Function + [20250519_005939.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005939.]: Entered 'cubic_regression'-Function + [20250519_005939.]: 'cubic_regression': minmax = FALSE + [20250519_005939.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : @@ -3364,90 +3364,90 @@ singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient - [20250519_005104.]: # CpG-site: CpG#1 + [20250519_005939.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_005104.]: Logging df_agg: CpG#1 - [20250519_005104.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005104.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005104.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005104.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005104.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_005104.]: Entered 'hyperbolic_regression'-Function - [20250519_005104.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005104.]: Entered 'cubic_regression'-Function - [20250519_005104.]: 'cubic_regression': minmax = FALSE - [20250519_005104.]: # CpG-site: CpG#2 + [20250519_005939.]: Logging df_agg: CpG#1 + [20250519_005939.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005939.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005939.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005939.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005939.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) + [20250519_005939.]: Entered 'hyperbolic_regression'-Function + [20250519_005939.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005940.]: Entered 'cubic_regression'-Function + [20250519_005940.]: 'cubic_regression': minmax = FALSE + [20250519_005940.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_005104.]: Logging df_agg: CpG#2 - [20250519_005104.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005104.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005104.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005104.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005104.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_005104.]: Entered 'hyperbolic_regression'-Function - [20250519_005104.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005104.]: Entered 'cubic_regression'-Function - [20250519_005104.]: 'cubic_regression': minmax = FALSE - [20250519_005104.]: # CpG-site: CpG#3 + [20250519_005940.]: Logging df_agg: CpG#2 + [20250519_005940.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005940.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005940.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005940.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005940.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) + [20250519_005940.]: Entered 'hyperbolic_regression'-Function + [20250519_005940.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005940.]: Entered 'cubic_regression'-Function + [20250519_005940.]: 'cubic_regression': minmax = FALSE + [20250519_005940.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_005104.]: Logging df_agg: CpG#3 - [20250519_005104.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005104.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005104.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005104.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005104.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_005104.]: Entered 'hyperbolic_regression'-Function - [20250519_005104.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005104.]: Entered 'cubic_regression'-Function - [20250519_005104.]: 'cubic_regression': minmax = FALSE - [20250519_005104.]: # CpG-site: CpG#4 + [20250519_005940.]: Logging df_agg: CpG#3 + [20250519_005940.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005940.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005940.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005940.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005940.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) + [20250519_005940.]: Entered 'hyperbolic_regression'-Function + [20250519_005940.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005940.]: Entered 'cubic_regression'-Function + [20250519_005940.]: 'cubic_regression': minmax = FALSE + [20250519_005940.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_005104.]: Logging df_agg: CpG#4 - [20250519_005104.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005104.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005104.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005104.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005104.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_005104.]: Entered 'hyperbolic_regression'-Function - [20250519_005104.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005105.]: Entered 'cubic_regression'-Function - [20250519_005105.]: 'cubic_regression': minmax = FALSE - [20250519_005105.]: # CpG-site: CpG#5 + [20250519_005940.]: Logging df_agg: CpG#4 + [20250519_005940.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005940.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005940.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005940.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005940.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) + [20250519_005940.]: Entered 'hyperbolic_regression'-Function + [20250519_005940.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005940.]: Entered 'cubic_regression'-Function + [20250519_005940.]: 'cubic_regression': minmax = FALSE + [20250519_005940.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_005105.]: Logging df_agg: CpG#5 - [20250519_005105.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005105.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005105.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005105.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005105.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_005105.]: Entered 'hyperbolic_regression'-Function - [20250519_005105.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005105.]: Entered 'cubic_regression'-Function - [20250519_005105.]: 'cubic_regression': minmax = FALSE - [20250519_005105.]: # CpG-site: CpG#6 + [20250519_005940.]: Logging df_agg: CpG#5 + [20250519_005940.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005940.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005940.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005940.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005940.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) + [20250519_005940.]: Entered 'hyperbolic_regression'-Function + [20250519_005940.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005941.]: Entered 'cubic_regression'-Function + [20250519_005941.]: 'cubic_regression': minmax = FALSE + [20250519_005941.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_005105.]: Logging df_agg: CpG#6 - [20250519_005105.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005105.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005105.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005105.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005105.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_005105.]: Entered 'hyperbolic_regression'-Function - [20250519_005105.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005105.]: Entered 'cubic_regression'-Function - [20250519_005105.]: 'cubic_regression': minmax = FALSE - [20250519_005105.]: # CpG-site: CpG#7 + [20250519_005941.]: Logging df_agg: CpG#6 + [20250519_005941.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005941.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005941.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005941.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005941.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) + [20250519_005941.]: Entered 'hyperbolic_regression'-Function + [20250519_005941.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005941.]: Entered 'cubic_regression'-Function + [20250519_005941.]: 'cubic_regression': minmax = FALSE + [20250519_005941.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_005105.]: Logging df_agg: CpG#7 - [20250519_005105.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005105.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005105.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005105.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005105.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_005105.]: Entered 'hyperbolic_regression'-Function - [20250519_005105.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005105.]: Entered 'cubic_regression'-Function - [20250519_005105.]: 'cubic_regression': minmax = FALSE - [20250519_005105.]: # CpG-site: CpG#8 + [20250519_005941.]: Logging df_agg: CpG#7 + [20250519_005941.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005941.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005941.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005941.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005941.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) + [20250519_005941.]: Entered 'hyperbolic_regression'-Function + [20250519_005941.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005941.]: Entered 'cubic_regression'-Function + [20250519_005941.]: 'cubic_regression': minmax = FALSE + [20250519_005941.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_005105.]: Logging df_agg: CpG#8 - [20250519_005105.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005105.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005105.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005105.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005105.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_005105.]: Entered 'hyperbolic_regression'-Function - [20250519_005105.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005106.]: Entered 'cubic_regression'-Function - [20250519_005106.]: 'cubic_regression': minmax = FALSE - [20250519_005106.]: # CpG-site: CpG#9 + [20250519_005941.]: Logging df_agg: CpG#8 + [20250519_005941.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005941.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005941.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005941.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005941.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) + [20250519_005941.]: Entered 'hyperbolic_regression'-Function + [20250519_005941.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005941.]: Entered 'cubic_regression'-Function + [20250519_005941.]: 'cubic_regression': minmax = FALSE + [20250519_005941.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_005106.]: Logging df_agg: CpG#9 - [20250519_005106.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005106.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005106.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005106.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005106.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_005106.]: Entered 'hyperbolic_regression'-Function - [20250519_005106.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005106.]: Entered 'cubic_regression'-Function - [20250519_005106.]: 'cubic_regression': minmax = FALSE - [20250519_005106.]: # CpG-site: row_means + [20250519_005941.]: Logging df_agg: CpG#9 + [20250519_005941.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005941.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005941.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005941.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005941.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) + [20250519_005941.]: Entered 'hyperbolic_regression'-Function + [20250519_005941.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005942.]: Entered 'cubic_regression'-Function + [20250519_005942.]: 'cubic_regression': minmax = FALSE + [20250519_005942.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_005106.]: Logging df_agg: row_means - [20250519_005106.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005106.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005106.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_005106.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005106.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_005106.]: Entered 'hyperbolic_regression'-Function - [20250519_005106.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005106.]: Entered 'cubic_regression'-Function - [20250519_005106.]: 'cubic_regression': minmax = FALSE - [20250519_005107.]: Entered 'solving_equations'-Function - [20250519_005107.]: Solving hyperbolic regression for CpG#1 + [20250519_005942.]: Logging df_agg: row_means + [20250519_005942.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005942.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005942.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_005942.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005942.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) + [20250519_005942.]: Entered 'hyperbolic_regression'-Function + [20250519_005942.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005942.]: Entered 'cubic_regression'-Function + [20250519_005942.]: 'cubic_regression': minmax = FALSE + [20250519_005942.]: Entered 'solving_equations'-Function + [20250519_005942.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: -2.23222837469517 - [20250519_005107.]: Samplename: 0 + [20250519_005942.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. @@ -3455,126 +3455,126 @@ Root: -2.232 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.1698391693126 - [20250519_005107.]: Samplename: 12.5 + [20250519_005942.]: Samplename: 12.5 Root: 12.17 --> Root in between the borders! Added to results. Hyperbolic solved: 24.4781729791561 - [20250519_005107.]: Samplename: 25 + [20250519_005942.]: Samplename: 25 Root: 24.478 --> Root in between the borders! Added to results. Hyperbolic solved: 38.1730159074047 - [20250519_005107.]: Samplename: 37.5 + [20250519_005942.]: Samplename: 37.5 Root: 38.173 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3348987967717 - [20250519_005107.]: Samplename: 50 + [20250519_005942.]: Samplename: 50 Root: 52.335 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4582305966058 - [20250519_005107.]: Samplename: 62.5 + [20250519_005942.]: Samplename: 62.5 Root: 65.458 --> Root in between the borders! Added to results. Hyperbolic solved: 75.0090269722885 - [20250519_005107.]: Samplename: 75 + [20250519_005942.]: Samplename: 75 Root: 75.009 --> Root in between the borders! Added to results. Hyperbolic solved: 81.527135738891 - [20250519_005107.]: Samplename: 87.5 + [20250519_005942.]: Samplename: 87.5 Root: 81.527 --> Root in between the borders! Added to results. Hyperbolic solved: 102.400825350044 - [20250519_005107.]: Samplename: 100 + [20250519_005942.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.401 --> '100 < root < 110' --> substitute 100 - [20250519_005107.]: Solving hyperbolic regression for CpG#2 + [20250519_005942.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 1.13663087266051 - [20250519_005107.]: Samplename: 0 + [20250519_005942.]: Samplename: 0 Root: 1.137 --> Root in between the borders! Added to results. Hyperbolic solved: 11.4130277963576 - [20250519_005107.]: Samplename: 12.5 + [20250519_005942.]: Samplename: 12.5 Root: 11.413 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1741025868157 - [20250519_005107.]: Samplename: 25 + [20250519_005942.]: Samplename: 25 Root: 26.174 --> Root in between the borders! Added to results. Hyperbolic solved: 35.1051721736724 - [20250519_005107.]: Samplename: 37.5 + [20250519_005942.]: Samplename: 37.5 Root: 35.105 --> Root in between the borders! Added to results. Hyperbolic solved: 47.6856613240114 - [20250519_005107.]: Samplename: 50 + [20250519_005942.]: Samplename: 50 Root: 47.686 --> Root in between the borders! Added to results. Hyperbolic solved: 67.1442585354696 - [20250519_005107.]: Samplename: 62.5 + [20250519_005942.]: Samplename: 62.5 Root: 67.144 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7646957109439 - [20250519_005107.]: Samplename: 75 + [20250519_005942.]: Samplename: 75 Root: 75.765 --> Root in between the borders! Added to results. Hyperbolic solved: 84.4056634834815 - [20250519_005107.]: Samplename: 87.5 + [20250519_005942.]: Samplename: 87.5 Root: 84.406 --> Root in between the borders! Added to results. Hyperbolic solved: 100.94855337669 - [20250519_005107.]: Samplename: 100 + [20250519_005942.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.949 --> '100 < root < 110' --> substitute 100 - [20250519_005107.]: Solving hyperbolic regression for CpG#3 + [20250519_005942.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 0.512348257854635 - [20250519_005107.]: Samplename: 0 + [20250519_005942.]: Samplename: 0 Root: 0.512 --> Root in between the borders! Added to results. Hyperbolic solved: 10.7523476241811 - [20250519_005107.]: Samplename: 12.5 + [20250519_005942.]: Samplename: 12.5 Root: 10.752 --> Root in between the borders! Added to results. Hyperbolic solved: 25.5218062516799 - [20250519_005107.]: Samplename: 25 + [20250519_005942.]: Samplename: 25 Root: 25.522 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5269315729339 - [20250519_005107.]: Samplename: 37.5 + [20250519_005942.]: Samplename: 37.5 Root: 36.527 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7907738274884 - [20250519_005107.]: Samplename: 50 + [20250519_005942.]: Samplename: 50 Root: 50.791 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8684489988589 - [20250519_005107.]: Samplename: 62.5 + [20250519_005942.]: Samplename: 62.5 Root: 64.868 --> Root in between the borders! Added to results. Hyperbolic solved: 77.5522101073979 - [20250519_005107.]: Samplename: 75 + [20250519_005942.]: Samplename: 75 Root: 77.552 --> Root in between the borders! Added to results. Hyperbolic solved: 80.4372474691229 - [20250519_005107.]: Samplename: 87.5 + [20250519_005942.]: Samplename: 87.5 Root: 80.437 --> Root in between the borders! Added to results. Hyperbolic solved: 102.703772810881 - [20250519_005107.]: Samplename: 100 + [20250519_005942.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.704 --> '100 < root < 110' --> substitute 100 - [20250519_005107.]: Solving hyperbolic regression for CpG#4 + [20250519_005942.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: -0.519513573026794 - [20250519_005107.]: Samplename: 0 + [20250519_005942.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. @@ -3582,126 +3582,126 @@ Root: -0.52 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.4933760051342 - [20250519_005107.]: Samplename: 12.5 + [20250519_005943.]: Samplename: 12.5 Root: 12.493 --> Root in between the borders! Added to results. Hyperbolic solved: 24.2684789512555 - [20250519_005107.]: Samplename: 25 + [20250519_005943.]: Samplename: 25 Root: 24.268 --> Root in between the borders! Added to results. Hyperbolic solved: 38.0816229393195 - [20250519_005107.]: Samplename: 37.5 + [20250519_005943.]: Samplename: 37.5 Root: 38.082 --> Root in between the borders! Added to results. Hyperbolic solved: 48.5842089589187 - [20250519_005107.]: Samplename: 50 + [20250519_005943.]: Samplename: 50 Root: 48.584 --> Root in between the borders! Added to results. Hyperbolic solved: 67.6720983549562 - [20250519_005107.]: Samplename: 62.5 + [20250519_005943.]: Samplename: 62.5 Root: 67.672 --> Root in between the borders! Added to results. Hyperbolic solved: 74.1547759694059 - [20250519_005107.]: Samplename: 75 + [20250519_005943.]: Samplename: 75 Root: 74.155 --> Root in between the borders! Added to results. Hyperbolic solved: 82.8820147905386 - [20250519_005107.]: Samplename: 87.5 + [20250519_005943.]: Samplename: 87.5 Root: 82.882 --> Root in between the borders! Added to results. Hyperbolic solved: 102.078935346876 - [20250519_005107.]: Samplename: 100 + [20250519_005943.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.079 --> '100 < root < 110' --> substitute 100 - [20250519_005107.]: Solving hyperbolic regression for CpG#5 + [20250519_005943.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 2.41558040143479 - [20250519_005107.]: Samplename: 0 + [20250519_005943.]: Samplename: 0 Root: 2.416 --> Root in between the borders! Added to results. Hyperbolic solved: 10.1649584830834 - [20250519_005107.]: Samplename: 12.5 + [20250519_005943.]: Samplename: 12.5 Root: 10.165 --> Root in between the borders! Added to results. Hyperbolic solved: 23.9830670020905 - [20250519_005107.]: Samplename: 25 + [20250519_005943.]: Samplename: 25 Root: 23.983 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2773406385708 - [20250519_005107.]: Samplename: 37.5 + [20250519_005943.]: Samplename: 37.5 Root: 37.277 --> Root in between the borders! Added to results. Hyperbolic solved: 50.8659103346102 - [20250519_005107.]: Samplename: 50 + [20250519_005943.]: Samplename: 50 Root: 50.866 --> Root in between the borders! Added to results. Hyperbolic solved: 62.4341926937382 - [20250519_005107.]: Samplename: 62.5 + [20250519_005943.]: Samplename: 62.5 Root: 62.434 --> Root in between the borders! Added to results. Hyperbolic solved: 76.3914832329149 - [20250519_005107.]: Samplename: 75 + [20250519_005943.]: Samplename: 75 Root: 76.391 --> Root in between the borders! Added to results. Hyperbolic solved: 86.1597399215782 - [20250519_005107.]: Samplename: 87.5 + [20250519_005943.]: Samplename: 87.5 Root: 86.16 --> Root in between the borders! Added to results. Hyperbolic solved: 100.267701841999 - [20250519_005107.]: Samplename: 100 + [20250519_005943.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.268 --> '100 < root < 110' --> substitute 100 - [20250519_005107.]: Solving hyperbolic regression for CpG#6 + [20250519_005943.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 0.138179963459196 - [20250519_005107.]: Samplename: 0 + [20250519_005943.]: Samplename: 0 Root: 0.138 --> Root in between the borders! Added to results. Hyperbolic solved: 11.8635989635202 - [20250519_005107.]: Samplename: 12.5 + [20250519_005943.]: Samplename: 12.5 Root: 11.864 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5108198460409 - [20250519_005107.]: Samplename: 25 + [20250519_005943.]: Samplename: 25 Root: 26.511 --> Root in between the borders! Added to results. Hyperbolic solved: 35.3206004021833 - [20250519_005107.]: Samplename: 37.5 + [20250519_005943.]: Samplename: 37.5 Root: 35.321 --> Root in between the borders! Added to results. Hyperbolic solved: 50.0571987473308 - [20250519_005107.]: Samplename: 50 + [20250519_005943.]: Samplename: 50 Root: 50.057 --> Root in between the borders! Added to results. Hyperbolic solved: 64.9604436607063 - [20250519_005107.]: Samplename: 62.5 + [20250519_005943.]: Samplename: 62.5 Root: 64.96 --> Root in between the borders! Added to results. Hyperbolic solved: 73.6690699266294 - [20250519_005107.]: Samplename: 75 + [20250519_005943.]: Samplename: 75 Root: 73.669 --> Root in between the borders! Added to results. Hyperbolic solved: 87.1267946879604 - [20250519_005107.]: Samplename: 87.5 + [20250519_005943.]: Samplename: 87.5 Root: 87.127 --> Root in between the borders! Added to results. Hyperbolic solved: 100.261842767865 - [20250519_005107.]: Samplename: 100 + [20250519_005943.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.262 --> '100 < root < 110' --> substitute 100 - [20250519_005107.]: Solving hyperbolic regression for CpG#7 + [20250519_005943.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: -1.37239249181669 - [20250519_005107.]: Samplename: 0 + [20250519_005943.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. @@ -3709,85 +3709,85 @@ Root: -1.372 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.199366215418 - [20250519_005107.]: Samplename: 12.5 + [20250519_005943.]: Samplename: 12.5 Root: 10.199 --> Root in between the borders! Added to results. Hyperbolic solved: 24.595295361327 - [20250519_005107.]: Samplename: 25 + [20250519_005943.]: Samplename: 25 Root: 24.595 --> Root in between the borders! Added to results. Hyperbolic solved: 37.8312095633391 - [20250519_005107.]: Samplename: 37.5 + [20250519_005943.]: Samplename: 37.5 Root: 37.831 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5590896582852 - [20250519_005107.]: Samplename: 50 + [20250519_005943.]: Samplename: 50 Root: 53.559 --> Root in between the borders! Added to results. Hyperbolic solved: 65.9367389282431 - [20250519_005107.]: Samplename: 62.5 + [20250519_005943.]: Samplename: 62.5 Root: 65.937 --> Root in between the borders! Added to results. Hyperbolic solved: 75.736370152841 - [20250519_005107.]: Samplename: 75 + [20250519_005943.]: Samplename: 75 Root: 75.736 --> Root in between the borders! Added to results. Hyperbolic solved: 79.433089359524 - [20250519_005107.]: Samplename: 87.5 + [20250519_005943.]: Samplename: 87.5 Root: 79.433 --> Root in between the borders! Added to results. Hyperbolic solved: 103.004516225662 - [20250519_005107.]: Samplename: 100 + [20250519_005943.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 103.005 --> '100 < root < 110' --> substitute 100 - [20250519_005107.]: Solving hyperbolic regression for CpG#8 + [20250519_005943.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 2.80067549526643 - [20250519_005107.]: Samplename: 0 + [20250519_005943.]: Samplename: 0 Root: 2.801 --> Root in between the borders! Added to results. Hyperbolic solved: 9.27534686003087 - [20250519_005107.]: Samplename: 12.5 + [20250519_005943.]: Samplename: 12.5 Root: 9.275 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4762624089952 - [20250519_005107.]: Samplename: 25 + [20250519_005943.]: Samplename: 25 Root: 25.476 --> Root in between the borders! Added to results. Hyperbolic solved: 34.0122098045585 - [20250519_005107.]: Samplename: 37.5 + [20250519_005943.]: Samplename: 37.5 Root: 34.012 --> Root in between the borders! Added to results. Hyperbolic solved: 51.784270967861 - [20250519_005107.]: Samplename: 50 + [20250519_005943.]: Samplename: 50 Root: 51.784 --> Root in between the borders! Added to results. Hyperbolic solved: 64.6732380258789 - [20250519_005107.]: Samplename: 62.5 + [20250519_005943.]: Samplename: 62.5 Root: 64.673 --> Root in between the borders! Added to results. Hyperbolic solved: 78.4327054483192 - [20250519_005107.]: Samplename: 75 + [20250519_005943.]: Samplename: 75 Root: 78.433 --> Root in between the borders! Added to results. Hyperbolic solved: 81.3427308951795 - [20250519_005107.]: Samplename: 87.5 + [20250519_005943.]: Samplename: 87.5 Root: 81.343 --> Root in between the borders! Added to results. Hyperbolic solved: 101.964413523995 - [20250519_005107.]: Samplename: 100 + [20250519_005943.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.964 --> '100 < root < 110' --> substitute 100 - [20250519_005107.]: Solving hyperbolic regression for CpG#9 + [20250519_005943.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: -2.13402763663736 - [20250519_005107.]: Samplename: 0 + [20250519_005943.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. @@ -3795,85 +3795,85 @@ Root: -2.134 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.5081558897243 - [20250519_005107.]: Samplename: 12.5 + [20250519_005943.]: Samplename: 12.5 Root: 10.508 --> Root in between the borders! Added to results. Hyperbolic solved: 26.9163121098627 - [20250519_005107.]: Samplename: 25 + [20250519_005943.]: Samplename: 25 Root: 26.916 --> Root in between the borders! Added to results. Hyperbolic solved: 36.8332915301825 - [20250519_005107.]: Samplename: 37.5 + [20250519_005943.]: Samplename: 37.5 Root: 36.833 --> Root in between the borders! Added to results. Hyperbolic solved: 52.00955 - [20250519_005107.]: Samplename: 50 + [20250519_005943.]: Samplename: 50 Root: 52.01 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8927781893359 - [20250519_005107.]: Samplename: 62.5 + [20250519_005943.]: Samplename: 62.5 Root: 64.893 --> Root in between the borders! Added to results. Hyperbolic solved: 74.566810799042 - [20250519_005107.]: Samplename: 75 + [20250519_005943.]: Samplename: 75 Root: 74.567 --> Root in between the borders! Added to results. Hyperbolic solved: 84.5291854856128 - [20250519_005107.]: Samplename: 87.5 + [20250519_005943.]: Samplename: 87.5 Root: 84.529 --> Root in between the borders! Added to results. Hyperbolic solved: 101.046823491232 - [20250519_005107.]: Samplename: 100 + [20250519_005943.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.047 --> '100 < root < 110' --> substitute 100 - [20250519_005107.]: Solving hyperbolic regression for row_means + [20250519_005943.]: Solving hyperbolic regression for row_means Hyperbolic solved: 0.290936038655697 - [20250519_005107.]: Samplename: 0 + [20250519_005943.]: Samplename: 0 Root: 0.291 --> Root in between the borders! Added to results. Hyperbolic solved: 11.0412364555656 - [20250519_005107.]: Samplename: 12.5 + [20250519_005943.]: Samplename: 12.5 Root: 11.041 --> Root in between the borders! Added to results. Hyperbolic solved: 25.408146358228 - [20250519_005107.]: Samplename: 25 + [20250519_005943.]: Samplename: 25 Root: 25.408 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5243683648753 - [20250519_005107.]: Samplename: 37.5 + [20250519_005943.]: Samplename: 37.5 Root: 36.524 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7348788337891 - [20250519_005107.]: Samplename: 50 + [20250519_005943.]: Samplename: 50 Root: 50.735 --> Root in between the borders! Added to results. Hyperbolic solved: 65.3135169308955 - [20250519_005107.]: Samplename: 62.5 + [20250519_005943.]: Samplename: 62.5 Root: 65.314 --> Root in between the borders! Added to results. Hyperbolic solved: 75.5342663138126 - [20250519_005107.]: Samplename: 75 + [20250519_005943.]: Samplename: 75 Root: 75.534 --> Root in between the borders! Added to results. Hyperbolic solved: 83.2411177153911 - [20250519_005107.]: Samplename: 87.5 + [20250519_005943.]: Samplename: 87.5 Root: 83.241 --> Root in between the borders! Added to results. Hyperbolic solved: 101.666935931185 - [20250519_005107.]: Samplename: 100 + [20250519_005943.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.667 --> '100 < root < 110' --> substitute 100 - [20250519_005107.]: + [20250519_005943.]: ### Starting with regression calculations ### - [20250519_005107.]: Entered 'regression_type1'-Function + [20250519_005943.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : @@ -4368,202 +4368,202 @@ singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient - [20250519_005107.]: # CpG-site: CpG#1 + [20250519_005943.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.1698391693126, 24.4781729791561, 38.1730159074047, 52.3348987967717, 65.4582305966058, 75.0090269722885, 81.527135738891, 100)c(0, 0.330160830687401, 0.521827020843901, 0.673015907404697, 2.3348987967717, 2.9582305966058, 0.00902697228849547, 5.972864261109, 0)c(NA, 2.64128664549921, 2.0873080833756, 1.79470908641252, 4.66979759354339, 4.73316895456928, 0.0120359630513273, 6.82613058412457, 0) - [20250519_005107.]: Logging df_agg: CpG#1 - [20250519_005107.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005107.]: c(0, 12.1698391693126, 24.4781729791561, 38.1730159074047, 52.3348987967717, 65.4582305966058, 75.0090269722885, 81.527135738891, 100)[20250519_005107.]: c(0, 0.330160830687401, 0.521827020843901, 0.673015907404697, 2.3348987967717, 2.9582305966058, 0.00902697228849547, 5.972864261109, 0)[20250519_005107.]: c(NA, 2.64128664549921, 2.0873080833756, 1.79470908641252, 4.66979759354339, 4.73316895456928, 0.0120359630513273, 6.82613058412457, 0) - [20250519_005107.]: Entered 'hyperbolic_regression'-Function - [20250519_005107.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005107.]: Entered 'cubic_regression'-Function - [20250519_005107.]: 'cubic_regression': minmax = FALSE - [20250519_005107.]: # CpG-site: CpG#2 + [20250519_005943.]: Logging df_agg: CpG#1 + [20250519_005943.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005943.]: c(0, 12.1698391693126, 24.4781729791561, 38.1730159074047, 52.3348987967717, 65.4582305966058, 75.0090269722885, 81.527135738891, 100)[20250519_005943.]: c(0, 0.330160830687401, 0.521827020843901, 0.673015907404697, 2.3348987967717, 2.9582305966058, 0.00902697228849547, 5.972864261109, 0)[20250519_005943.]: c(NA, 2.64128664549921, 2.0873080833756, 1.79470908641252, 4.66979759354339, 4.73316895456928, 0.0120359630513273, 6.82613058412457, 0) + [20250519_005943.]: Entered 'hyperbolic_regression'-Function + [20250519_005943.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005943.]: Entered 'cubic_regression'-Function + [20250519_005943.]: 'cubic_regression': minmax = FALSE + [20250519_005943.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.13663087266051, 11.4130277963576, 26.1741025868157, 35.1051721736724, 47.6856613240114, 67.1442585354696, 75.7646957109439, 84.4056634834815, 100)c(1.13663087266051, 1.0869722036424, 1.1741025868157, 2.3948278263276, 2.3143386759886, 4.6442585354696, 0.764695710943897, 3.0943365165185, 0)c(NA, 8.6957776291392, 4.6964103472628, 6.3862075368736, 4.6286773519772, 7.43081365675137, 1.01959428125853, 3.53638459030685, 0) - [20250519_005107.]: Logging df_agg: CpG#2 - [20250519_005107.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005107.]: c(1.13663087266051, 11.4130277963576, 26.1741025868157, 35.1051721736724, 47.6856613240114, 67.1442585354696, 75.7646957109439, 84.4056634834815, 100)[20250519_005107.]: c(1.13663087266051, 1.0869722036424, 1.1741025868157, 2.3948278263276, 2.3143386759886, 4.6442585354696, 0.764695710943897, 3.0943365165185, 0)[20250519_005107.]: c(NA, 8.6957776291392, 4.6964103472628, 6.3862075368736, 4.6286773519772, 7.43081365675137, 1.01959428125853, 3.53638459030685, 0) - [20250519_005107.]: Entered 'hyperbolic_regression'-Function - [20250519_005107.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005107.]: Entered 'cubic_regression'-Function - [20250519_005107.]: 'cubic_regression': minmax = FALSE - [20250519_005107.]: # CpG-site: CpG#3 + [20250519_005943.]: Logging df_agg: CpG#2 + [20250519_005943.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005943.]: c(1.13663087266051, 11.4130277963576, 26.1741025868157, 35.1051721736724, 47.6856613240114, 67.1442585354696, 75.7646957109439, 84.4056634834815, 100)[20250519_005943.]: c(1.13663087266051, 1.0869722036424, 1.1741025868157, 2.3948278263276, 2.3143386759886, 4.6442585354696, 0.764695710943897, 3.0943365165185, 0)[20250519_005943.]: c(NA, 8.6957776291392, 4.6964103472628, 6.3862075368736, 4.6286773519772, 7.43081365675137, 1.01959428125853, 3.53638459030685, 0) + [20250519_005943.]: Entered 'hyperbolic_regression'-Function + [20250519_005943.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005943.]: Entered 'cubic_regression'-Function + [20250519_005943.]: 'cubic_regression': minmax = FALSE + [20250519_005943.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.512348257854635, 10.7523476241811, 25.5218062516799, 36.5269315729339, 50.7907738274884, 64.8684489988589, 77.5522101073979, 80.4372474691229, 100)c(0.512348257854635, 1.7476523758189, 0.521806251679902, 0.973068427066103, 0.790773827488401, 2.3684489988589, 2.5522101073979, 7.06275253087711, 0)c(NA, 13.9812190065512, 2.08722500671961, 2.59484913884294, 1.5815476549768, 3.78951839817423, 3.40294680986386, 8.07171717814526, 0) - [20250519_005108.]: Logging df_agg: CpG#3 - [20250519_005108.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005108.]: c(0.512348257854635, 10.7523476241811, 25.5218062516799, 36.5269315729339, 50.7907738274884, 64.8684489988589, 77.5522101073979, 80.4372474691229, 100)[20250519_005108.]: c(0.512348257854635, 1.7476523758189, 0.521806251679902, 0.973068427066103, 0.790773827488401, 2.3684489988589, 2.5522101073979, 7.06275253087711, 0)[20250519_005108.]: c(NA, 13.9812190065512, 2.08722500671961, 2.59484913884294, 1.5815476549768, 3.78951839817423, 3.40294680986386, 8.07171717814526, 0) - [20250519_005108.]: Entered 'hyperbolic_regression'-Function - [20250519_005108.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005108.]: Entered 'cubic_regression'-Function - [20250519_005108.]: 'cubic_regression': minmax = FALSE - [20250519_005108.]: # CpG-site: CpG#4 + [20250519_005943.]: Logging df_agg: CpG#3 + [20250519_005943.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005943.]: c(0.512348257854635, 10.7523476241811, 25.5218062516799, 36.5269315729339, 50.7907738274884, 64.8684489988589, 77.5522101073979, 80.4372474691229, 100)[20250519_005943.]: c(0.512348257854635, 1.7476523758189, 0.521806251679902, 0.973068427066103, 0.790773827488401, 2.3684489988589, 2.5522101073979, 7.06275253087711, 0)[20250519_005943.]: c(NA, 13.9812190065512, 2.08722500671961, 2.59484913884294, 1.5815476549768, 3.78951839817423, 3.40294680986386, 8.07171717814526, 0) + [20250519_005943.]: Entered 'hyperbolic_regression'-Function + [20250519_005943.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005943.]: Entered 'cubic_regression'-Function + [20250519_005943.]: 'cubic_regression': minmax = FALSE + [20250519_005943.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.4933760051342, 24.2684789512555, 38.0816229393195, 48.5842089589187, 67.6720983549562, 74.1547759694059, 82.8820147905386, 100)c(0, 0.00662399486579979, 0.731521048744501, 0.581622939319502, 1.4157910410813, 5.1720983549562, 0.845224030594096, 4.61798520946139, 0)c(NA, 0.0529919589263983, 2.926084194978, 1.550994504852, 2.8315820821626, 8.27535736792993, 1.12696537412546, 5.27769738224159, 0) - [20250519_005108.]: Logging df_agg: CpG#4 - [20250519_005108.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005108.]: c(0, 12.4933760051342, 24.2684789512555, 38.0816229393195, 48.5842089589187, 67.6720983549562, 74.1547759694059, 82.8820147905386, 100)[20250519_005108.]: c(0, 0.00662399486579979, 0.731521048744501, 0.581622939319502, 1.4157910410813, 5.1720983549562, 0.845224030594096, 4.61798520946139, 0)[20250519_005108.]: c(NA, 0.0529919589263983, 2.926084194978, 1.550994504852, 2.8315820821626, 8.27535736792993, 1.12696537412546, 5.27769738224159, 0) - [20250519_005108.]: Entered 'hyperbolic_regression'-Function - [20250519_005108.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005108.]: Entered 'cubic_regression'-Function - [20250519_005108.]: 'cubic_regression': minmax = FALSE - [20250519_005108.]: # CpG-site: CpG#5 + [20250519_005943.]: Logging df_agg: CpG#4 + [20250519_005943.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005943.]: c(0, 12.4933760051342, 24.2684789512555, 38.0816229393195, 48.5842089589187, 67.6720983549562, 74.1547759694059, 82.8820147905386, 100)[20250519_005943.]: c(0, 0.00662399486579979, 0.731521048744501, 0.581622939319502, 1.4157910410813, 5.1720983549562, 0.845224030594096, 4.61798520946139, 0)[20250519_005943.]: c(NA, 0.0529919589263983, 2.926084194978, 1.550994504852, 2.8315820821626, 8.27535736792993, 1.12696537412546, 5.27769738224159, 0) + [20250519_005943.]: Entered 'hyperbolic_regression'-Function + [20250519_005944.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005944.]: Entered 'cubic_regression'-Function + [20250519_005944.]: 'cubic_regression': minmax = FALSE + [20250519_005944.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.41558040143479, 10.1649584830834, 23.9830670020905, 37.2773406385708, 50.8659103346102, 62.4341926937382, 76.3914832329149, 86.1597399215782, 100)c(2.41558040143479, 2.3350415169166, 1.0169329979095, 0.222659361429201, 0.865910334610199, 0.065807306261803, 1.39148323291489, 1.34026007842181, 0)c(NA, 18.6803321353328, 4.067731991638, 0.593758297144537, 1.7318206692204, 0.105291690018885, 1.85531097721986, 1.53172580391064, 0) - [20250519_005108.]: Logging df_agg: CpG#5 - [20250519_005108.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005108.]: c(2.41558040143479, 10.1649584830834, 23.9830670020905, 37.2773406385708, 50.8659103346102, 62.4341926937382, 76.3914832329149, 86.1597399215782, 100)[20250519_005108.]: c(2.41558040143479, 2.3350415169166, 1.0169329979095, 0.222659361429201, 0.865910334610199, 0.065807306261803, 1.39148323291489, 1.34026007842181, 0)[20250519_005108.]: c(NA, 18.6803321353328, 4.067731991638, 0.593758297144537, 1.7318206692204, 0.105291690018885, 1.85531097721986, 1.53172580391064, 0) - [20250519_005108.]: Entered 'hyperbolic_regression'-Function - [20250519_005108.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005108.]: Entered 'cubic_regression'-Function - [20250519_005108.]: 'cubic_regression': minmax = FALSE - [20250519_005108.]: # CpG-site: CpG#6 + [20250519_005944.]: Logging df_agg: CpG#5 + [20250519_005944.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005944.]: c(2.41558040143479, 10.1649584830834, 23.9830670020905, 37.2773406385708, 50.8659103346102, 62.4341926937382, 76.3914832329149, 86.1597399215782, 100)[20250519_005944.]: c(2.41558040143479, 2.3350415169166, 1.0169329979095, 0.222659361429201, 0.865910334610199, 0.065807306261803, 1.39148323291489, 1.34026007842181, 0)[20250519_005944.]: c(NA, 18.6803321353328, 4.067731991638, 0.593758297144537, 1.7318206692204, 0.105291690018885, 1.85531097721986, 1.53172580391064, 0) + [20250519_005944.]: Entered 'hyperbolic_regression'-Function + [20250519_005944.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005944.]: Entered 'cubic_regression'-Function + [20250519_005944.]: 'cubic_regression': minmax = FALSE + [20250519_005944.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.138179963459196, 11.8635989635202, 26.5108198460409, 35.3206004021833, 50.0571987473308, 64.9604436607063, 73.6690699266294, 87.1267946879604, 100)c(0.138179963459196, 0.6364010364798, 1.5108198460409, 2.1793995978167, 0.0571987473308013, 2.4604436607063, 1.3309300733706, 0.373205312039602, 0)c(NA, 5.0912082918384, 6.0432793841636, 5.81173226084453, 0.114397494661603, 3.93670985713009, 1.77457343116079, 0.426520356616688, 0) - [20250519_005108.]: Logging df_agg: CpG#6 - [20250519_005108.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005108.]: c(0.138179963459196, 11.8635989635202, 26.5108198460409, 35.3206004021833, 50.0571987473308, 64.9604436607063, 73.6690699266294, 87.1267946879604, 100)[20250519_005108.]: c(0.138179963459196, 0.6364010364798, 1.5108198460409, 2.1793995978167, 0.0571987473308013, 2.4604436607063, 1.3309300733706, 0.373205312039602, 0)[20250519_005108.]: c(NA, 5.0912082918384, 6.0432793841636, 5.81173226084453, 0.114397494661603, 3.93670985713009, 1.77457343116079, 0.426520356616688, 0) - [20250519_005108.]: Entered 'hyperbolic_regression'-Function - [20250519_005108.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005108.]: Entered 'cubic_regression'-Function - [20250519_005108.]: 'cubic_regression': minmax = FALSE - [20250519_005108.]: # CpG-site: CpG#7 + [20250519_005944.]: Logging df_agg: CpG#6 + [20250519_005944.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005944.]: c(0.138179963459196, 11.8635989635202, 26.5108198460409, 35.3206004021833, 50.0571987473308, 64.9604436607063, 73.6690699266294, 87.1267946879604, 100)[20250519_005944.]: c(0.138179963459196, 0.6364010364798, 1.5108198460409, 2.1793995978167, 0.0571987473308013, 2.4604436607063, 1.3309300733706, 0.373205312039602, 0)[20250519_005944.]: c(NA, 5.0912082918384, 6.0432793841636, 5.81173226084453, 0.114397494661603, 3.93670985713009, 1.77457343116079, 0.426520356616688, 0) + [20250519_005944.]: Entered 'hyperbolic_regression'-Function + [20250519_005944.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005944.]: Entered 'cubic_regression'-Function + [20250519_005944.]: 'cubic_regression': minmax = FALSE + [20250519_005944.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.199366215418, 24.595295361327, 37.8312095633391, 53.5590896582852, 65.9367389282431, 75.736370152841, 79.433089359524, 100)c(0, 2.300633784582, 0.404704638673, 0.331209563339101, 3.5590896582852, 3.4367389282431, 0.736370152841005, 8.066910640476, 0)c(NA, 18.405070276656, 1.618818554692, 0.883225502237603, 7.11817931657041, 5.49878228518896, 0.981826870454673, 9.21932644625828, 0) - [20250519_005108.]: Logging df_agg: CpG#7 - [20250519_005108.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005108.]: c(0, 10.199366215418, 24.595295361327, 37.8312095633391, 53.5590896582852, 65.9367389282431, 75.736370152841, 79.433089359524, 100)[20250519_005108.]: c(0, 2.300633784582, 0.404704638673, 0.331209563339101, 3.5590896582852, 3.4367389282431, 0.736370152841005, 8.066910640476, 0)[20250519_005108.]: c(NA, 18.405070276656, 1.618818554692, 0.883225502237603, 7.11817931657041, 5.49878228518896, 0.981826870454673, 9.21932644625828, 0) - [20250519_005108.]: Entered 'hyperbolic_regression'-Function - [20250519_005108.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005109.]: Entered 'cubic_regression'-Function - [20250519_005109.]: 'cubic_regression': minmax = FALSE - [20250519_005109.]: # CpG-site: CpG#8 + [20250519_005944.]: Logging df_agg: CpG#7 + [20250519_005944.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005944.]: c(0, 10.199366215418, 24.595295361327, 37.8312095633391, 53.5590896582852, 65.9367389282431, 75.736370152841, 79.433089359524, 100)[20250519_005944.]: c(0, 2.300633784582, 0.404704638673, 0.331209563339101, 3.5590896582852, 3.4367389282431, 0.736370152841005, 8.066910640476, 0)[20250519_005944.]: c(NA, 18.405070276656, 1.618818554692, 0.883225502237603, 7.11817931657041, 5.49878228518896, 0.981826870454673, 9.21932644625828, 0) + [20250519_005944.]: Entered 'hyperbolic_regression'-Function + [20250519_005944.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005944.]: Entered 'cubic_regression'-Function + [20250519_005944.]: 'cubic_regression': minmax = FALSE + [20250519_005944.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.80067549526643, 9.27534686003087, 25.4762624089952, 34.0122098045585, 51.784270967861, 64.6732380258789, 78.4327054483192, 81.3427308951795, 100)c(2.80067549526643, 3.22465313996913, 0.476262408995201, 3.4877901954415, 1.784270967861, 2.1732380258789, 3.4327054483192, 6.1572691048205, 0)c(NA, 25.797225119753, 1.9050496359808, 9.30077385451066, 3.568541935722, 3.47718084140624, 4.57694059775893, 7.03687897693771, 0) - [20250519_005109.]: Logging df_agg: CpG#8 - [20250519_005109.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005109.]: c(2.80067549526643, 9.27534686003087, 25.4762624089952, 34.0122098045585, 51.784270967861, 64.6732380258789, 78.4327054483192, 81.3427308951795, 100)[20250519_005109.]: c(2.80067549526643, 3.22465313996913, 0.476262408995201, 3.4877901954415, 1.784270967861, 2.1732380258789, 3.4327054483192, 6.1572691048205, 0)[20250519_005109.]: c(NA, 25.797225119753, 1.9050496359808, 9.30077385451066, 3.568541935722, 3.47718084140624, 4.57694059775893, 7.03687897693771, 0) - [20250519_005109.]: Entered 'hyperbolic_regression'-Function - [20250519_005109.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005109.]: Entered 'cubic_regression'-Function - [20250519_005109.]: 'cubic_regression': minmax = FALSE - [20250519_005109.]: # CpG-site: CpG#9 + [20250519_005944.]: Logging df_agg: CpG#8 + [20250519_005944.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005944.]: c(2.80067549526643, 9.27534686003087, 25.4762624089952, 34.0122098045585, 51.784270967861, 64.6732380258789, 78.4327054483192, 81.3427308951795, 100)[20250519_005944.]: c(2.80067549526643, 3.22465313996913, 0.476262408995201, 3.4877901954415, 1.784270967861, 2.1732380258789, 3.4327054483192, 6.1572691048205, 0)[20250519_005944.]: c(NA, 25.797225119753, 1.9050496359808, 9.30077385451066, 3.568541935722, 3.47718084140624, 4.57694059775893, 7.03687897693771, 0) + [20250519_005944.]: Entered 'hyperbolic_regression'-Function + [20250519_005944.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005945.]: Entered 'cubic_regression'-Function + [20250519_005945.]: 'cubic_regression': minmax = FALSE + [20250519_005945.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.5081558897243, 26.9163121098627, 36.8332915301825, 52.00955, 64.8927781893359, 74.566810799042, 84.5291854856128, 100)c(0, 1.9918441102757, 1.9163121098627, 0.6667084698175, 2.00955, 2.3927781893359, 0.433189200957997, 2.9708145143872, 0)c(NA, 15.9347528822056, 7.66524843945081, 1.77788925284667, 4.01909999999999, 3.82844510293744, 0.57758560127733, 3.39521658787109, 0) - [20250519_005109.]: Logging df_agg: CpG#9 - [20250519_005109.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005109.]: c(0, 10.5081558897243, 26.9163121098627, 36.8332915301825, 52.00955, 64.8927781893359, 74.566810799042, 84.5291854856128, 100)[20250519_005109.]: c(0, 1.9918441102757, 1.9163121098627, 0.6667084698175, 2.00955, 2.3927781893359, 0.433189200957997, 2.9708145143872, 0)[20250519_005109.]: c(NA, 15.9347528822056, 7.66524843945081, 1.77788925284667, 4.01909999999999, 3.82844510293744, 0.57758560127733, 3.39521658787109, 0) - [20250519_005109.]: Entered 'hyperbolic_regression'-Function - [20250519_005109.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005109.]: Entered 'cubic_regression'-Function - [20250519_005109.]: 'cubic_regression': minmax = FALSE - [20250519_005109.]: # CpG-site: row_means + [20250519_005945.]: Logging df_agg: CpG#9 + [20250519_005945.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005945.]: c(0, 10.5081558897243, 26.9163121098627, 36.8332915301825, 52.00955, 64.8927781893359, 74.566810799042, 84.5291854856128, 100)[20250519_005945.]: c(0, 1.9918441102757, 1.9163121098627, 0.6667084698175, 2.00955, 2.3927781893359, 0.433189200957997, 2.9708145143872, 0)[20250519_005945.]: c(NA, 15.9347528822056, 7.66524843945081, 1.77788925284667, 4.01909999999999, 3.82844510293744, 0.57758560127733, 3.39521658787109, 0) + [20250519_005945.]: Entered 'hyperbolic_regression'-Function + [20250519_005945.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005945.]: Entered 'cubic_regression'-Function + [20250519_005945.]: 'cubic_regression': minmax = FALSE + [20250519_005945.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.290936038655697, 11.0412364555656, 25.408146358228, 36.5243683648753, 50.7348788337891, 65.3135169308955, 75.5342663138126, 83.2411177153911, 100)c(0.290936038655697, 1.4587635444344, 0.408146358227999, 0.975631635124699, 0.734878833789097, 2.8135169308955, 0.534266313812594, 4.2588822846089, 0)c(NA, 11.6701083554752, 1.63258543291199, 2.60168436033253, 1.46975766757819, 4.5016270894328, 0.712355085083459, 4.86729403955303, 0) - [20250519_005109.]: Logging df_agg: row_means - [20250519_005109.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005109.]: c(0.290936038655697, 11.0412364555656, 25.408146358228, 36.5243683648753, 50.7348788337891, 65.3135169308955, 75.5342663138126, 83.2411177153911, 100)[20250519_005109.]: c(0.290936038655697, 1.4587635444344, 0.408146358227999, 0.975631635124699, 0.734878833789097, 2.8135169308955, 0.534266313812594, 4.2588822846089, 0)[20250519_005109.]: c(NA, 11.6701083554752, 1.63258543291199, 2.60168436033253, 1.46975766757819, 4.5016270894328, 0.712355085083459, 4.86729403955303, 0) - [20250519_005109.]: Entered 'hyperbolic_regression'-Function - [20250519_005109.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005109.]: Entered 'cubic_regression'-Function - [20250519_005109.]: 'cubic_regression': minmax = FALSE - [20250519_005110.]: Entered 'solving_equations'-Function - [20250519_005110.]: Solving cubic regression for CpG#1 + [20250519_005945.]: Logging df_agg: row_means + [20250519_005945.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005945.]: c(0.290936038655697, 11.0412364555656, 25.408146358228, 36.5243683648753, 50.7348788337891, 65.3135169308955, 75.5342663138126, 83.2411177153911, 100)[20250519_005945.]: c(0.290936038655697, 1.4587635444344, 0.408146358227999, 0.975631635124699, 0.734878833789097, 2.8135169308955, 0.534266313812594, 4.2588822846089, 0)[20250519_005945.]: c(NA, 11.6701083554752, 1.63258543291199, 2.60168436033253, 1.46975766757819, 4.5016270894328, 0.712355085083459, 4.86729403955303, 0) + [20250519_005945.]: Entered 'hyperbolic_regression'-Function + [20250519_005945.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005945.]: Entered 'cubic_regression'-Function + [20250519_005945.]: 'cubic_regression': minmax = FALSE + [20250519_005946.]: Entered 'solving_equations'-Function + [20250519_005946.]: Solving cubic regression for CpG#1 Coefficients: -1.036Coefficients: 0.784Coefficients: -0.006Coefficients: 0 - [20250519_005110.]: Samplename: 0 + [20250519_005946.]: Samplename: 0 Root: 1.335 --> Root in between the borders! Added to results. Coefficients: -8.34133333333333Coefficients: 0.784Coefficients: -0.006Coefficients: 0 - [20250519_005110.]: Samplename: 12.5 + [20250519_005946.]: Samplename: 12.5 Root: 11.684 --> Root in between the borders! Added to results. Coefficients: -15.388Coefficients: 0.784Coefficients: -0.006Coefficients: 0 - [20250519_005110.]: Samplename: 25 + [20250519_005946.]: Samplename: 25 Root: 24.056 --> Root in between the borders! Added to results. Coefficients: -24.28Coefficients: 0.784Coefficients: -0.006Coefficients: 0 - [20250519_005110.]: Samplename: 37.5 + [20250519_005946.]: Samplename: 37.5 Root: 50.441 --> Root in between the borders! Added to results. Coefficients: -34.9005Coefficients: 0.784Coefficients: -0.006Coefficients: 0 - [20250519_005110.]: Samplename: 50 + [20250519_005946.]: Samplename: 50 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -46.354Coefficients: 0.784Coefficients: -0.006Coefficients: 0 - [20250519_005110.]: Samplename: 62.5 + [20250519_005946.]: Samplename: 62.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -55.893Coefficients: 0.784Coefficients: -0.006Coefficients: 0 - [20250519_005110.]: Samplename: 75 + [20250519_005946.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -63.098Coefficients: 0.784Coefficients: -0.006Coefficients: 0 - [20250519_005110.]: Samplename: 87.5 + [20250519_005946.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -91.046Coefficients: 0.784Coefficients: -0.006Coefficients: 0 - [20250519_005110.]: Samplename: 100 + [20250519_005946.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA - [20250519_005110.]: Solving cubic regression for CpG#2 + [20250519_005946.]: Solving cubic regression for CpG#2 Coefficients: -0.283000000000001Coefficients: 0.515Coefficients: 0.003Coefficients: 0 - [20250519_005110.]: Samplename: 0 + [20250519_005946.]: Samplename: 0 Root: 0.548 --> Root in between the borders! Added to results. Coefficients: -6.33966666666667Coefficients: 0.515Coefficients: 0.003Coefficients: 0 - [20250519_005110.]: Samplename: 12.5 + [20250519_005946.]: Samplename: 12.5 Root: 11.535 --> Root in between the borders! Added to results. Coefficients: -15.939Coefficients: 0.515Coefficients: 0.003Coefficients: 0 - [20250519_005110.]: Samplename: 25 + [20250519_005946.]: Samplename: 25 Root: 26.774 --> Root in between the borders! Added to results. Coefficients: -22.337Coefficients: 0.515Coefficients: 0.003Coefficients: 0 - [20250519_005110.]: Samplename: 37.5 + [20250519_005946.]: Samplename: 37.5 Root: 35.875 --> Root in between the borders! Added to results. Coefficients: -32.228Coefficients: 0.515Coefficients: 0.003Coefficients: 0 - [20250519_005110.]: Samplename: 50 + [20250519_005946.]: Samplename: 50 Root: 48.74 --> Root in between the borders! Added to results. Coefficients: -49.963Coefficients: 0.515Coefficients: 0.003Coefficients: 0 - [20250519_005110.]: Samplename: 62.5 + [20250519_005946.]: Samplename: 62.5 Root: 69.156 --> Root in between the borders! Added to results. Coefficients: -58.9655Coefficients: 0.515Coefficients: 0.003Coefficients: 0 - [20250519_005110.]: Samplename: 75 + [20250519_005946.]: Samplename: 75 Root: 78.552 --> Root in between the borders! Added to results. Coefficients: -68.8363333333333Coefficients: 0.515Coefficients: 0.003Coefficients: 0 - [20250519_005110.]: Samplename: 87.5 + [20250519_005946.]: Samplename: 87.5 Root: 88.272 --> Root in between the borders! Added to results. Coefficients: -90.577Coefficients: 0.515Coefficients: 0.003Coefficients: 0 - [20250519_005110.]: Samplename: 100 + [20250519_005946.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 107.97 --> '100 < root < 110' --> substitute 100 - [20250519_005110.]: Solving cubic regression for CpG#3 + [20250519_005946.]: Solving cubic regression for CpG#3 Coefficients: -0.903Coefficients: 0.628Coefficients: -0.001Coefficients: 0 - [20250519_005110.]: Samplename: 0 + [20250519_005946.]: Samplename: 0 Root: 1.441 --> Root in between the borders! Added to results. Coefficients: -6.573Coefficients: 0.628Coefficients: -0.001Coefficients: 0 - [20250519_005110.]: Samplename: 12.5 + [20250519_005946.]: Samplename: 12.5 Root: 10.647 --> Root in between the borders! Added to results. Coefficients: -15.429Coefficients: 0.628Coefficients: -0.001Coefficients: 0 - [20250519_005110.]: Samplename: 25 + [20250519_005946.]: Samplename: 25 Root: 25.613 --> Root in between the borders! Added to results. Coefficients: -22.613Coefficients: 0.628Coefficients: -0.001Coefficients: 0 - [20250519_005110.]: Samplename: 37.5 + [20250519_005946.]: Samplename: 37.5 Root: 38.35 --> Root in between the borders! Added to results. Coefficients: -32.7755Coefficients: 0.628Coefficients: -0.001Coefficients: 0 - [20250519_005110.]: Samplename: 50 + [20250519_005946.]: Samplename: 50 Root: 57.445 --> Root in between the borders! Added to results. Coefficients: -43.889Coefficients: 0.628Coefficients: -0.001Coefficients: 0 - [20250519_005110.]: Samplename: 62.5 + [20250519_005946.]: Samplename: 62.5 Root: 80.105 --> Root in between the borders! Added to results. Coefficients: -54.9755Coefficients: 0.628Coefficients: -0.001Coefficients: 0 - [20250519_005110.]: Samplename: 75 + [20250519_005946.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. @@ -4571,40 +4571,40 @@ Root: 105.145 --> '100 < root < 110' --> substitute 100 Coefficients: -57.6563333333333Coefficients: 0.628Coefficients: -0.001Coefficients: 0 - [20250519_005110.]: Samplename: 87.5 + [20250519_005946.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -80.665Coefficients: 0.628Coefficients: -0.001Coefficients: 0 - [20250519_005110.]: Samplename: 100 + [20250519_005946.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA - [20250519_005110.]: Solving cubic regression for CpG#4 + [20250519_005946.]: Solving cubic regression for CpG#4 Coefficients: -0.597Coefficients: 0.697Coefficients: -0.002Coefficients: 0 - [20250519_005110.]: Samplename: 0 + [20250519_005946.]: Samplename: 0 Root: 0.859 --> Root in between the borders! Added to results. Coefficients: -8.25233333333333Coefficients: 0.697Coefficients: -0.002Coefficients: 0 - [20250519_005110.]: Samplename: 12.5 + [20250519_005946.]: Samplename: 12.5 Root: 12.272 --> Root in between the borders! Added to results. Coefficients: -15.803Coefficients: 0.697Coefficients: -0.002Coefficients: 0 - [20250519_005110.]: Samplename: 25 + [20250519_005946.]: Samplename: 25 Root: 24.378 --> Root in between the borders! Added to results. Coefficients: -25.527Coefficients: 0.697Coefficients: -0.002Coefficients: 0 - [20250519_005110.]: Samplename: 37.5 + [20250519_005946.]: Samplename: 37.5 Root: 41.587 --> Root in between the borders! Added to results. Coefficients: -33.6365Coefficients: 0.697Coefficients: -0.002Coefficients: 0 - [20250519_005110.]: Samplename: 50 + [20250519_005946.]: Samplename: 50 Root: 57.868 --> Root in between the borders! Added to results. Coefficients: -50.255Coefficients: 0.697Coefficients: -0.002Coefficients: 0 - [20250519_005110.]: Samplename: 62.5 + [20250519_005946.]: Samplename: 62.5 ## WARNING ## No fitting root within the borders found. @@ -4612,91 +4612,91 @@ Root: 101.893 --> '100 < root < 110' --> substitute 100 Coefficients: -56.539Coefficients: 0.697Coefficients: -0.002Coefficients: 0 - [20250519_005110.]: Samplename: 75 + [20250519_005946.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -65.5923333333333Coefficients: 0.697Coefficients: -0.002Coefficients: 0 - [20250519_005110.]: Samplename: 87.5 + [20250519_005946.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -88.321Coefficients: 0.697Coefficients: -0.002Coefficients: 0 - [20250519_005110.]: Samplename: 100 + [20250519_005946.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA - [20250519_005110.]: Solving cubic regression for CpG#5 + [20250519_005946.]: Solving cubic regression for CpG#5 Coefficients: -0.624000000000001Coefficients: 0.422Coefficients: 0.004Coefficients: 0 - [20250519_005110.]: Samplename: 0 + [20250519_005946.]: Samplename: 0 Root: 1.459 --> Root in between the borders! Added to results. Coefficients: -4.768Coefficients: 0.422Coefficients: 0.004Coefficients: 0 - [20250519_005110.]: Samplename: 12.5 + [20250519_005946.]: Samplename: 12.5 Root: 10.294 --> Root in between the borders! Added to results. Coefficients: -12.726Coefficients: 0.422Coefficients: 0.004Coefficients: 0 - [20250519_005110.]: Samplename: 25 + [20250519_005946.]: Samplename: 25 Root: 24.477 --> Root in between the borders! Added to results. Coefficients: -21.16Coefficients: 0.422Coefficients: 0.004Coefficients: 0 - [20250519_005110.]: Samplename: 37.5 + [20250519_005946.]: Samplename: 37.5 Root: 37.097 --> Root in between the borders! Added to results. Coefficients: -30.6955Coefficients: 0.422Coefficients: 0.004Coefficients: 0 - [20250519_005110.]: Samplename: 50 + [20250519_005946.]: Samplename: 50 Root: 49.507 --> Root in between the borders! Added to results. Coefficients: -39.658Coefficients: 0.422Coefficients: 0.004Coefficients: 0 - [20250519_005110.]: Samplename: 62.5 + [20250519_005946.]: Samplename: 62.5 Root: 59.931 --> Root in between the borders! Added to results. Coefficients: -51.683Coefficients: 0.422Coefficients: 0.004Coefficients: 0 - [20250519_005110.]: Samplename: 75 + [20250519_005946.]: Samplename: 75 Root: 72.563 --> Root in between the borders! Added to results. Coefficients: -61.0146666666667Coefficients: 0.422Coefficients: 0.004Coefficients: 0 - [20250519_005110.]: Samplename: 87.5 + [20250519_005946.]: Samplename: 87.5 Root: 81.549 --> Root in between the borders! Added to results. Coefficients: -76.07Coefficients: 0.422Coefficients: 0.004Coefficients: 0 - [20250519_005110.]: Samplename: 100 + [20250519_005946.]: Samplename: 100 Root: 94.898 --> Root in between the borders! Added to results. - [20250519_005110.]: Solving cubic regression for CpG#6 + [20250519_005946.]: Solving cubic regression for CpG#6 Coefficients: -0.196000000000001Coefficients: 0.561Coefficients: 0.001Coefficients: 0 - [20250519_005110.]: Samplename: 0 + [20250519_005946.]: Samplename: 0 Root: 0.349 --> Root in between the borders! Added to results. Coefficients: -6.73866666666667Coefficients: 0.561Coefficients: 0.001Coefficients: 0 - [20250519_005110.]: Samplename: 12.5 + [20250519_005946.]: Samplename: 12.5 Root: 11.765 --> Root in between the borders! Added to results. Coefficients: -15.888Coefficients: 0.561Coefficients: 0.001Coefficients: 0 - [20250519_005110.]: Samplename: 25 + [20250519_005946.]: Samplename: 25 Root: 27.02 --> Root in between the borders! Added to results. Coefficients: -22Coefficients: 0.561Coefficients: 0.001Coefficients: 0 - [20250519_005110.]: Samplename: 37.5 + [20250519_005946.]: Samplename: 37.5 Root: 36.802 --> Root in between the borders! Added to results. Coefficients: -33.4445Coefficients: 0.561Coefficients: 0.001Coefficients: 0 - [20250519_005110.]: Samplename: 50 + [20250519_005946.]: Samplename: 50 Root: 54.35 --> Root in between the borders! Added to results. Coefficients: -46.9Coefficients: 0.561Coefficients: 0.001Coefficients: 0 - [20250519_005110.]: Samplename: 62.5 + [20250519_005946.]: Samplename: 62.5 Root: 73.873 --> Root in between the borders! Added to results. Coefficients: -55.832Coefficients: 0.561Coefficients: 0.001Coefficients: 0 - [20250519_005110.]: Samplename: 75 + [20250519_005946.]: Samplename: 75 Root: 86.259 --> Root in between the borders! Added to results. Coefficients: -71.5453333333333Coefficients: 0.561Coefficients: 0.001Coefficients: 0 - [20250519_005110.]: Samplename: 87.5 + [20250519_005946.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. @@ -4704,188 +4704,188 @@ Root: 107.089 --> '100 < root < 110' --> substitute 100 Coefficients: -89.656Coefficients: 0.561Coefficients: 0.001Coefficients: 0 - [20250519_005110.]: Samplename: 100 + [20250519_005946.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA - [20250519_005110.]: Solving cubic regression for CpG#7 + [20250519_005946.]: Solving cubic regression for CpG#7 Coefficients: -1.215Coefficients: 0.58Coefficients: -0.004Coefficients: 0 - [20250519_005110.]: Samplename: 0 + [20250519_005946.]: Samplename: 0 Root: 2.126 --> Root in between the borders! Added to results. Coefficients: -5.39566666666667Coefficients: 0.58Coefficients: -0.004Coefficients: 0 - [20250519_005110.]: Samplename: 12.5 + [20250519_005946.]: Samplename: 12.5 Root: 9.991 --> Root in between the borders! Added to results. Coefficients: -11.265Coefficients: 0.58Coefficients: -0.004Coefficients: 0 - [20250519_005110.]: Samplename: 25 + [20250519_005946.]: Samplename: 25 Root: 23.104 --> Root in between the borders! Added to results. Coefficients: -17.451Coefficients: 0.58Coefficients: -0.004Coefficients: 0 - [20250519_005110.]: Samplename: 37.5 + [20250519_005946.]: Samplename: 37.5 Root: 42.609 --> Root in between the borders! Added to results. Coefficients: -26.0315Coefficients: 0.58Coefficients: -0.004Coefficients: 0 - [20250519_005110.]: Samplename: 50 + [20250519_005946.]: Samplename: 50 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -33.965Coefficients: 0.58Coefficients: -0.004Coefficients: 0 - [20250519_005110.]: Samplename: 62.5 + [20250519_005946.]: Samplename: 62.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -41.169Coefficients: 0.58Coefficients: -0.004Coefficients: 0 - [20250519_005110.]: Samplename: 75 + [20250519_005946.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -44.1356666666667Coefficients: 0.58Coefficients: -0.004Coefficients: 0 - [20250519_005110.]: Samplename: 87.5 + [20250519_005946.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -67.223Coefficients: 0.58Coefficients: -0.004Coefficients: 0 - [20250519_005110.]: Samplename: 100 + [20250519_005946.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA - [20250519_005110.]: Solving cubic regression for CpG#8 + [20250519_005946.]: Solving cubic regression for CpG#8 Coefficients: -1.096Coefficients: 0.535Coefficients: 0.004Coefficients: 0 - [20250519_005110.]: Samplename: 0 + [20250519_005946.]: Samplename: 0 Root: 2.018 --> Root in between the borders! Added to results. Coefficients: -5.44666666666667Coefficients: 0.535Coefficients: 0.004Coefficients: 0 - [20250519_005110.]: Samplename: 12.5 + [20250519_005946.]: Samplename: 12.5 Root: 9.505 --> Root in between the borders! Added to results. Coefficients: -16.93Coefficients: 0.535Coefficients: 0.004Coefficients: 0 - [20250519_005110.]: Samplename: 25 + [20250519_005946.]: Samplename: 25 Root: 26.424 --> Root in between the borders! Added to results. Coefficients: -23.35Coefficients: 0.535Coefficients: 0.004Coefficients: 0 - [20250519_005110.]: Samplename: 37.5 + [20250519_005946.]: Samplename: 37.5 Root: 34.662 --> Root in between the borders! Added to results. Coefficients: -37.625Coefficients: 0.535Coefficients: 0.004Coefficients: 0 - [20250519_005110.]: Samplename: 50 + [20250519_005946.]: Samplename: 50 Root: 50.932 --> Root in between the borders! Added to results. Coefficients: -48.826Coefficients: 0.535Coefficients: 0.004Coefficients: 0 - [20250519_005110.]: Samplename: 62.5 + [20250519_005946.]: Samplename: 62.5 Root: 62.271 --> Root in between the borders! Added to results. Coefficients: -61.6675Coefficients: 0.535Coefficients: 0.004Coefficients: 0 - [20250519_005110.]: Samplename: 75 + [20250519_005946.]: Samplename: 75 Root: 74.154 --> Root in between the borders! Added to results. Coefficients: -64.51Coefficients: 0.535Coefficients: 0.004Coefficients: 0 - [20250519_005110.]: Samplename: 87.5 + [20250519_005946.]: Samplename: 87.5 Root: 76.651 --> Root in between the borders! Added to results. Coefficients: -86.06Coefficients: 0.535Coefficients: 0.004Coefficients: 0 - [20250519_005110.]: Samplename: 100 + [20250519_005946.]: Samplename: 100 Root: 94.331 --> Root in between the borders! Added to results. - [20250519_005110.]: Solving cubic regression for CpG#9 + [20250519_005946.]: Solving cubic regression for CpG#9 Coefficients: -0.99Coefficients: 0.679Coefficients: -0.005Coefficients: 0 - [20250519_005110.]: Samplename: 0 + [20250519_005946.]: Samplename: 0 Root: 1.474 --> Root in between the borders! Added to results. Coefficients: -6.396Coefficients: 0.679Coefficients: -0.005Coefficients: 0 - [20250519_005110.]: Samplename: 12.5 + [20250519_005946.]: Samplename: 12.5 Root: 10.183 --> Root in between the borders! Added to results. Coefficients: -14.706Coefficients: 0.679Coefficients: -0.005Coefficients: 0 - [20250519_005110.]: Samplename: 25 + [20250519_005946.]: Samplename: 25 Root: 27.044 --> Root in between the borders! Added to results. Coefficients: -20.624Coefficients: 0.679Coefficients: -0.005Coefficients: 0 - [20250519_005110.]: Samplename: 37.5 + [20250519_005946.]: Samplename: 37.5 Root: 45.863 --> Root in between the borders! Added to results. Coefficients: -31.396Coefficients: 0.679Coefficients: -0.005Coefficients: 0 - [20250519_005110.]: Samplename: 50 + [20250519_005946.]: Samplename: 50 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -42.686Coefficients: 0.679Coefficients: -0.005Coefficients: 0 - [20250519_005110.]: Samplename: 62.5 + [20250519_005946.]: Samplename: 62.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -52.9035Coefficients: 0.679Coefficients: -0.005Coefficients: 0 - [20250519_005110.]: Samplename: 75 + [20250519_005946.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -65.4926666666667Coefficients: 0.679Coefficients: -0.005Coefficients: 0 - [20250519_005110.]: Samplename: 87.5 + [20250519_005946.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -92.99Coefficients: 0.679Coefficients: -0.005Coefficients: 0 - [20250519_005110.]: Samplename: 100 + [20250519_005946.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA - [20250519_005110.]: Solving cubic regression for row_means + [20250519_005946.]: Solving cubic regression for row_means Coefficients: -0.771111111111111Coefficients: 0.6Coefficients: -0.001Coefficients: 0 - [20250519_005110.]: Samplename: 0 + [20250519_005946.]: Samplename: 0 Root: 1.288 --> Root in between the borders! Added to results. Coefficients: -6.47237037037037Coefficients: 0.6Coefficients: -0.001Coefficients: 0 - [20250519_005110.]: Samplename: 12.5 + [20250519_005946.]: Samplename: 12.5 Root: 10.989 --> Root in between the borders! Added to results. Coefficients: -14.8971111111111Coefficients: 0.6Coefficients: -0.001Coefficients: 0 - [20250519_005110.]: Samplename: 25 + [20250519_005946.]: Samplename: 25 Root: 25.951 --> Root in between the borders! Added to results. Coefficients: -22.1491111111111Coefficients: 0.6Coefficients: -0.001Coefficients: 0 - [20250519_005110.]: Samplename: 37.5 + [20250519_005946.]: Samplename: 37.5 Root: 39.518 --> Root in between the borders! Added to results. Coefficients: -32.5258888888889Coefficients: 0.6Coefficients: -0.001Coefficients: 0 - [20250519_005110.]: Samplename: 50 + [20250519_005946.]: Samplename: 50 Root: 60.262 --> Root in between the borders! Added to results. Coefficients: -44.7217777777778Coefficients: 0.6Coefficients: -0.001Coefficients: 0 - [20250519_005110.]: Samplename: 62.5 + [20250519_005946.]: Samplename: 62.5 Root: 87.213 --> Root in between the borders! Added to results. Coefficients: -54.4031111111111Coefficients: 0.6Coefficients: -0.001Coefficients: 0 - [20250519_005110.]: Samplename: 75 + [20250519_005946.]: Samplename: 75 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -62.4312592592593Coefficients: 0.6Coefficients: -0.001Coefficients: 0 - [20250519_005110.]: Samplename: 87.5 + [20250519_005946.]: Samplename: 87.5 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA Coefficients: -84.7342222222222Coefficients: 0.6Coefficients: -0.001Coefficients: 0 - [20250519_005110.]: Samplename: 100 + [20250519_005946.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA - [20250519_005110.]: + [20250519_005946.]: ### Starting with regression calculations ### - [20250519_005110.]: Entered 'regression_type1'-Function + [20250519_005946.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : @@ -5410,525 +5410,525 @@ singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient - [20250519_005110.]: # CpG-site: CpG#1 + [20250519_005946.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5)c(1.3350694679926, 11.6842678967181, 24.056490215457, 50.4411280642076)c(1.3350694679926, 0.815732103281899, 0.943509784543, 12.9411280642076)c(NA, 6.52585682625519, 3.774039138172, 34.5096748378869) - [20250519_005110.]: Logging df_agg: CpG#1 - [20250519_005110.]: c(0, 12.5, 25, 37.5)[20250519_005110.]: c(1.3350694679926, 11.6842678967181, 24.056490215457, 50.4411280642076)[20250519_005110.]: c(1.3350694679926, 0.815732103281899, 0.943509784543, 12.9411280642076)[20250519_005110.]: c(NA, 6.52585682625519, 3.774039138172, 34.5096748378869) - [20250519_005110.]: Entered 'hyperbolic_regression'-Function - [20250519_005110.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005111.]: Entered 'cubic_regression'-Function - [20250519_005111.]: 'cubic_regression': minmax = FALSE - [20250519_005111.]: # CpG-site: CpG#2 + [20250519_005946.]: Logging df_agg: CpG#1 + [20250519_005946.]: c(0, 12.5, 25, 37.5)[20250519_005946.]: c(1.3350694679926, 11.6842678967181, 24.056490215457, 50.4411280642076)[20250519_005946.]: c(1.3350694679926, 0.815732103281899, 0.943509784543, 12.9411280642076)[20250519_005946.]: c(NA, 6.52585682625519, 3.774039138172, 34.5096748378869) + [20250519_005946.]: Entered 'hyperbolic_regression'-Function + [20250519_005946.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005946.]: Entered 'cubic_regression'-Function + [20250519_005946.]: 'cubic_regression': minmax = FALSE + [20250519_005946.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.547766708541827, 11.5349537694669, 26.7737761718445, 35.875449331591, 48.7401699196391, 69.1560032174257, 78.551969470104, 88.2723976336729, 100)c(0.547766708541827, 0.965046230533099, 1.7737761718445, 1.624550668409, 1.2598300803609, 6.65600321742571, 3.551969470104, 0.772397633672895, 0)c(NA, 7.72036984426479, 7.095104687378, 4.33213511575733, 2.5196601607218, 10.6496051478811, 4.735959293472, 0.882740152769023, 0) - [20250519_005111.]: Logging df_agg: CpG#2 - [20250519_005111.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005111.]: c(0.547766708541827, 11.5349537694669, 26.7737761718445, 35.875449331591, 48.7401699196391, 69.1560032174257, 78.551969470104, 88.2723976336729, 100)[20250519_005111.]: c(0.547766708541827, 0.965046230533099, 1.7737761718445, 1.624550668409, 1.2598300803609, 6.65600321742571, 3.551969470104, 0.772397633672895, 0)[20250519_005111.]: c(NA, 7.72036984426479, 7.095104687378, 4.33213511575733, 2.5196601607218, 10.6496051478811, 4.735959293472, 0.882740152769023, 0) - [20250519_005111.]: Entered 'hyperbolic_regression'-Function - [20250519_005111.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005111.]: Entered 'cubic_regression'-Function - [20250519_005111.]: 'cubic_regression': minmax = FALSE - [20250519_005111.]: # CpG-site: CpG#3 + [20250519_005946.]: Logging df_agg: CpG#2 + [20250519_005946.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005946.]: c(0.547766708541827, 11.5349537694669, 26.7737761718445, 35.875449331591, 48.7401699196391, 69.1560032174257, 78.551969470104, 88.2723976336729, 100)[20250519_005946.]: c(0.547766708541827, 0.965046230533099, 1.7737761718445, 1.624550668409, 1.2598300803609, 6.65600321742571, 3.551969470104, 0.772397633672895, 0)[20250519_005946.]: c(NA, 7.72036984426479, 7.095104687378, 4.33213511575733, 2.5196601607218, 10.6496051478811, 4.735959293472, 0.882740152769023, 0) + [20250519_005946.]: Entered 'hyperbolic_regression'-Function + [20250519_005946.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005947.]: Entered 'cubic_regression'-Function + [20250519_005947.]: 'cubic_regression': minmax = FALSE + [20250519_005947.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75)c(1.44120553086336, 10.6470702300701, 25.6131070939596, 38.3498594232174, 57.444937683935, 80.104724288839, 100)c(1.44120553086336, 1.8529297699299, 0.613107093959599, 0.849859423217403, 7.444937683935, 17.604724288839, 25)c(NA, 14.8234381594392, 2.45242837583839, 2.26629179524641, 14.88987536787, 28.1675588621424, 33.3333333333333) - [20250519_005111.]: Logging df_agg: CpG#3 - [20250519_005111.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75)[20250519_005111.]: c(1.44120553086336, 10.6470702300701, 25.6131070939596, 38.3498594232174, 57.444937683935, 80.104724288839, 100)[20250519_005111.]: c(1.44120553086336, 1.8529297699299, 0.613107093959599, 0.849859423217403, 7.444937683935, 17.604724288839, 25)[20250519_005111.]: c(NA, 14.8234381594392, 2.45242837583839, 2.26629179524641, 14.88987536787, 28.1675588621424, 33.3333333333333) - [20250519_005111.]: Entered 'hyperbolic_regression'-Function - [20250519_005111.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005111.]: Entered 'cubic_regression'-Function - [20250519_005111.]: 'cubic_regression': minmax = FALSE - [20250519_005111.]: # CpG-site: CpG#4 + [20250519_005947.]: Logging df_agg: CpG#3 + [20250519_005947.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75)[20250519_005947.]: c(1.44120553086336, 10.6470702300701, 25.6131070939596, 38.3498594232174, 57.444937683935, 80.104724288839, 100)[20250519_005947.]: c(1.44120553086336, 1.8529297699299, 0.613107093959599, 0.849859423217403, 7.444937683935, 17.604724288839, 25)[20250519_005947.]: c(NA, 14.8234381594392, 2.45242837583839, 2.26629179524641, 14.88987536787, 28.1675588621424, 33.3333333333333) + [20250519_005947.]: Entered 'hyperbolic_regression'-Function + [20250519_005947.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005947.]: Entered 'cubic_regression'-Function + [20250519_005947.]: 'cubic_regression': minmax = FALSE + [20250519_005947.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5)c(0.858643525693588, 12.2719279243844, 24.3781797668421, 41.5866572861968, 57.86781708526, 100)c(0.858643525693588, 0.2280720756156, 0.621820233157901, 4.0866572861968, 7.86781708526, 37.5)c(NA, 1.8245766049248, 2.48728093263161, 10.8977527631915, 15.73563417052, 60) - [20250519_005111.]: Logging df_agg: CpG#4 - [20250519_005111.]: c(0, 12.5, 25, 37.5, 50, 62.5)[20250519_005111.]: c(0.858643525693588, 12.2719279243844, 24.3781797668421, 41.5866572861968, 57.86781708526, 100)[20250519_005111.]: c(0.858643525693588, 0.2280720756156, 0.621820233157901, 4.0866572861968, 7.86781708526, 37.5)[20250519_005111.]: c(NA, 1.8245766049248, 2.48728093263161, 10.8977527631915, 15.73563417052, 60) - [20250519_005111.]: Entered 'hyperbolic_regression'-Function - [20250519_005111.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005111.]: Entered 'cubic_regression'-Function - [20250519_005111.]: 'cubic_regression': minmax = FALSE - [20250519_005111.]: # CpG-site: CpG#5 + [20250519_005947.]: Logging df_agg: CpG#4 + [20250519_005947.]: c(0, 12.5, 25, 37.5, 50, 62.5)[20250519_005947.]: c(0.858643525693588, 12.2719279243844, 24.3781797668421, 41.5866572861968, 57.86781708526, 100)[20250519_005947.]: c(0.858643525693588, 0.2280720756156, 0.621820233157901, 4.0866572861968, 7.86781708526, 37.5)[20250519_005947.]: c(NA, 1.8245766049248, 2.48728093263161, 10.8977527631915, 15.73563417052, 60) + [20250519_005947.]: Entered 'hyperbolic_regression'-Function + [20250519_005947.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005947.]: Entered 'cubic_regression'-Function + [20250519_005947.]: 'cubic_regression': minmax = FALSE + [20250519_005947.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.45850947960108, 10.2941313684311, 24.4773429557174, 37.0974401416089, 49.5067234953282, 59.9312428933937, 72.5628584782902, 81.5490289118528, 94.898442253889)c(1.45850947960108, 2.2058686315689, 0.5226570442826, 0.402559858391101, 0.493276504671798, 2.5687571066063, 2.4371415217098, 5.9509710881472, 5.101557746111)c(NA, 17.6469490525512, 2.0906281771304, 1.0734929557096, 0.986553009343595, 4.11001137057008, 3.24952202894641, 6.80110981502537, 5.101557746111) - [20250519_005111.]: Logging df_agg: CpG#5 - [20250519_005111.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005111.]: c(1.45850947960108, 10.2941313684311, 24.4773429557174, 37.0974401416089, 49.5067234953282, 59.9312428933937, 72.5628584782902, 81.5490289118528, 94.898442253889)[20250519_005111.]: c(1.45850947960108, 2.2058686315689, 0.5226570442826, 0.402559858391101, 0.493276504671798, 2.5687571066063, 2.4371415217098, 5.9509710881472, 5.101557746111)[20250519_005111.]: c(NA, 17.6469490525512, 2.0906281771304, 1.0734929557096, 0.986553009343595, 4.11001137057008, 3.24952202894641, 6.80110981502537, 5.101557746111) - [20250519_005111.]: Entered 'hyperbolic_regression'-Function - [20250519_005111.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005112.]: Entered 'cubic_regression'-Function - [20250519_005112.]: 'cubic_regression': minmax = FALSE - [20250519_005112.]: # CpG-site: CpG#6 + [20250519_005947.]: Logging df_agg: CpG#5 + [20250519_005947.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005947.]: c(1.45850947960108, 10.2941313684311, 24.4773429557174, 37.0974401416089, 49.5067234953282, 59.9312428933937, 72.5628584782902, 81.5490289118528, 94.898442253889)[20250519_005947.]: c(1.45850947960108, 2.2058686315689, 0.5226570442826, 0.402559858391101, 0.493276504671798, 2.5687571066063, 2.4371415217098, 5.9509710881472, 5.101557746111)[20250519_005947.]: c(NA, 17.6469490525512, 2.0906281771304, 1.0734929557096, 0.986553009343595, 4.11001137057008, 3.24952202894641, 6.80110981502537, 5.101557746111) + [20250519_005947.]: Entered 'hyperbolic_regression'-Function + [20250519_005947.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005947.]: Entered 'cubic_regression'-Function + [20250519_005947.]: 'cubic_regression': minmax = FALSE + [20250519_005947.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)c(0.349158802372062, 11.7651478823068, 27.019511576095, 36.8015127603397, 54.3503397041729, 73.8730379134395, 86.2591171327579, 100)c(0.349158802372062, 0.734852117693199, 2.019511576095, 0.698487239660302, 4.3503397041729, 11.3730379134395, 11.2591171327579, 12.5)c(NA, 5.87881694154559, 8.07804630438, 1.86263263909414, 8.70067940834581, 18.1968606615032, 15.0121561770105, 14.2857142857143) - [20250519_005112.]: Logging df_agg: CpG#6 - [20250519_005112.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)[20250519_005112.]: c(0.349158802372062, 11.7651478823068, 27.019511576095, 36.8015127603397, 54.3503397041729, 73.8730379134395, 86.2591171327579, 100)[20250519_005112.]: c(0.349158802372062, 0.734852117693199, 2.019511576095, 0.698487239660302, 4.3503397041729, 11.3730379134395, 11.2591171327579, 12.5)[20250519_005112.]: c(NA, 5.87881694154559, 8.07804630438, 1.86263263909414, 8.70067940834581, 18.1968606615032, 15.0121561770105, 14.2857142857143) - [20250519_005112.]: Entered 'hyperbolic_regression'-Function - [20250519_005112.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005112.]: Entered 'cubic_regression'-Function - [20250519_005112.]: 'cubic_regression': minmax = FALSE - [20250519_005112.]: # CpG-site: CpG#7 + [20250519_005947.]: Logging df_agg: CpG#6 + [20250519_005947.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)[20250519_005947.]: c(0.349158802372062, 11.7651478823068, 27.019511576095, 36.8015127603397, 54.3503397041729, 73.8730379134395, 86.2591171327579, 100)[20250519_005947.]: c(0.349158802372062, 0.734852117693199, 2.019511576095, 0.698487239660302, 4.3503397041729, 11.3730379134395, 11.2591171327579, 12.5)[20250519_005947.]: c(NA, 5.87881694154559, 8.07804630438, 1.86263263909414, 8.70067940834581, 18.1968606615032, 15.0121561770105, 14.2857142857143) + [20250519_005947.]: Entered 'hyperbolic_regression'-Function + [20250519_005947.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005948.]: Entered 'cubic_regression'-Function + [20250519_005948.]: 'cubic_regression': minmax = FALSE + [20250519_005948.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5)c(2.12599911899281, 9.99133393413891, 23.1036438590861, 42.6085296447297)c(2.12599911899281, 2.50866606586109, 1.8963561409139, 5.1085296447297)c(NA, 20.0693285268887, 7.58542456365559, 13.6227457192792) - [20250519_005112.]: Logging df_agg: CpG#7 - [20250519_005112.]: c(0, 12.5, 25, 37.5)[20250519_005112.]: c(2.12599911899281, 9.99133393413891, 23.1036438590861, 42.6085296447297)[20250519_005112.]: c(2.12599911899281, 2.50866606586109, 1.8963561409139, 5.1085296447297)[20250519_005112.]: c(NA, 20.0693285268887, 7.58542456365559, 13.6227457192792) - [20250519_005112.]: Entered 'hyperbolic_regression'-Function - [20250519_005112.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005112.]: Entered 'cubic_regression'-Function - [20250519_005112.]: 'cubic_regression': minmax = FALSE - [20250519_005112.]: # CpG-site: CpG#8 + [20250519_005948.]: Logging df_agg: CpG#7 + [20250519_005948.]: c(0, 12.5, 25, 37.5)[20250519_005948.]: c(2.12599911899281, 9.99133393413891, 23.1036438590861, 42.6085296447297)[20250519_005948.]: c(2.12599911899281, 2.50866606586109, 1.8963561409139, 5.1085296447297)[20250519_005948.]: c(NA, 20.0693285268887, 7.58542456365559, 13.6227457192792) + [20250519_005948.]: Entered 'hyperbolic_regression'-Function + [20250519_005948.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005948.]: Entered 'cubic_regression'-Function + [20250519_005948.]: 'cubic_regression': minmax = FALSE + [20250519_005948.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.0181464298155, 9.50518258466437, 26.4243334649289, 34.6620160335629, 50.9321119457565, 62.2712954366094, 74.1538645100711, 76.6511844577497, 94.3306625091067)c(2.0181464298155, 2.99481741533563, 1.4243334649289, 2.8379839664371, 0.932111945756503, 0.2287045633906, 0.846135489928898, 10.8488155422503, 5.6693374908933)c(NA, 23.958539322685, 5.6973338597156, 7.56795724383227, 1.86422389151301, 0.365927301424961, 1.12818065323853, 12.3986463340004, 5.6693374908933) - [20250519_005112.]: Logging df_agg: CpG#8 - [20250519_005112.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005112.]: c(2.0181464298155, 9.50518258466437, 26.4243334649289, 34.6620160335629, 50.9321119457565, 62.2712954366094, 74.1538645100711, 76.6511844577497, 94.3306625091067)[20250519_005112.]: c(2.0181464298155, 2.99481741533563, 1.4243334649289, 2.8379839664371, 0.932111945756503, 0.2287045633906, 0.846135489928898, 10.8488155422503, 5.6693374908933)[20250519_005112.]: c(NA, 23.958539322685, 5.6973338597156, 7.56795724383227, 1.86422389151301, 0.365927301424961, 1.12818065323853, 12.3986463340004, 5.6693374908933) - [20250519_005112.]: Entered 'hyperbolic_regression'-Function - [20250519_005112.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005112.]: Entered 'cubic_regression'-Function - [20250519_005112.]: 'cubic_regression': minmax = FALSE - [20250519_005112.]: # CpG-site: CpG#9 + [20250519_005948.]: Logging df_agg: CpG#8 + [20250519_005948.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005948.]: c(2.0181464298155, 9.50518258466437, 26.4243334649289, 34.6620160335629, 50.9321119457565, 62.2712954366094, 74.1538645100711, 76.6511844577497, 94.3306625091067)[20250519_005948.]: c(2.0181464298155, 2.99481741533563, 1.4243334649289, 2.8379839664371, 0.932111945756503, 0.2287045633906, 0.846135489928898, 10.8488155422503, 5.6693374908933)[20250519_005948.]: c(NA, 23.958539322685, 5.6973338597156, 7.56795724383227, 1.86422389151301, 0.365927301424961, 1.12818065323853, 12.3986463340004, 5.6693374908933) + [20250519_005948.]: Entered 'hyperbolic_regression'-Function + [20250519_005948.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005948.]: Entered 'cubic_regression'-Function + [20250519_005948.]: 'cubic_regression': minmax = FALSE + [20250519_005948.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5)c(1.47402616445883, 10.1833646164297, 27.0440334834678, 45.8634394698265)c(1.47402616445883, 2.3166353835703, 2.0440334834678, 8.3634394698265)c(NA, 18.5330830685624, 8.1761339338712, 22.3025052528707) - [20250519_005112.]: Logging df_agg: CpG#9 - [20250519_005112.]: c(0, 12.5, 25, 37.5)[20250519_005112.]: c(1.47402616445883, 10.1833646164297, 27.0440334834678, 45.8634394698265)[20250519_005112.]: c(1.47402616445883, 2.3166353835703, 2.0440334834678, 8.3634394698265)[20250519_005112.]: c(NA, 18.5330830685624, 8.1761339338712, 22.3025052528707) - [20250519_005112.]: Entered 'hyperbolic_regression'-Function - [20250519_005112.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005112.]: Entered 'cubic_regression'-Function - [20250519_005112.]: 'cubic_regression': minmax = FALSE - [20250519_005112.]: # CpG-site: row_means + [20250519_005948.]: Logging df_agg: CpG#9 + [20250519_005948.]: c(0, 12.5, 25, 37.5)[20250519_005948.]: c(1.47402616445883, 10.1833646164297, 27.0440334834678, 45.8634394698265)[20250519_005948.]: c(1.47402616445883, 2.3166353835703, 2.0440334834678, 8.3634394698265)[20250519_005948.]: c(NA, 18.5330830685624, 8.1761339338712, 22.3025052528707) + [20250519_005948.]: Entered 'hyperbolic_regression'-Function + [20250519_005948.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005948.]: Entered 'cubic_regression'-Function + [20250519_005948.]: 'cubic_regression': minmax = FALSE + [20250519_005948.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5)c(1.28794987665983, 10.9885302801468, 25.950937077156, 39.5179682033924, 60.2624119769469, 87.2132000752344)c(1.28794987665983, 1.5114697198532, 0.950937077155999, 2.0179682033924, 10.2624119769469, 24.7132000752344)c(NA, 12.0917577588256, 3.80374830862399, 5.38124854237973, 20.5248239538938, 39.541120120375) - [20250519_005112.]: Logging df_agg: row_means - [20250519_005112.]: c(0, 12.5, 25, 37.5, 50, 62.5)[20250519_005112.]: c(1.28794987665983, 10.9885302801468, 25.950937077156, 39.5179682033924, 60.2624119769469, 87.2132000752344)[20250519_005112.]: c(1.28794987665983, 1.5114697198532, 0.950937077155999, 2.0179682033924, 10.2624119769469, 24.7132000752344)[20250519_005112.]: c(NA, 12.0917577588256, 3.80374830862399, 5.38124854237973, 20.5248239538938, 39.541120120375) - [20250519_005112.]: Entered 'hyperbolic_regression'-Function - [20250519_005112.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005113.]: Entered 'cubic_regression'-Function - [20250519_005113.]: 'cubic_regression': minmax = FALSE - [20250519_005113.]: Entered 'solving_equations'-Function - [20250519_005113.]: Solving hyperbolic regression for CpG#1 + [20250519_005948.]: Logging df_agg: row_means + [20250519_005948.]: c(0, 12.5, 25, 37.5, 50, 62.5)[20250519_005948.]: c(1.28794987665983, 10.9885302801468, 25.950937077156, 39.5179682033924, 60.2624119769469, 87.2132000752344)[20250519_005948.]: c(1.28794987665983, 1.5114697198532, 0.950937077155999, 2.0179682033924, 10.2624119769469, 24.7132000752344)[20250519_005948.]: c(NA, 12.0917577588256, 3.80374830862399, 5.38124854237973, 20.5248239538938, 39.541120120375) + [20250519_005948.]: Entered 'hyperbolic_regression'-Function + [20250519_005948.]: 'hyperbolic_regression': minmax = FALSE + [20250519_005948.]: Entered 'cubic_regression'-Function + [20250519_005948.]: 'cubic_regression': minmax = FALSE + [20250519_005949.]: Entered 'solving_equations'-Function + [20250519_005949.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 79.8672902891399 - [20250519_005113.]: Samplename: Sample#1 + [20250519_005949.]: Samplename: Sample#1 Root: 79.867 --> Root in between the borders! Added to results. Hyperbolic solved: 29.789995526108 - [20250519_005113.]: Samplename: Sample#10 + [20250519_005949.]: Samplename: Sample#10 Root: 29.79 --> Root in between the borders! Added to results. Hyperbolic solved: 41.6525103283956 - [20250519_005113.]: Samplename: Sample#2 + [20250519_005949.]: Samplename: Sample#2 Root: 41.653 --> Root in between the borders! Added to results. Hyperbolic solved: 57.4651672980431 - [20250519_005113.]: Samplename: Sample#3 + [20250519_005949.]: Samplename: Sample#3 Root: 57.465 --> Root in between the borders! Added to results. Hyperbolic solved: 9.20070553821487 - [20250519_005113.]: Samplename: Sample#4 + [20250519_005949.]: Samplename: Sample#4 Root: 9.201 --> Root in between the borders! Added to results. Hyperbolic solved: 21.8059429711637 - [20250519_005113.]: Samplename: Sample#5 + [20250519_005949.]: Samplename: Sample#5 Root: 21.806 --> Root in between the borders! Added to results. Hyperbolic solved: 23.0837787089758 - [20250519_005113.]: Samplename: Sample#6 + [20250519_005949.]: Samplename: Sample#6 Root: 23.084 --> Root in between the borders! Added to results. Hyperbolic solved: 45.5033907021902 - [20250519_005113.]: Samplename: Sample#7 + [20250519_005949.]: Samplename: Sample#7 Root: 45.503 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6987316769908 - [20250519_005113.]: Samplename: Sample#8 + [20250519_005949.]: Samplename: Sample#8 Root: 85.699 --> Root in between the borders! Added to results. Hyperbolic solved: -3.66512538562846 - [20250519_005113.]: Samplename: Sample#9 + [20250519_005949.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -3.665 --> '-10 < root < 0' --> substitute 0 - [20250519_005113.]: Solving hyperbolic regression for CpG#2 + [20250519_005949.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 76.7230240612756 - [20250519_005113.]: Samplename: Sample#1 + [20250519_005949.]: Samplename: Sample#1 Root: 76.723 --> Root in between the borders! Added to results. Hyperbolic solved: 30.992543297941 - [20250519_005113.]: Samplename: Sample#10 + [20250519_005949.]: Samplename: Sample#10 Root: 30.993 --> Root in between the borders! Added to results. Hyperbolic solved: 42.6812361010669 - [20250519_005113.]: Samplename: Sample#2 + [20250519_005949.]: Samplename: Sample#2 Root: 42.681 --> Root in between the borders! Added to results. Hyperbolic solved: 58.933980239834 - [20250519_005113.]: Samplename: Sample#3 + [20250519_005949.]: Samplename: Sample#3 Root: 58.934 --> Root in between the borders! Added to results. Hyperbolic solved: 4.98024265777963 - [20250519_005113.]: Samplename: Sample#4 + [20250519_005949.]: Samplename: Sample#4 Root: 4.98 --> Root in between the borders! Added to results. Hyperbolic solved: 20.2481111308688 - [20250519_005113.]: Samplename: Sample#5 + [20250519_005949.]: Samplename: Sample#5 Root: 20.248 --> Root in between the borders! Added to results. Hyperbolic solved: 17.813386088098 - [20250519_005113.]: Samplename: Sample#6 + [20250519_005949.]: Samplename: Sample#6 Root: 17.813 --> Root in between the borders! Added to results. Hyperbolic solved: 40.9178176381316 - [20250519_005113.]: Samplename: Sample#7 + [20250519_005949.]: Samplename: Sample#7 Root: 40.918 --> Root in between the borders! Added to results. Hyperbolic solved: 86.0453226524415 - [20250519_005113.]: Samplename: Sample#8 + [20250519_005949.]: Samplename: Sample#8 Root: 86.045 --> Root in between the borders! Added to results. Hyperbolic solved: 3.11582988095862 - [20250519_005113.]: Samplename: Sample#9 + [20250519_005949.]: Samplename: Sample#9 Root: 3.116 --> Root in between the borders! Added to results. - [20250519_005113.]: Solving hyperbolic regression for CpG#3 + [20250519_005949.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 74.934721803959 - [20250519_005113.]: Samplename: Sample#1 + [20250519_005949.]: Samplename: Sample#1 Root: 74.935 --> Root in between the borders! Added to results. Hyperbolic solved: 27.6843475257834 - [20250519_005113.]: Samplename: Sample#10 + [20250519_005949.]: Samplename: Sample#10 Root: 27.684 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8518905782652 - [20250519_005113.]: Samplename: Sample#2 + [20250519_005949.]: Samplename: Sample#2 Root: 41.852 --> Root in between the borders! Added to results. Hyperbolic solved: 55.8323554649537 - [20250519_005113.]: Samplename: Sample#3 + [20250519_005949.]: Samplename: Sample#3 Root: 55.832 --> Root in between the borders! Added to results. Hyperbolic solved: 8.03516016616019 - [20250519_005113.]: Samplename: Sample#4 + [20250519_005949.]: Samplename: Sample#4 Root: 8.035 --> Root in between the borders! Added to results. Hyperbolic solved: 24.1065510576898 - [20250519_005113.]: Samplename: Sample#5 + [20250519_005949.]: Samplename: Sample#5 Root: 24.107 --> Root in between the borders! Added to results. Hyperbolic solved: 26.2418954231184 - [20250519_005113.]: Samplename: Sample#6 + [20250519_005949.]: Samplename: Sample#6 Root: 26.242 --> Root in between the borders! Added to results. Hyperbolic solved: 44.0943580511304 - [20250519_005113.]: Samplename: Sample#7 + [20250519_005949.]: Samplename: Sample#7 Root: 44.094 --> Root in between the borders! Added to results. Hyperbolic solved: 85.8277140531902 - [20250519_005113.]: Samplename: Sample#8 + [20250519_005949.]: Samplename: Sample#8 Root: 85.828 --> Root in between the borders! Added to results. Hyperbolic solved: -0.666486811581751 - [20250519_005113.]: Samplename: Sample#9 + [20250519_005949.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.666 --> '-10 < root < 0' --> substitute 0 - [20250519_005113.]: Solving hyperbolic regression for CpG#4 + [20250519_005949.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 76.349372671628 - [20250519_005113.]: Samplename: Sample#1 + [20250519_005949.]: Samplename: Sample#1 Root: 76.349 --> Root in between the borders! Added to results. Hyperbolic solved: 28.2567843724913 - [20250519_005113.]: Samplename: Sample#10 + [20250519_005949.]: Samplename: Sample#10 Root: 28.257 --> Root in between the borders! Added to results. Hyperbolic solved: 43.4088841408467 - [20250519_005113.]: Samplename: Sample#2 + [20250519_005949.]: Samplename: Sample#2 Root: 43.409 --> Root in between the borders! Added to results. Hyperbolic solved: 58.5433972042602 - [20250519_005113.]: Samplename: Sample#3 + [20250519_005949.]: Samplename: Sample#3 Root: 58.543 --> Root in between the borders! Added to results. Hyperbolic solved: 10.3086704325003 - [20250519_005113.]: Samplename: Sample#4 + [20250519_005949.]: Samplename: Sample#4 Root: 10.309 --> Root in between the borders! Added to results. Hyperbolic solved: 22.182986320858 - [20250519_005113.]: Samplename: Sample#5 + [20250519_005949.]: Samplename: Sample#5 Root: 22.183 --> Root in between the borders! Added to results. Hyperbolic solved: 27.1337081900258 - [20250519_005113.]: Samplename: Sample#6 + [20250519_005949.]: Samplename: Sample#6 Root: 27.134 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8320127104856 - [20250519_005113.]: Samplename: Sample#7 + [20250519_005949.]: Samplename: Sample#7 Root: 41.832 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6888498156134 - [20250519_005113.]: Samplename: Sample#8 + [20250519_005949.]: Samplename: Sample#8 Root: 85.689 --> Root in between the borders! Added to results. Hyperbolic solved: 2.42230396811441 - [20250519_005113.]: Samplename: Sample#9 + [20250519_005949.]: Samplename: Sample#9 Root: 2.422 --> Root in between the borders! Added to results. - [20250519_005113.]: Solving hyperbolic regression for CpG#5 + [20250519_005949.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 72.8126507590781 - [20250519_005113.]: Samplename: Sample#1 + [20250519_005949.]: Samplename: Sample#1 Root: 72.813 --> Root in between the borders! Added to results. Hyperbolic solved: 26.4206774223762 - [20250519_005113.]: Samplename: Sample#10 + [20250519_005949.]: Samplename: Sample#10 Root: 26.421 --> Root in between the borders! Added to results. Hyperbolic solved: 44.2738516315831 - [20250519_005113.]: Samplename: Sample#2 + [20250519_005949.]: Samplename: Sample#2 Root: 44.274 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5324088748019 - [20250519_005113.]: Samplename: Sample#3 + [20250519_005949.]: Samplename: Sample#3 Root: 53.532 --> Root in between the borders! Added to results. Hyperbolic solved: 10.0373628417333 - [20250519_005113.]: Samplename: Sample#4 + [20250519_005949.]: Samplename: Sample#4 Root: 10.037 --> Root in between the borders! Added to results. Hyperbolic solved: 22.8732969704156 - [20250519_005113.]: Samplename: Sample#5 + [20250519_005949.]: Samplename: Sample#5 Root: 22.873 --> Root in between the borders! Added to results. Hyperbolic solved: 25.9417756406497 - [20250519_005113.]: Samplename: Sample#6 + [20250519_005949.]: Samplename: Sample#6 Root: 25.942 --> Root in between the borders! Added to results. Hyperbolic solved: 42.7944833740993 - [20250519_005113.]: Samplename: Sample#7 + [20250519_005949.]: Samplename: Sample#7 Root: 42.794 --> Root in between the borders! Added to results. Hyperbolic solved: 88.8740708807914 - [20250519_005113.]: Samplename: Sample#8 + [20250519_005949.]: Samplename: Sample#8 Root: 88.874 --> Root in between the borders! Added to results. Hyperbolic solved: 2.34646946564885 - [20250519_005113.]: Samplename: Sample#9 + [20250519_005949.]: Samplename: Sample#9 Root: 2.346 --> Root in between the borders! Added to results. - [20250519_005113.]: Solving hyperbolic regression for CpG#6 + [20250519_005949.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 79.2782328649447 - [20250519_005113.]: Samplename: Sample#1 + [20250519_005949.]: Samplename: Sample#1 Root: 79.278 --> Root in between the borders! Added to results. Hyperbolic solved: 30.2013285011234 - [20250519_005113.]: Samplename: Sample#10 + [20250519_005949.]: Samplename: Sample#10 Root: 30.201 --> Root in between the borders! Added to results. Hyperbolic solved: 41.847545497608 - [20250519_005113.]: Samplename: Sample#2 + [20250519_005949.]: Samplename: Sample#2 Root: 41.848 --> Root in between the borders! Added to results. Hyperbolic solved: 56.8424863688491 - [20250519_005113.]: Samplename: Sample#3 + [20250519_005949.]: Samplename: Sample#3 Root: 56.842 --> Root in between the borders! Added to results. Hyperbolic solved: 8.87859681588532 - [20250519_005113.]: Samplename: Sample#4 + [20250519_005949.]: Samplename: Sample#4 Root: 8.879 --> Root in between the borders! Added to results. Hyperbolic solved: 18.6902176438696 - [20250519_005113.]: Samplename: Sample#5 + [20250519_005949.]: Samplename: Sample#5 Root: 18.69 --> Root in between the borders! Added to results. Hyperbolic solved: 29.9310083954849 - [20250519_005113.]: Samplename: Sample#6 + [20250519_005949.]: Samplename: Sample#6 Root: 29.931 --> Root in between the borders! Added to results. Hyperbolic solved: 42.814964037839 - [20250519_005113.]: Samplename: Sample#7 + [20250519_005949.]: Samplename: Sample#7 Root: 42.815 --> Root in between the borders! Added to results. Hyperbolic solved: 86.7503685829287 - [20250519_005113.]: Samplename: Sample#8 + [20250519_005949.]: Samplename: Sample#8 Root: 86.75 --> Root in between the borders! Added to results. Hyperbolic solved: 1.51518138323643 - [20250519_005113.]: Samplename: Sample#9 + [20250519_005949.]: Samplename: Sample#9 Root: 1.515 --> Root in between the borders! Added to results. - [20250519_005113.]: Solving hyperbolic regression for CpG#7 + [20250519_005949.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 78.2568233815803 - [20250519_005113.]: Samplename: Sample#1 + [20250519_005949.]: Samplename: Sample#1 Root: 78.257 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4888594923858 - [20250519_005113.]: Samplename: Sample#10 + [20250519_005949.]: Samplename: Sample#10 Root: 25.489 --> Root in between the borders! Added to results. Hyperbolic solved: 47.3714242177043 - [20250519_005113.]: Samplename: Sample#2 + [20250519_005949.]: Samplename: Sample#2 Root: 47.371 --> Root in between the borders! Added to results. Hyperbolic solved: 58.3144949237501 - [20250519_005113.]: Samplename: Sample#3 + [20250519_005949.]: Samplename: Sample#3 Root: 58.314 --> Root in between the borders! Added to results. Hyperbolic solved: 11.7212806729981 - [20250519_005113.]: Samplename: Sample#4 + [20250519_005949.]: Samplename: Sample#4 Root: 11.721 --> Root in between the borders! Added to results. Hyperbolic solved: 25.3798682873976 - [20250519_005113.]: Samplename: Sample#5 + [20250519_005949.]: Samplename: Sample#5 Root: 25.38 --> Root in between the borders! Added to results. Hyperbolic solved: 29.4096493828253 - [20250519_005113.]: Samplename: Sample#6 + [20250519_005949.]: Samplename: Sample#6 Root: 29.41 --> Root in between the borders! Added to results. Hyperbolic solved: 44.5756969430888 - [20250519_005113.]: Samplename: Sample#7 + [20250519_005949.]: Samplename: Sample#7 Root: 44.576 --> Root in between the borders! Added to results. Hyperbolic solved: 85.9631455147929 - [20250519_005113.]: Samplename: Sample#8 + [20250519_005949.]: Samplename: Sample#8 Root: 85.963 --> Root in between the borders! Added to results. Hyperbolic solved: -4.16459229634232 - [20250519_005113.]: Samplename: Sample#9 + [20250519_005949.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -4.165 --> '-10 < root < 0' --> substitute 0 - [20250519_005113.]: Solving hyperbolic regression for CpG#8 + [20250519_005949.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 72.9696678780879 - [20250519_005113.]: Samplename: Sample#1 + [20250519_005949.]: Samplename: Sample#1 Root: 72.97 --> Root in between the borders! Added to results. Hyperbolic solved: 27.825096438612 - [20250519_005113.]: Samplename: Sample#10 + [20250519_005949.]: Samplename: Sample#10 Root: 27.825 --> Root in between the borders! Added to results. Hyperbolic solved: 34.9068412412515 - [20250519_005113.]: Samplename: Sample#2 + [20250519_005949.]: Samplename: Sample#2 Root: 34.907 --> Root in between the borders! Added to results. Hyperbolic solved: 59.1637501357335 - [20250519_005113.]: Samplename: Sample#3 + [20250519_005949.]: Samplename: Sample#3 Root: 59.164 --> Root in between the borders! Added to results. Hyperbolic solved: 9.6464397671905 - [20250519_005113.]: Samplename: Sample#4 + [20250519_005949.]: Samplename: Sample#4 Root: 9.646 --> Root in between the borders! Added to results. Hyperbolic solved: 19.3522593577541 - [20250519_005113.]: Samplename: Sample#5 + [20250519_005949.]: Samplename: Sample#5 Root: 19.352 --> Root in between the borders! Added to results. Hyperbolic solved: 38.5571468548369 - [20250519_005113.]: Samplename: Sample#6 + [20250519_005949.]: Samplename: Sample#6 Root: 38.557 --> Root in between the borders! Added to results. Hyperbolic solved: 44.775966890921 - [20250519_005113.]: Samplename: Sample#7 + [20250519_005949.]: Samplename: Sample#7 Root: 44.776 --> Root in between the borders! Added to results. Hyperbolic solved: 84.8599747802978 - [20250519_005113.]: Samplename: Sample#8 + [20250519_005949.]: Samplename: Sample#8 Root: 84.86 --> Root in between the borders! Added to results. Hyperbolic solved: -2.05045739684141 - [20250519_005113.]: Samplename: Sample#9 + [20250519_005949.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.05 --> '-10 < root < 0' --> substitute 0 - [20250519_005113.]: Solving hyperbolic regression for CpG#9 + [20250519_005949.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 81.0413625074508 - [20250519_005113.]: Samplename: Sample#1 + [20250519_005949.]: Samplename: Sample#1 Root: 81.041 --> Root in between the borders! Added to results. Hyperbolic solved: 26.6440232383638 - [20250519_005113.]: Samplename: Sample#10 + [20250519_005949.]: Samplename: Sample#10 Root: 26.644 --> Root in between the borders! Added to results. Hyperbolic solved: 45.704400902509 - [20250519_005113.]: Samplename: Sample#2 + [20250519_005949.]: Samplename: Sample#2 Root: 45.704 --> Root in between the borders! Added to results. Hyperbolic solved: 56.9765444860285 - [20250519_005113.]: Samplename: Sample#3 + [20250519_005949.]: Samplename: Sample#3 Root: 56.977 --> Root in between the borders! Added to results. Hyperbolic solved: 6.81226926030202 - [20250519_005113.]: Samplename: Sample#4 + [20250519_005949.]: Samplename: Sample#4 Root: 6.812 --> Root in between the borders! Added to results. Hyperbolic solved: 22.9283517775063 - [20250519_005113.]: Samplename: Sample#5 + [20250519_005949.]: Samplename: Sample#5 Root: 22.928 --> Root in between the borders! Added to results. Hyperbolic solved: 38.8651260750996 - [20250519_005113.]: Samplename: Sample#6 + [20250519_005949.]: Samplename: Sample#6 Root: 38.865 --> Root in between the borders! Added to results. Hyperbolic solved: 43.6853511825078 - [20250519_005113.]: Samplename: Sample#7 + [20250519_005949.]: Samplename: Sample#7 Root: 43.685 --> Root in between the borders! Added to results. Hyperbolic solved: 88.0152224353095 - [20250519_005113.]: Samplename: Sample#8 + [20250519_005949.]: Samplename: Sample#8 Root: 88.015 --> Root in between the borders! Added to results. Hyperbolic solved: -3.35608361520768 - [20250519_005113.]: Samplename: Sample#9 + [20250519_005949.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -3.356 --> '-10 < root < 0' --> substitute 0 - [20250519_005113.]: Solving hyperbolic regression for row_means + [20250519_005949.]: Solving hyperbolic regression for row_means Hyperbolic solved: 77.069275047056 - [20250519_005113.]: Samplename: Sample#1 + [20250519_005949.]: Samplename: Sample#1 Root: 77.069 --> Root in between the borders! Added to results. Hyperbolic solved: 28.3620003757916 - [20250519_005113.]: Samplename: Sample#10 + [20250519_005949.]: Samplename: Sample#10 Root: 28.362 --> Root in between the borders! Added to results. Hyperbolic solved: 42.5026135474906 - [20250519_005113.]: Samplename: Sample#2 + [20250519_005949.]: Samplename: Sample#2 Root: 42.503 --> Root in between the borders! Added to results. Hyperbolic solved: 57.2972007822813 - [20250519_005113.]: Samplename: Sample#3 + [20250519_005949.]: Samplename: Sample#3 Root: 57.297 --> Root in between the borders! Added to results. Hyperbolic solved: 8.82703592503651 - [20250519_005113.]: Samplename: Sample#4 + [20250519_005949.]: Samplename: Sample#4 Root: 8.827 --> Root in between the borders! Added to results. Hyperbolic solved: 21.8102552608375 - [20250519_005113.]: Samplename: Sample#5 + [20250519_005949.]: Samplename: Sample#5 Root: 21.81 --> Root in between the borders! Added to results. Hyperbolic solved: 28.7228620570755 - [20250519_005113.]: Samplename: Sample#6 + [20250519_005949.]: Samplename: Sample#6 Root: 28.723 --> Root in between the borders! Added to results. Hyperbolic solved: 43.4105062814289 - [20250519_005113.]: Samplename: Sample#7 + [20250519_005949.]: Samplename: Sample#7 Root: 43.411 --> Root in between the borders! Added to results. Hyperbolic solved: 86.4143497902066 - [20250519_005113.]: Samplename: Sample#8 + [20250519_005949.]: Samplename: Sample#8 Root: 86.414 --> Root in between the borders! Added to results. Hyperbolic solved: -0.237025016559672 - [20250519_005113.]: Samplename: Sample#9 + [20250519_005949.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.237 --> '-10 < root < 0' --> substitute 0 - [20250519_005114.]: Entered 'solving_equations'-Function - [20250519_005114.]: Solving hyperbolic regression for CpG#1 + [20250519_005949.]: Entered 'solving_equations'-Function + [20250519_005949.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: -2.23222837469517 - [20250519_005114.]: Samplename: 0 + [20250519_005949.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. @@ -5936,126 +5936,126 @@ Root: -2.232 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.1698391693126 - [20250519_005114.]: Samplename: 12.5 + [20250519_005949.]: Samplename: 12.5 Root: 12.17 --> Root in between the borders! Added to results. Hyperbolic solved: 24.4781729791561 - [20250519_005114.]: Samplename: 25 + [20250519_005949.]: Samplename: 25 Root: 24.478 --> Root in between the borders! Added to results. Hyperbolic solved: 38.1730159074047 - [20250519_005114.]: Samplename: 37.5 + [20250519_005949.]: Samplename: 37.5 Root: 38.173 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3348987967717 - [20250519_005114.]: Samplename: 50 + [20250519_005949.]: Samplename: 50 Root: 52.335 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4582305966058 - [20250519_005114.]: Samplename: 62.5 + [20250519_005949.]: Samplename: 62.5 Root: 65.458 --> Root in between the borders! Added to results. Hyperbolic solved: 75.0090269722885 - [20250519_005114.]: Samplename: 75 + [20250519_005949.]: Samplename: 75 Root: 75.009 --> Root in between the borders! Added to results. Hyperbolic solved: 81.527135738891 - [20250519_005114.]: Samplename: 87.5 + [20250519_005949.]: Samplename: 87.5 Root: 81.527 --> Root in between the borders! Added to results. Hyperbolic solved: 102.400825350044 - [20250519_005114.]: Samplename: 100 + [20250519_005949.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.401 --> '100 < root < 110' --> substitute 100 - [20250519_005114.]: Solving hyperbolic regression for CpG#2 + [20250519_005949.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 1.13663087266051 - [20250519_005114.]: Samplename: 0 + [20250519_005949.]: Samplename: 0 Root: 1.137 --> Root in between the borders! Added to results. Hyperbolic solved: 11.4130277963576 - [20250519_005114.]: Samplename: 12.5 + [20250519_005949.]: Samplename: 12.5 Root: 11.413 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1741025868157 - [20250519_005114.]: Samplename: 25 + [20250519_005949.]: Samplename: 25 Root: 26.174 --> Root in between the borders! Added to results. Hyperbolic solved: 35.1051721736724 - [20250519_005114.]: Samplename: 37.5 + [20250519_005949.]: Samplename: 37.5 Root: 35.105 --> Root in between the borders! Added to results. Hyperbolic solved: 47.6856613240114 - [20250519_005114.]: Samplename: 50 + [20250519_005949.]: Samplename: 50 Root: 47.686 --> Root in between the borders! Added to results. Hyperbolic solved: 67.1442585354696 - [20250519_005114.]: Samplename: 62.5 + [20250519_005949.]: Samplename: 62.5 Root: 67.144 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7646957109439 - [20250519_005114.]: Samplename: 75 + [20250519_005949.]: Samplename: 75 Root: 75.765 --> Root in between the borders! Added to results. Hyperbolic solved: 84.4056634834815 - [20250519_005114.]: Samplename: 87.5 + [20250519_005949.]: Samplename: 87.5 Root: 84.406 --> Root in between the borders! Added to results. Hyperbolic solved: 100.94855337669 - [20250519_005114.]: Samplename: 100 + [20250519_005949.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.949 --> '100 < root < 110' --> substitute 100 - [20250519_005114.]: Solving hyperbolic regression for CpG#3 + [20250519_005949.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 0.512348257854635 - [20250519_005114.]: Samplename: 0 + [20250519_005949.]: Samplename: 0 Root: 0.512 --> Root in between the borders! Added to results. Hyperbolic solved: 10.7523476241811 - [20250519_005114.]: Samplename: 12.5 + [20250519_005949.]: Samplename: 12.5 Root: 10.752 --> Root in between the borders! Added to results. Hyperbolic solved: 25.5218062516799 - [20250519_005114.]: Samplename: 25 + [20250519_005949.]: Samplename: 25 Root: 25.522 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5269315729339 - [20250519_005114.]: Samplename: 37.5 + [20250519_005949.]: Samplename: 37.5 Root: 36.527 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7907738274884 - [20250519_005114.]: Samplename: 50 + [20250519_005949.]: Samplename: 50 Root: 50.791 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8684489988589 - [20250519_005114.]: Samplename: 62.5 + [20250519_005949.]: Samplename: 62.5 Root: 64.868 --> Root in between the borders! Added to results. Hyperbolic solved: 77.5522101073979 - [20250519_005114.]: Samplename: 75 + [20250519_005949.]: Samplename: 75 Root: 77.552 --> Root in between the borders! Added to results. Hyperbolic solved: 80.4372474691229 - [20250519_005114.]: Samplename: 87.5 + [20250519_005949.]: Samplename: 87.5 Root: 80.437 --> Root in between the borders! Added to results. Hyperbolic solved: 102.703772810881 - [20250519_005114.]: Samplename: 100 + [20250519_005949.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.704 --> '100 < root < 110' --> substitute 100 - [20250519_005114.]: Solving hyperbolic regression for CpG#4 + [20250519_005949.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: -0.519513573026794 - [20250519_005114.]: Samplename: 0 + [20250519_005949.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. @@ -6063,126 +6063,126 @@ Root: -0.52 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.4933760051342 - [20250519_005114.]: Samplename: 12.5 + [20250519_005949.]: Samplename: 12.5 Root: 12.493 --> Root in between the borders! Added to results. Hyperbolic solved: 24.2684789512555 - [20250519_005114.]: Samplename: 25 + [20250519_005949.]: Samplename: 25 Root: 24.268 --> Root in between the borders! Added to results. Hyperbolic solved: 38.0816229393195 - [20250519_005114.]: Samplename: 37.5 + [20250519_005949.]: Samplename: 37.5 Root: 38.082 --> Root in between the borders! Added to results. Hyperbolic solved: 48.5842089589187 - [20250519_005114.]: Samplename: 50 + [20250519_005949.]: Samplename: 50 Root: 48.584 --> Root in between the borders! Added to results. Hyperbolic solved: 67.6720983549562 - [20250519_005114.]: Samplename: 62.5 + [20250519_005949.]: Samplename: 62.5 Root: 67.672 --> Root in between the borders! Added to results. Hyperbolic solved: 74.1547759694059 - [20250519_005114.]: Samplename: 75 + [20250519_005949.]: Samplename: 75 Root: 74.155 --> Root in between the borders! Added to results. Hyperbolic solved: 82.8820147905386 - [20250519_005114.]: Samplename: 87.5 + [20250519_005949.]: Samplename: 87.5 Root: 82.882 --> Root in between the borders! Added to results. Hyperbolic solved: 102.078935346876 - [20250519_005114.]: Samplename: 100 + [20250519_005949.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.079 --> '100 < root < 110' --> substitute 100 - [20250519_005114.]: Solving hyperbolic regression for CpG#5 + [20250519_005949.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 2.41558040143479 - [20250519_005114.]: Samplename: 0 + [20250519_005949.]: Samplename: 0 Root: 2.416 --> Root in between the borders! Added to results. Hyperbolic solved: 10.1649584830834 - [20250519_005114.]: Samplename: 12.5 + [20250519_005949.]: Samplename: 12.5 Root: 10.165 --> Root in between the borders! Added to results. Hyperbolic solved: 23.9830670020905 - [20250519_005114.]: Samplename: 25 + [20250519_005949.]: Samplename: 25 Root: 23.983 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2773406385708 - [20250519_005114.]: Samplename: 37.5 + [20250519_005949.]: Samplename: 37.5 Root: 37.277 --> Root in between the borders! Added to results. Hyperbolic solved: 50.8659103346102 - [20250519_005114.]: Samplename: 50 + [20250519_005949.]: Samplename: 50 Root: 50.866 --> Root in between the borders! Added to results. Hyperbolic solved: 62.4341926937382 - [20250519_005114.]: Samplename: 62.5 + [20250519_005949.]: Samplename: 62.5 Root: 62.434 --> Root in between the borders! Added to results. Hyperbolic solved: 76.3914832329149 - [20250519_005114.]: Samplename: 75 + [20250519_005949.]: Samplename: 75 Root: 76.391 --> Root in between the borders! Added to results. Hyperbolic solved: 86.1597399215782 - [20250519_005114.]: Samplename: 87.5 + [20250519_005949.]: Samplename: 87.5 Root: 86.16 --> Root in between the borders! Added to results. Hyperbolic solved: 100.267701841999 - [20250519_005114.]: Samplename: 100 + [20250519_005949.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.268 --> '100 < root < 110' --> substitute 100 - [20250519_005114.]: Solving hyperbolic regression for CpG#6 + [20250519_005949.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 0.138179963459196 - [20250519_005114.]: Samplename: 0 + [20250519_005949.]: Samplename: 0 Root: 0.138 --> Root in between the borders! Added to results. Hyperbolic solved: 11.8635989635202 - [20250519_005114.]: Samplename: 12.5 + [20250519_005949.]: Samplename: 12.5 Root: 11.864 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5108198460409 - [20250519_005114.]: Samplename: 25 + [20250519_005949.]: Samplename: 25 Root: 26.511 --> Root in between the borders! Added to results. Hyperbolic solved: 35.3206004021833 - [20250519_005114.]: Samplename: 37.5 + [20250519_005949.]: Samplename: 37.5 Root: 35.321 --> Root in between the borders! Added to results. Hyperbolic solved: 50.0571987473308 - [20250519_005114.]: Samplename: 50 + [20250519_005949.]: Samplename: 50 Root: 50.057 --> Root in between the borders! Added to results. Hyperbolic solved: 64.9604436607063 - [20250519_005114.]: Samplename: 62.5 + [20250519_005949.]: Samplename: 62.5 Root: 64.96 --> Root in between the borders! Added to results. Hyperbolic solved: 73.6690699266294 - [20250519_005114.]: Samplename: 75 + [20250519_005949.]: Samplename: 75 Root: 73.669 --> Root in between the borders! Added to results. Hyperbolic solved: 87.1267946879604 - [20250519_005114.]: Samplename: 87.5 + [20250519_005949.]: Samplename: 87.5 Root: 87.127 --> Root in between the borders! Added to results. Hyperbolic solved: 100.261842767865 - [20250519_005114.]: Samplename: 100 + [20250519_005949.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.262 --> '100 < root < 110' --> substitute 100 - [20250519_005114.]: Solving hyperbolic regression for CpG#7 + [20250519_005949.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: -1.37239249181669 - [20250519_005114.]: Samplename: 0 + [20250519_005949.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. @@ -6190,85 +6190,85 @@ Root: -1.372 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.199366215418 - [20250519_005114.]: Samplename: 12.5 + [20250519_005949.]: Samplename: 12.5 Root: 10.199 --> Root in between the borders! Added to results. Hyperbolic solved: 24.595295361327 - [20250519_005114.]: Samplename: 25 + [20250519_005949.]: Samplename: 25 Root: 24.595 --> Root in between the borders! Added to results. Hyperbolic solved: 37.8312095633391 - [20250519_005114.]: Samplename: 37.5 + [20250519_005949.]: Samplename: 37.5 Root: 37.831 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5590896582852 - [20250519_005114.]: Samplename: 50 + [20250519_005949.]: Samplename: 50 Root: 53.559 --> Root in between the borders! Added to results. Hyperbolic solved: 65.9367389282431 - [20250519_005114.]: Samplename: 62.5 + [20250519_005949.]: Samplename: 62.5 Root: 65.937 --> Root in between the borders! Added to results. Hyperbolic solved: 75.736370152841 - [20250519_005114.]: Samplename: 75 + [20250519_005949.]: Samplename: 75 Root: 75.736 --> Root in between the borders! Added to results. Hyperbolic solved: 79.433089359524 - [20250519_005114.]: Samplename: 87.5 + [20250519_005949.]: Samplename: 87.5 Root: 79.433 --> Root in between the borders! Added to results. Hyperbolic solved: 103.004516225662 - [20250519_005114.]: Samplename: 100 + [20250519_005949.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 103.005 --> '100 < root < 110' --> substitute 100 - [20250519_005114.]: Solving hyperbolic regression for CpG#8 + [20250519_005949.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 2.80067549526643 - [20250519_005114.]: Samplename: 0 + [20250519_005949.]: Samplename: 0 Root: 2.801 --> Root in between the borders! Added to results. Hyperbolic solved: 9.27534686003087 - [20250519_005114.]: Samplename: 12.5 + [20250519_005949.]: Samplename: 12.5 Root: 9.275 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4762624089952 - [20250519_005114.]: Samplename: 25 + [20250519_005949.]: Samplename: 25 Root: 25.476 --> Root in between the borders! Added to results. Hyperbolic solved: 34.0122098045585 - [20250519_005114.]: Samplename: 37.5 + [20250519_005949.]: Samplename: 37.5 Root: 34.012 --> Root in between the borders! Added to results. Hyperbolic solved: 51.784270967861 - [20250519_005114.]: Samplename: 50 + [20250519_005950.]: Samplename: 50 Root: 51.784 --> Root in between the borders! Added to results. Hyperbolic solved: 64.6732380258789 - [20250519_005114.]: Samplename: 62.5 + [20250519_005950.]: Samplename: 62.5 Root: 64.673 --> Root in between the borders! Added to results. Hyperbolic solved: 78.4327054483192 - [20250519_005114.]: Samplename: 75 + [20250519_005950.]: Samplename: 75 Root: 78.433 --> Root in between the borders! Added to results. Hyperbolic solved: 81.3427308951795 - [20250519_005114.]: Samplename: 87.5 + [20250519_005950.]: Samplename: 87.5 Root: 81.343 --> Root in between the borders! Added to results. Hyperbolic solved: 101.964413523995 - [20250519_005114.]: Samplename: 100 + [20250519_005950.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.964 --> '100 < root < 110' --> substitute 100 - [20250519_005114.]: Solving hyperbolic regression for CpG#9 + [20250519_005950.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: -2.13402763663736 - [20250519_005114.]: Samplename: 0 + [20250519_005950.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. @@ -6276,91 +6276,91 @@ Root: -2.134 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.5081558897243 - [20250519_005114.]: Samplename: 12.5 + [20250519_005950.]: Samplename: 12.5 Root: 10.508 --> Root in between the borders! Added to results. Hyperbolic solved: 26.9163121098627 - [20250519_005114.]: Samplename: 25 + [20250519_005950.]: Samplename: 25 Root: 26.916 --> Root in between the borders! Added to results. Hyperbolic solved: 36.8332915301825 - [20250519_005114.]: Samplename: 37.5 + [20250519_005950.]: Samplename: 37.5 Root: 36.833 --> Root in between the borders! Added to results. Hyperbolic solved: 52.00955 - [20250519_005114.]: Samplename: 50 + [20250519_005950.]: Samplename: 50 Root: 52.01 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8927781893359 - [20250519_005114.]: Samplename: 62.5 + [20250519_005950.]: Samplename: 62.5 Root: 64.893 --> Root in between the borders! Added to results. Hyperbolic solved: 74.566810799042 - [20250519_005114.]: Samplename: 75 + [20250519_005950.]: Samplename: 75 Root: 74.567 --> Root in between the borders! Added to results. Hyperbolic solved: 84.5291854856128 - [20250519_005114.]: Samplename: 87.5 + [20250519_005950.]: Samplename: 87.5 Root: 84.529 --> Root in between the borders! Added to results. Hyperbolic solved: 101.046823491232 - [20250519_005114.]: Samplename: 100 + [20250519_005950.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.047 --> '100 < root < 110' --> substitute 100 - [20250519_005114.]: Solving hyperbolic regression for row_means + [20250519_005950.]: Solving hyperbolic regression for row_means Hyperbolic solved: 0.290936038655697 - [20250519_005114.]: Samplename: 0 + [20250519_005950.]: Samplename: 0 Root: 0.291 --> Root in between the borders! Added to results. Hyperbolic solved: 11.0412364555656 - [20250519_005114.]: Samplename: 12.5 + [20250519_005950.]: Samplename: 12.5 Root: 11.041 --> Root in between the borders! Added to results. Hyperbolic solved: 25.408146358228 - [20250519_005114.]: Samplename: 25 + [20250519_005950.]: Samplename: 25 Root: 25.408 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5243683648753 - [20250519_005114.]: Samplename: 37.5 + [20250519_005950.]: Samplename: 37.5 Root: 36.524 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7348788337891 - [20250519_005114.]: Samplename: 50 + [20250519_005950.]: Samplename: 50 Root: 50.735 --> Root in between the borders! Added to results. Hyperbolic solved: 65.3135169308955 - [20250519_005114.]: Samplename: 62.5 + [20250519_005950.]: Samplename: 62.5 Root: 65.314 --> Root in between the borders! Added to results. Hyperbolic solved: 75.5342663138126 - [20250519_005114.]: Samplename: 75 + [20250519_005950.]: Samplename: 75 Root: 75.534 --> Root in between the borders! Added to results. Hyperbolic solved: 83.2411177153911 - [20250519_005114.]: Samplename: 87.5 + [20250519_005950.]: Samplename: 87.5 Root: 83.241 --> Root in between the borders! Added to results. Hyperbolic solved: 101.666935931185 - [20250519_005114.]: Samplename: 100 + [20250519_005950.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.667 --> '100 < root < 110' --> substitute 100 - [20250519_005114.]: Entered 'clean_dt'-Function - [20250519_005114.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_005114.]: got experimental data - [20250519_005114.]: Entered 'clean_dt'-Function - [20250519_005114.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_005114.]: got calibration data - [20250519_005114.]: + [20250519_005950.]: Entered 'clean_dt'-Function + [20250519_005950.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_005950.]: got experimental data + [20250519_005950.]: Entered 'clean_dt'-Function + [20250519_005950.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_005950.]: got calibration data + [20250519_005950.]: ### Starting with regression calculations ### - [20250519_005114.]: Entered 'regression_type1'-Function + [20250519_005950.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : @@ -6899,253 +6899,253 @@ singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient - [20250519_005114.]: # CpG-site: CpG#1 + [20250519_005950.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_005114.]: Logging df_agg: CpG#1 - [20250519_005114.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005114.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005114.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005114.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005114.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_005114.]: Entered 'hyperbolic_regression'-Function - [20250519_005114.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005114.]: Entered 'cubic_regression'-Function - [20250519_005114.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005114.]: # CpG-site: CpG#2 + [20250519_005950.]: Logging df_agg: CpG#1 + [20250519_005950.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005950.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005950.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005950.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005950.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) + [20250519_005950.]: Entered 'hyperbolic_regression'-Function + [20250519_005950.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005950.]: Entered 'cubic_regression'-Function + [20250519_005950.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005950.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_005114.]: Logging df_agg: CpG#2 - [20250519_005114.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005114.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005114.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005114.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005114.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_005114.]: Entered 'hyperbolic_regression'-Function - [20250519_005115.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005115.]: Entered 'cubic_regression'-Function - [20250519_005115.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005115.]: # CpG-site: CpG#3 + [20250519_005950.]: Logging df_agg: CpG#2 + [20250519_005950.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005950.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005950.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005950.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005950.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) + [20250519_005950.]: Entered 'hyperbolic_regression'-Function + [20250519_005950.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005950.]: Entered 'cubic_regression'-Function + [20250519_005950.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005951.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_005115.]: Logging df_agg: CpG#3 - [20250519_005115.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005115.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005115.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005115.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005115.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_005115.]: Entered 'hyperbolic_regression'-Function - [20250519_005115.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005115.]: Entered 'cubic_regression'-Function - [20250519_005115.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005115.]: # CpG-site: CpG#4 + [20250519_005951.]: Logging df_agg: CpG#3 + [20250519_005951.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005951.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005951.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005951.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005951.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) + [20250519_005951.]: Entered 'hyperbolic_regression'-Function + [20250519_005951.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005951.]: Entered 'cubic_regression'-Function + [20250519_005951.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005951.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_005115.]: Logging df_agg: CpG#4 - [20250519_005115.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005115.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005115.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005115.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005115.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_005115.]: Entered 'hyperbolic_regression'-Function - [20250519_005115.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005116.]: Entered 'cubic_regression'-Function - [20250519_005116.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005116.]: # CpG-site: CpG#5 + [20250519_005951.]: Logging df_agg: CpG#4 + [20250519_005951.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005951.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005951.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005951.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005951.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) + [20250519_005951.]: Entered 'hyperbolic_regression'-Function + [20250519_005951.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005951.]: Entered 'cubic_regression'-Function + [20250519_005951.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005951.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_005116.]: Logging df_agg: CpG#5 - [20250519_005116.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005116.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005116.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005116.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005116.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_005116.]: Entered 'hyperbolic_regression'-Function - [20250519_005116.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005116.]: Entered 'cubic_regression'-Function - [20250519_005116.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005116.]: # CpG-site: CpG#6 + [20250519_005951.]: Logging df_agg: CpG#5 + [20250519_005951.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005951.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005951.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005951.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005951.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) + [20250519_005951.]: Entered 'hyperbolic_regression'-Function + [20250519_005951.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005952.]: Entered 'cubic_regression'-Function + [20250519_005952.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005952.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_005116.]: Logging df_agg: CpG#6 - [20250519_005116.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005116.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005116.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005116.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005116.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_005116.]: Entered 'hyperbolic_regression'-Function - [20250519_005116.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005116.]: Entered 'cubic_regression'-Function - [20250519_005116.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005116.]: # CpG-site: CpG#7 + [20250519_005952.]: Logging df_agg: CpG#6 + [20250519_005952.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005952.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005952.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005952.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005952.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) + [20250519_005952.]: Entered 'hyperbolic_regression'-Function + [20250519_005952.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005952.]: Entered 'cubic_regression'-Function + [20250519_005952.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005952.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_005116.]: Logging df_agg: CpG#7 - [20250519_005116.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005116.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005116.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005116.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005116.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_005116.]: Entered 'hyperbolic_regression'-Function - [20250519_005116.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005117.]: Entered 'cubic_regression'-Function - [20250519_005117.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005117.]: # CpG-site: CpG#8 + [20250519_005952.]: Logging df_agg: CpG#7 + [20250519_005952.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005952.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005952.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005952.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005952.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) + [20250519_005952.]: Entered 'hyperbolic_regression'-Function + [20250519_005952.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005952.]: Entered 'cubic_regression'-Function + [20250519_005952.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005952.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_005117.]: Logging df_agg: CpG#8 - [20250519_005117.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005117.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005117.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005117.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005117.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_005117.]: Entered 'hyperbolic_regression'-Function - [20250519_005117.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005117.]: Entered 'cubic_regression'-Function - [20250519_005117.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005117.]: # CpG-site: CpG#9 + [20250519_005953.]: Logging df_agg: CpG#8 + [20250519_005953.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005953.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005953.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005953.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005953.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) + [20250519_005953.]: Entered 'hyperbolic_regression'-Function + [20250519_005953.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005953.]: Entered 'cubic_regression'-Function + [20250519_005953.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005953.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_005117.]: Logging df_agg: CpG#9 - [20250519_005117.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005117.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005117.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005117.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005117.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_005117.]: Entered 'hyperbolic_regression'-Function - [20250519_005117.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005117.]: Entered 'cubic_regression'-Function - [20250519_005117.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005118.]: # CpG-site: row_means + [20250519_005953.]: Logging df_agg: CpG#9 + [20250519_005953.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005953.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005953.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005953.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005953.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) + [20250519_005953.]: Entered 'hyperbolic_regression'-Function + [20250519_005953.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005953.]: Entered 'cubic_regression'-Function + [20250519_005953.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005953.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_005118.]: Logging df_agg: row_means - [20250519_005118.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005118.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005118.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_005118.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005118.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_005118.]: Entered 'hyperbolic_regression'-Function - [20250519_005118.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005118.]: Entered 'cubic_regression'-Function - [20250519_005118.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005119.]: + [20250519_005953.]: Logging df_agg: row_means + [20250519_005953.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005953.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005953.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_005953.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005953.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) + [20250519_005953.]: Entered 'hyperbolic_regression'-Function + [20250519_005953.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005954.]: Entered 'cubic_regression'-Function + [20250519_005954.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005954.]: ### Starting with regression calculations ### - [20250519_005119.]: Entered 'regression_type1'-Function - [20250519_005119.]: # CpG-site: CpG#1 + [20250519_005954.]: Entered 'regression_type1'-Function + [20250519_005954.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_005119.]: Logging df_agg: CpG#1 - [20250519_005119.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005119.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005119.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005119.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005119.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_005119.]: Entered 'hyperbolic_regression'-Function - [20250519_005119.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005119.]: Entered 'cubic_regression'-Function - [20250519_005119.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005119.]: # CpG-site: CpG#2 + [20250519_005954.]: Logging df_agg: CpG#1 + [20250519_005954.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005954.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005954.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005954.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005954.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) + [20250519_005954.]: Entered 'hyperbolic_regression'-Function + [20250519_005954.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005955.]: Entered 'cubic_regression'-Function + [20250519_005955.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005955.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_005119.]: Logging df_agg: CpG#2 - [20250519_005119.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005119.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005119.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005119.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005119.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_005119.]: Entered 'hyperbolic_regression'-Function - [20250519_005119.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005119.]: Entered 'cubic_regression'-Function - [20250519_005119.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005119.]: # CpG-site: CpG#3 + [20250519_005955.]: Logging df_agg: CpG#2 + [20250519_005955.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005955.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005955.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005955.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005955.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) + [20250519_005955.]: Entered 'hyperbolic_regression'-Function + [20250519_005955.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005955.]: Entered 'cubic_regression'-Function + [20250519_005955.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005955.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_005119.]: Logging df_agg: CpG#3 - [20250519_005119.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005119.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005119.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005119.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005119.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_005119.]: Entered 'hyperbolic_regression'-Function - [20250519_005119.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005120.]: Entered 'cubic_regression'-Function - [20250519_005120.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005120.]: # CpG-site: CpG#4 + [20250519_005955.]: Logging df_agg: CpG#3 + [20250519_005955.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005955.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005955.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005955.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005955.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) + [20250519_005955.]: Entered 'hyperbolic_regression'-Function + [20250519_005955.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005955.]: Entered 'cubic_regression'-Function + [20250519_005955.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005955.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_005120.]: Logging df_agg: CpG#4 - [20250519_005120.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005120.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005120.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005120.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005120.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_005120.]: Entered 'hyperbolic_regression'-Function - [20250519_005120.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005120.]: Entered 'cubic_regression'-Function - [20250519_005120.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005120.]: # CpG-site: CpG#5 + [20250519_005955.]: Logging df_agg: CpG#4 + [20250519_005955.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005955.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005955.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005955.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005955.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) + [20250519_005955.]: Entered 'hyperbolic_regression'-Function + [20250519_005955.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005956.]: Entered 'cubic_regression'-Function + [20250519_005956.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005956.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_005120.]: Logging df_agg: CpG#5 - [20250519_005120.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005120.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005120.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005120.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005120.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_005120.]: Entered 'hyperbolic_regression'-Function - [20250519_005120.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005120.]: Entered 'cubic_regression'-Function - [20250519_005120.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005121.]: # CpG-site: CpG#6 + [20250519_005956.]: Logging df_agg: CpG#5 + [20250519_005956.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005956.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005956.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005956.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005956.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) + [20250519_005956.]: Entered 'hyperbolic_regression'-Function + [20250519_005956.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005956.]: Entered 'cubic_regression'-Function + [20250519_005956.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005956.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_005121.]: Logging df_agg: CpG#6 - [20250519_005121.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005121.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005121.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005121.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005121.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_005121.]: Entered 'hyperbolic_regression'-Function - [20250519_005121.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005121.]: Entered 'cubic_regression'-Function - [20250519_005121.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005121.]: # CpG-site: CpG#7 + [20250519_005956.]: Logging df_agg: CpG#6 + [20250519_005956.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005956.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005956.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005956.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005956.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) + [20250519_005956.]: Entered 'hyperbolic_regression'-Function + [20250519_005956.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005956.]: Entered 'cubic_regression'-Function + [20250519_005956.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005957.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_005121.]: Logging df_agg: CpG#7 - [20250519_005121.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005121.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005121.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005121.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005121.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_005121.]: Entered 'hyperbolic_regression'-Function - [20250519_005121.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005121.]: Entered 'cubic_regression'-Function - [20250519_005121.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005121.]: # CpG-site: CpG#8 + [20250519_005957.]: Logging df_agg: CpG#7 + [20250519_005957.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005957.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005957.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005957.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005957.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) + [20250519_005957.]: Entered 'hyperbolic_regression'-Function + [20250519_005957.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005957.]: Entered 'cubic_regression'-Function + [20250519_005957.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005957.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_005121.]: Logging df_agg: CpG#8 - [20250519_005121.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005121.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005121.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005121.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005121.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_005121.]: Entered 'hyperbolic_regression'-Function - [20250519_005121.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005122.]: Entered 'cubic_regression'-Function - [20250519_005122.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005122.]: # CpG-site: CpG#9 + [20250519_005957.]: Logging df_agg: CpG#8 + [20250519_005957.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005957.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005957.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005957.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005957.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) + [20250519_005957.]: Entered 'hyperbolic_regression'-Function + [20250519_005957.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005957.]: Entered 'cubic_regression'-Function + [20250519_005957.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005957.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_005122.]: Logging df_agg: CpG#9 - [20250519_005122.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005122.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005122.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005122.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005122.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_005122.]: Entered 'hyperbolic_regression'-Function - [20250519_005122.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005122.]: Entered 'cubic_regression'-Function - [20250519_005122.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005122.]: # CpG-site: row_means + [20250519_005957.]: Logging df_agg: CpG#9 + [20250519_005957.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005957.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005957.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005957.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005957.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) + [20250519_005957.]: Entered 'hyperbolic_regression'-Function + [20250519_005957.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005958.]: Entered 'cubic_regression'-Function + [20250519_005958.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005958.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_005122.]: Logging df_agg: row_means - [20250519_005122.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005122.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005122.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_005122.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005122.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_005122.]: Entered 'hyperbolic_regression'-Function - [20250519_005122.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005122.]: Entered 'cubic_regression'-Function - [20250519_005122.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005123.]: + [20250519_005958.]: Logging df_agg: row_means + [20250519_005958.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005958.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005958.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_005958.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005958.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) + [20250519_005958.]: Entered 'hyperbolic_regression'-Function + [20250519_005958.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005958.]: Entered 'cubic_regression'-Function + [20250519_005958.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005959.]: ### Starting with regression calculations ### - [20250519_005123.]: Entered 'regression_type1'-Function - [20250519_005124.]: # CpG-site: CpG#1 + [20250519_005959.]: Entered 'regression_type1'-Function + [20250519_005959.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_005124.]: Logging df_agg: CpG#1 - [20250519_005124.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005124.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005124.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005124.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005124.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_005124.]: Entered 'hyperbolic_regression'-Function - [20250519_005124.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005124.]: Entered 'cubic_regression'-Function - [20250519_005124.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005124.]: # CpG-site: CpG#2 + [20250519_005959.]: Logging df_agg: CpG#1 + [20250519_005959.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005959.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005959.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005959.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005959.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) + [20250519_005959.]: Entered 'hyperbolic_regression'-Function + [20250519_005959.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005959.]: Entered 'cubic_regression'-Function + [20250519_005959.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005959.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_005124.]: Logging df_agg: CpG#2 - [20250519_005124.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005124.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005124.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005124.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005124.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_005124.]: Entered 'hyperbolic_regression'-Function - [20250519_005124.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005124.]: Entered 'cubic_regression'-Function - [20250519_005124.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005124.]: # CpG-site: CpG#3 + [20250519_005959.]: Logging df_agg: CpG#2 + [20250519_005959.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005959.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005959.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005959.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005959.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) + [20250519_005959.]: Entered 'hyperbolic_regression'-Function + [20250519_005959.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005959.]: Entered 'cubic_regression'-Function + [20250519_005959.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005959.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_005124.]: Logging df_agg: CpG#3 - [20250519_005124.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005124.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005124.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005124.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005124.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_005124.]: Entered 'hyperbolic_regression'-Function - [20250519_005124.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005124.]: Entered 'cubic_regression'-Function - [20250519_005124.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005124.]: # CpG-site: CpG#4 + [20250519_005959.]: Logging df_agg: CpG#3 + [20250519_005959.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005959.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005959.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005959.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005959.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) + [20250519_005959.]: Entered 'hyperbolic_regression'-Function + [20250519_005959.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005959.]: Entered 'cubic_regression'-Function + [20250519_005959.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005959.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_005124.]: Logging df_agg: CpG#4 - [20250519_005124.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005124.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005124.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005124.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005124.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_005124.]: Entered 'hyperbolic_regression'-Function - [20250519_005124.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005124.]: Entered 'cubic_regression'-Function - [20250519_005124.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005124.]: # CpG-site: CpG#5 + [20250519_005959.]: Logging df_agg: CpG#4 + [20250519_005959.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005959.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005959.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005959.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005959.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) + [20250519_005959.]: Entered 'hyperbolic_regression'-Function + [20250519_005959.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005959.]: Entered 'cubic_regression'-Function + [20250519_005959.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005959.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_005124.]: Logging df_agg: CpG#5 - [20250519_005124.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005124.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005124.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005124.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005124.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_005124.]: Entered 'hyperbolic_regression'-Function - [20250519_005124.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005124.]: Entered 'cubic_regression'-Function - [20250519_005124.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005124.]: # CpG-site: CpG#6 + [20250519_005959.]: Logging df_agg: CpG#5 + [20250519_005959.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005959.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005959.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005959.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005959.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) + [20250519_005959.]: Entered 'hyperbolic_regression'-Function + [20250519_005959.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005959.]: Entered 'cubic_regression'-Function + [20250519_005959.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005959.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_005124.]: Logging df_agg: CpG#6 - [20250519_005124.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005124.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005124.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005124.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005124.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_005124.]: Entered 'hyperbolic_regression'-Function - [20250519_005124.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005124.]: Entered 'cubic_regression'-Function - [20250519_005124.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005124.]: # CpG-site: CpG#7 + [20250519_005959.]: Logging df_agg: CpG#6 + [20250519_005959.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005959.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005959.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005959.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005959.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) + [20250519_005959.]: Entered 'hyperbolic_regression'-Function + [20250519_005959.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005959.]: Entered 'cubic_regression'-Function + [20250519_005959.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_005959.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_005124.]: Logging df_agg: CpG#7 - [20250519_005124.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005124.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005124.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005124.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005124.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_005124.]: Entered 'hyperbolic_regression'-Function - [20250519_005124.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005124.]: Entered 'cubic_regression'-Function - [20250519_005124.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005124.]: # CpG-site: CpG#8 + [20250519_010000.]: Logging df_agg: CpG#7 + [20250519_010000.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010000.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_010000.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_010000.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_010000.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) + [20250519_010000.]: Entered 'hyperbolic_regression'-Function + [20250519_010000.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010000.]: Entered 'cubic_regression'-Function + [20250519_010000.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010000.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_005124.]: Logging df_agg: CpG#8 - [20250519_005124.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005124.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005124.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005124.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005124.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_005124.]: Entered 'hyperbolic_regression'-Function - [20250519_005124.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005124.]: Entered 'cubic_regression'-Function - [20250519_005124.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005124.]: # CpG-site: CpG#9 + [20250519_010000.]: Logging df_agg: CpG#8 + [20250519_010000.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010000.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_010000.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_010000.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_010000.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) + [20250519_010000.]: Entered 'hyperbolic_regression'-Function + [20250519_010000.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010000.]: Entered 'cubic_regression'-Function + [20250519_010000.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010000.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_005124.]: Logging df_agg: CpG#9 - [20250519_005124.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005124.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005124.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005124.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005124.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_005124.]: Entered 'hyperbolic_regression'-Function - [20250519_005124.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005124.]: Entered 'cubic_regression'-Function - [20250519_005124.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005124.]: # CpG-site: row_means + [20250519_010000.]: Logging df_agg: CpG#9 + [20250519_010000.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010000.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_010000.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_010000.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_010000.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) + [20250519_010000.]: Entered 'hyperbolic_regression'-Function + [20250519_010000.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010000.]: Entered 'cubic_regression'-Function + [20250519_010000.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010000.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_005124.]: Logging df_agg: row_means - [20250519_005124.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005124.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005124.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_005124.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005124.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_005124.]: Entered 'hyperbolic_regression'-Function - [20250519_005124.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005124.]: Entered 'cubic_regression'-Function - [20250519_005124.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005125.]: Entered 'regression_type1'-Function + [20250519_010000.]: Logging df_agg: row_means + [20250519_010000.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010000.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_010000.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_010000.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_010000.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) + [20250519_010000.]: Entered 'hyperbolic_regression'-Function + [20250519_010000.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010000.]: Entered 'cubic_regression'-Function + [20250519_010000.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010001.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : @@ -7684,95 +7684,95 @@ singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient - [20250519_005125.]: # CpG-site: CpG#1 + [20250519_010001.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_005125.]: Logging df_agg: CpG#1 - [20250519_005125.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005125.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005125.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005125.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005125.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_005125.]: Entered 'hyperbolic_regression'-Function - [20250519_005125.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005126.]: Entered 'cubic_regression'-Function - [20250519_005126.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005126.]: # CpG-site: CpG#2 + [20250519_010001.]: Logging df_agg: CpG#1 + [20250519_010001.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010001.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_010001.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_010001.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_010001.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) + [20250519_010001.]: Entered 'hyperbolic_regression'-Function + [20250519_010001.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010001.]: Entered 'cubic_regression'-Function + [20250519_010001.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010001.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_005126.]: Logging df_agg: CpG#2 - [20250519_005126.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005126.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005126.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005126.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005126.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_005126.]: Entered 'hyperbolic_regression'-Function - [20250519_005126.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005126.]: Entered 'cubic_regression'-Function - [20250519_005126.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005126.]: # CpG-site: CpG#3 + [20250519_010001.]: Logging df_agg: CpG#2 + [20250519_010001.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010001.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_010001.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_010001.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_010001.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) + [20250519_010001.]: Entered 'hyperbolic_regression'-Function + [20250519_010001.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010002.]: Entered 'cubic_regression'-Function + [20250519_010002.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010002.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_005126.]: Logging df_agg: CpG#3 - [20250519_005126.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005126.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005126.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005126.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005126.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_005126.]: Entered 'hyperbolic_regression'-Function - [20250519_005126.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005127.]: Entered 'cubic_regression'-Function - [20250519_005127.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005127.]: # CpG-site: CpG#4 + [20250519_010002.]: Logging df_agg: CpG#3 + [20250519_010002.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010002.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_010002.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_010002.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_010002.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) + [20250519_010002.]: Entered 'hyperbolic_regression'-Function + [20250519_010002.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010002.]: Entered 'cubic_regression'-Function + [20250519_010002.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010002.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_005127.]: Logging df_agg: CpG#4 - [20250519_005127.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005127.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005127.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005127.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005127.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_005127.]: Entered 'hyperbolic_regression'-Function - [20250519_005127.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005127.]: Entered 'cubic_regression'-Function - [20250519_005127.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005127.]: # CpG-site: CpG#5 + [20250519_010002.]: Logging df_agg: CpG#4 + [20250519_010002.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010002.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_010002.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_010002.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_010002.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) + [20250519_010002.]: Entered 'hyperbolic_regression'-Function + [20250519_010002.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010003.]: Entered 'cubic_regression'-Function + [20250519_010003.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010003.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_005127.]: Logging df_agg: CpG#5 - [20250519_005127.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005127.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005127.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005127.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005127.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_005127.]: Entered 'hyperbolic_regression'-Function - [20250519_005127.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005127.]: Entered 'cubic_regression'-Function - [20250519_005127.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005127.]: # CpG-site: CpG#6 + [20250519_010003.]: Logging df_agg: CpG#5 + [20250519_010003.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010003.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_010003.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_010003.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_010003.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) + [20250519_010003.]: Entered 'hyperbolic_regression'-Function + [20250519_010003.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010003.]: Entered 'cubic_regression'-Function + [20250519_010003.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010003.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_005127.]: Logging df_agg: CpG#6 - [20250519_005127.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005127.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005127.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005127.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005127.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_005127.]: Entered 'hyperbolic_regression'-Function - [20250519_005127.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005128.]: Entered 'cubic_regression'-Function - [20250519_005128.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005128.]: # CpG-site: CpG#7 + [20250519_010003.]: Logging df_agg: CpG#6 + [20250519_010003.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010003.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_010003.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_010003.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_010003.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) + [20250519_010003.]: Entered 'hyperbolic_regression'-Function + [20250519_010003.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010003.]: Entered 'cubic_regression'-Function + [20250519_010003.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010003.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_005128.]: Logging df_agg: CpG#7 - [20250519_005128.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005128.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005128.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005128.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005128.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_005128.]: Entered 'hyperbolic_regression'-Function - [20250519_005128.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005128.]: Entered 'cubic_regression'-Function - [20250519_005128.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005128.]: # CpG-site: CpG#8 + [20250519_010003.]: Logging df_agg: CpG#7 + [20250519_010003.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010003.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_010003.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_010003.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_010003.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) + [20250519_010003.]: Entered 'hyperbolic_regression'-Function + [20250519_010003.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010004.]: Entered 'cubic_regression'-Function + [20250519_010004.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010004.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_005128.]: Logging df_agg: CpG#8 - [20250519_005128.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005128.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005128.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005128.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005128.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_005128.]: Entered 'hyperbolic_regression'-Function - [20250519_005128.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005128.]: Entered 'cubic_regression'-Function - [20250519_005128.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005128.]: # CpG-site: CpG#9 + [20250519_010004.]: Logging df_agg: CpG#8 + [20250519_010004.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010004.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_010004.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_010004.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_010004.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) + [20250519_010004.]: Entered 'hyperbolic_regression'-Function + [20250519_010004.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010004.]: Entered 'cubic_regression'-Function + [20250519_010004.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010004.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_005129.]: Logging df_agg: CpG#9 - [20250519_005129.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005129.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005129.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005129.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005129.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_005129.]: Entered 'hyperbolic_regression'-Function - [20250519_005129.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005129.]: Entered 'cubic_regression'-Function - [20250519_005129.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005129.]: # CpG-site: row_means + [20250519_010004.]: Logging df_agg: CpG#9 + [20250519_010004.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010004.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_010004.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_010004.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_010004.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) + [20250519_010004.]: Entered 'hyperbolic_regression'-Function + [20250519_010004.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010004.]: Entered 'cubic_regression'-Function + [20250519_010004.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010004.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_005129.]: Logging df_agg: row_means - [20250519_005129.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005129.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005129.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_005129.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005129.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_005129.]: Entered 'hyperbolic_regression'-Function - [20250519_005129.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005129.]: Entered 'cubic_regression'-Function - [20250519_005129.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005130.]: Entered 'clean_dt'-Function - [20250519_005130.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_005130.]: got experimental data - [20250519_005130.]: Entered 'clean_dt'-Function - [20250519_005130.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_005130.]: got calibration data - [20250519_005130.]: + [20250519_010004.]: Logging df_agg: row_means + [20250519_010004.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010004.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_010004.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_010004.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_010004.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) + [20250519_010004.]: Entered 'hyperbolic_regression'-Function + [20250519_010004.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010005.]: Entered 'cubic_regression'-Function + [20250519_010005.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010005.]: Entered 'clean_dt'-Function + [20250519_010005.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_010005.]: got experimental data + [20250519_010005.]: Entered 'clean_dt'-Function + [20250519_010005.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_010005.]: got calibration data + [20250519_010005.]: ### Starting with regression calculations ### - [20250519_005130.]: Entered 'regression_type1'-Function + [20250519_010005.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : @@ -8311,87 +8311,87 @@ singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient - [20250519_005130.]: # CpG-site: CpG#1 + [20250519_010005.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_005130.]: Logging df_agg: CpG#1 - [20250519_005130.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005130.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005130.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005130.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005130.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_005130.]: Entered 'hyperbolic_regression'-Function - [20250519_005130.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005130.]: Entered 'cubic_regression'-Function - [20250519_005130.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005130.]: # CpG-site: CpG#2 + [20250519_010005.]: Logging df_agg: CpG#1 + [20250519_010005.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010005.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_010005.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_010005.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_010005.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) + [20250519_010005.]: Entered 'hyperbolic_regression'-Function + [20250519_010005.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010006.]: Entered 'cubic_regression'-Function + [20250519_010006.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010006.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_005130.]: Logging df_agg: CpG#2 - [20250519_005130.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005130.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005130.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005130.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005130.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_005130.]: Entered 'hyperbolic_regression'-Function - [20250519_005130.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005131.]: Entered 'cubic_regression'-Function - [20250519_005131.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005131.]: # CpG-site: CpG#3 + [20250519_010006.]: Logging df_agg: CpG#2 + [20250519_010006.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010006.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_010006.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_010006.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_010006.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) + [20250519_010006.]: Entered 'hyperbolic_regression'-Function + [20250519_010006.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010006.]: Entered 'cubic_regression'-Function + [20250519_010006.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010006.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_005131.]: Logging df_agg: CpG#3 - [20250519_005131.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005131.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005131.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005131.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005131.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_005131.]: Entered 'hyperbolic_regression'-Function - [20250519_005131.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005131.]: Entered 'cubic_regression'-Function - [20250519_005131.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005131.]: # CpG-site: CpG#4 + [20250519_010006.]: Logging df_agg: CpG#3 + [20250519_010006.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010006.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_010006.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_010006.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_010006.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) + [20250519_010006.]: Entered 'hyperbolic_regression'-Function + [20250519_010006.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010006.]: Entered 'cubic_regression'-Function + [20250519_010006.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010007.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_005131.]: Logging df_agg: CpG#4 - [20250519_005131.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005131.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005131.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005131.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005131.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_005131.]: Entered 'hyperbolic_regression'-Function - [20250519_005131.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005131.]: Entered 'cubic_regression'-Function - [20250519_005131.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005131.]: # CpG-site: CpG#5 + [20250519_010007.]: Logging df_agg: CpG#4 + [20250519_010007.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010007.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_010007.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_010007.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_010007.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) + [20250519_010007.]: Entered 'hyperbolic_regression'-Function + [20250519_010007.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010007.]: Entered 'cubic_regression'-Function + [20250519_010007.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010007.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_005131.]: Logging df_agg: CpG#5 - [20250519_005131.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005131.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005131.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005131.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005131.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_005131.]: Entered 'hyperbolic_regression'-Function - [20250519_005131.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005132.]: Entered 'cubic_regression'-Function - [20250519_005132.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005132.]: # CpG-site: CpG#6 + [20250519_010007.]: Logging df_agg: CpG#5 + [20250519_010007.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010007.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_010007.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_010007.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_010007.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) + [20250519_010007.]: Entered 'hyperbolic_regression'-Function + [20250519_010007.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010007.]: Entered 'cubic_regression'-Function + [20250519_010007.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010007.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_005132.]: Logging df_agg: CpG#6 - [20250519_005132.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005132.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005132.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005132.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005132.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_005132.]: Entered 'hyperbolic_regression'-Function - [20250519_005132.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005132.]: Entered 'cubic_regression'-Function - [20250519_005132.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005132.]: # CpG-site: CpG#7 + [20250519_010007.]: Logging df_agg: CpG#6 + [20250519_010007.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010007.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_010007.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_010007.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_010007.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) + [20250519_010007.]: Entered 'hyperbolic_regression'-Function + [20250519_010007.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010008.]: Entered 'cubic_regression'-Function + [20250519_010008.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010008.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_005132.]: Logging df_agg: CpG#7 - [20250519_005132.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005132.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005132.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005132.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005132.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_005132.]: Entered 'hyperbolic_regression'-Function - [20250519_005132.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005132.]: Entered 'cubic_regression'-Function - [20250519_005132.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005133.]: # CpG-site: CpG#8 + [20250519_010008.]: Logging df_agg: CpG#7 + [20250519_010008.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010008.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_010008.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_010008.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_010008.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) + [20250519_010008.]: Entered 'hyperbolic_regression'-Function + [20250519_010008.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010008.]: Entered 'cubic_regression'-Function + [20250519_010008.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010008.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_005133.]: Logging df_agg: CpG#8 - [20250519_005133.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005133.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005133.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005133.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005133.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_005133.]: Entered 'hyperbolic_regression'-Function - [20250519_005133.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005133.]: Entered 'cubic_regression'-Function - [20250519_005133.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005133.]: # CpG-site: CpG#9 + [20250519_010008.]: Logging df_agg: CpG#8 + [20250519_010008.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010008.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_010008.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_010008.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_010008.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) + [20250519_010008.]: Entered 'hyperbolic_regression'-Function + [20250519_010008.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010008.]: Entered 'cubic_regression'-Function + [20250519_010008.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010008.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_005133.]: Logging df_agg: CpG#9 - [20250519_005133.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005133.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005133.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005133.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005133.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_005133.]: Entered 'hyperbolic_regression'-Function - [20250519_005133.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005133.]: Entered 'cubic_regression'-Function - [20250519_005133.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005133.]: # CpG-site: row_means + [20250519_010008.]: Logging df_agg: CpG#9 + [20250519_010008.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010008.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_010008.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_010008.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_010008.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) + [20250519_010008.]: Entered 'hyperbolic_regression'-Function + [20250519_010008.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010009.]: Entered 'cubic_regression'-Function + [20250519_010009.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010009.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_005133.]: Logging df_agg: row_means - [20250519_005133.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005133.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005133.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_005133.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005133.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_005133.]: Entered 'hyperbolic_regression'-Function - [20250519_005133.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005133.]: Entered 'cubic_regression'-Function - [20250519_005133.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005134.]: Entered 'regression_type1'-Function + [20250519_010009.]: Logging df_agg: row_means + [20250519_010009.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010009.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_010009.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_010009.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_010009.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) + [20250519_010009.]: Entered 'hyperbolic_regression'-Function + [20250519_010009.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010009.]: Entered 'cubic_regression'-Function + [20250519_010009.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010010.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : @@ -8930,464 +8930,464 @@ singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient - [20250519_005134.]: # CpG-site: CpG#1 + [20250519_010010.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_005134.]: Logging df_agg: CpG#1 - [20250519_005134.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005134.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005134.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005134.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005134.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_005134.]: Entered 'hyperbolic_regression'-Function - [20250519_005134.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005134.]: Entered 'cubic_regression'-Function - [20250519_005134.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005135.]: # CpG-site: CpG#2 + [20250519_010010.]: Logging df_agg: CpG#1 + [20250519_010010.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010010.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_010010.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_010010.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_010010.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) + [20250519_010010.]: Entered 'hyperbolic_regression'-Function + [20250519_010010.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010010.]: Entered 'cubic_regression'-Function + [20250519_010010.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010010.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_005135.]: Logging df_agg: CpG#2 - [20250519_005135.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005135.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005135.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005135.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005135.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_005135.]: Entered 'hyperbolic_regression'-Function - [20250519_005135.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005135.]: Entered 'cubic_regression'-Function - [20250519_005135.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005135.]: # CpG-site: CpG#3 + [20250519_010010.]: Logging df_agg: CpG#2 + [20250519_010010.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010010.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_010010.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_010010.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_010010.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) + [20250519_010010.]: Entered 'hyperbolic_regression'-Function + [20250519_010010.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010010.]: Entered 'cubic_regression'-Function + [20250519_010010.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010010.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_005135.]: Logging df_agg: CpG#3 - [20250519_005135.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005135.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005135.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005135.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005135.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_005135.]: Entered 'hyperbolic_regression'-Function - [20250519_005135.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005135.]: Entered 'cubic_regression'-Function - [20250519_005135.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005135.]: # CpG-site: CpG#4 + [20250519_010010.]: Logging df_agg: CpG#3 + [20250519_010010.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010010.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_010010.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_010010.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_010010.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) + [20250519_010010.]: Entered 'hyperbolic_regression'-Function + [20250519_010010.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010011.]: Entered 'cubic_regression'-Function + [20250519_010011.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010011.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_005135.]: Logging df_agg: CpG#4 - [20250519_005135.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005135.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005135.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005135.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005135.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_005135.]: Entered 'hyperbolic_regression'-Function - [20250519_005135.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005136.]: Entered 'cubic_regression'-Function - [20250519_005136.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005136.]: # CpG-site: CpG#5 + [20250519_010011.]: Logging df_agg: CpG#4 + [20250519_010011.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010011.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_010011.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_010011.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_010011.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) + [20250519_010011.]: Entered 'hyperbolic_regression'-Function + [20250519_010011.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010011.]: Entered 'cubic_regression'-Function + [20250519_010011.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010011.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_005136.]: Logging df_agg: CpG#5 - [20250519_005136.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005136.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005136.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005136.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005136.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_005136.]: Entered 'hyperbolic_regression'-Function - [20250519_005136.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005136.]: Entered 'cubic_regression'-Function - [20250519_005136.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005136.]: # CpG-site: CpG#6 + [20250519_010011.]: Logging df_agg: CpG#5 + [20250519_010011.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010011.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_010011.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_010011.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_010011.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) + [20250519_010011.]: Entered 'hyperbolic_regression'-Function + [20250519_010011.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010011.]: Entered 'cubic_regression'-Function + [20250519_010011.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010012.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_005136.]: Logging df_agg: CpG#6 - [20250519_005136.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005136.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005136.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005136.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005136.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_005136.]: Entered 'hyperbolic_regression'-Function - [20250519_005136.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005136.]: Entered 'cubic_regression'-Function - [20250519_005136.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005136.]: # CpG-site: CpG#7 + [20250519_010012.]: Logging df_agg: CpG#6 + [20250519_010012.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010012.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_010012.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_010012.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_010012.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) + [20250519_010012.]: Entered 'hyperbolic_regression'-Function + [20250519_010012.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010012.]: Entered 'cubic_regression'-Function + [20250519_010012.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010012.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_005136.]: Logging df_agg: CpG#7 - [20250519_005136.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005136.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005136.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005136.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005136.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_005136.]: Entered 'hyperbolic_regression'-Function - [20250519_005136.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005137.]: Entered 'cubic_regression'-Function - [20250519_005137.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005137.]: # CpG-site: CpG#8 + [20250519_010012.]: Logging df_agg: CpG#7 + [20250519_010012.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010012.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_010012.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_010012.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_010012.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) + [20250519_010012.]: Entered 'hyperbolic_regression'-Function + [20250519_010012.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010012.]: Entered 'cubic_regression'-Function + [20250519_010012.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010012.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_005137.]: Logging df_agg: CpG#8 - [20250519_005137.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005137.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005137.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005137.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005137.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_005137.]: Entered 'hyperbolic_regression'-Function - [20250519_005137.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005137.]: Entered 'cubic_regression'-Function - [20250519_005137.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005137.]: # CpG-site: CpG#9 + [20250519_010012.]: Logging df_agg: CpG#8 + [20250519_010012.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010012.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_010012.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_010012.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_010012.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) + [20250519_010012.]: Entered 'hyperbolic_regression'-Function + [20250519_010012.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010013.]: Entered 'cubic_regression'-Function + [20250519_010013.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010013.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_005137.]: Logging df_agg: CpG#9 - [20250519_005137.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005137.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005137.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005137.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005137.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_005137.]: Entered 'hyperbolic_regression'-Function - [20250519_005137.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005137.]: Entered 'cubic_regression'-Function - [20250519_005137.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005138.]: # CpG-site: row_means + [20250519_010013.]: Logging df_agg: CpG#9 + [20250519_010013.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010013.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_010013.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_010013.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_010013.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) + [20250519_010013.]: Entered 'hyperbolic_regression'-Function + [20250519_010013.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010013.]: Entered 'cubic_regression'-Function + [20250519_010013.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010013.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_005138.]: Logging df_agg: row_means - [20250519_005138.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005138.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005138.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_005138.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005138.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_005138.]: Entered 'hyperbolic_regression'-Function - [20250519_005138.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005138.]: Entered 'cubic_regression'-Function - [20250519_005138.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005138.]: Entered 'solving_equations'-Function - [20250519_005138.]: Solving hyperbolic regression for CpG#1 + [20250519_010013.]: Logging df_agg: row_means + [20250519_010013.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010013.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_010013.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_010013.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_010013.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) + [20250519_010013.]: Entered 'hyperbolic_regression'-Function + [20250519_010013.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010013.]: Entered 'cubic_regression'-Function + [20250519_010013.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010014.]: Entered 'solving_equations'-Function + [20250519_010014.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 0 - [20250519_005138.]: Samplename: 0 + [20250519_010014.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 14.1480167109277 - [20250519_005138.]: Samplename: 12.5 + [20250519_010014.]: Samplename: 12.5 Root: 14.148 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1398443790446 - [20250519_005138.]: Samplename: 25 + [20250519_010014.]: Samplename: 25 Root: 26.14 --> Root in between the borders! Added to results. Hyperbolic solved: 39.3762039523382 - [20250519_005139.]: Samplename: 37.5 + [20250519_010014.]: Samplename: 37.5 Root: 39.376 --> Root in between the borders! Added to results. Hyperbolic solved: 52.9476244028783 - [20250519_005139.]: Samplename: 50 + [20250519_010014.]: Samplename: 50 Root: 52.948 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4195105873419 - [20250519_005139.]: Samplename: 62.5 + [20250519_010014.]: Samplename: 62.5 Root: 65.42 --> Root in between the borders! Added to results. Hyperbolic solved: 74.433837698331 - [20250519_005139.]: Samplename: 75 + [20250519_010014.]: Samplename: 75 Root: 74.434 --> Root in between the borders! Added to results. Hyperbolic solved: 80.5559264925808 - [20250519_005139.]: Samplename: 87.5 + [20250519_010014.]: Samplename: 87.5 Root: 80.556 --> Root in between the borders! Added to results. Hyperbolic solved: 100 - [20250519_005139.]: Samplename: 100 + [20250519_010014.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. - [20250519_005139.]: Solving hyperbolic regression for CpG#2 + [20250519_010014.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 0 - [20250519_005139.]: Samplename: 0 + [20250519_010014.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 10.7812303822548 - [20250519_005139.]: Samplename: 12.5 + [20250519_010014.]: Samplename: 12.5 Root: 10.781 --> Root in between the borders! Added to results. Hyperbolic solved: 26.0648314401535 - [20250519_005139.]: Samplename: 25 + [20250519_010014.]: Samplename: 25 Root: 26.065 --> Root in between the borders! Added to results. Hyperbolic solved: 35.1980950192158 - [20250519_005139.]: Samplename: 37.5 + [20250519_010014.]: Samplename: 37.5 Root: 35.198 --> Root in between the borders! Added to results. Hyperbolic solved: 47.9203836090837 - [20250519_005139.]: Samplename: 50 + [20250519_010014.]: Samplename: 50 Root: 47.92 --> Root in between the borders! Added to results. Hyperbolic solved: 67.275750545092 - [20250519_005139.]: Samplename: 62.5 + [20250519_010014.]: Samplename: 62.5 Root: 67.276 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7278143684244 - [20250519_005139.]: Samplename: 75 + [20250519_010014.]: Samplename: 75 Root: 75.728 --> Root in between the borders! Added to results. Hyperbolic solved: 84.1258428690749 - [20250519_005139.]: Samplename: 87.5 + [20250519_010014.]: Samplename: 87.5 Root: 84.126 --> Root in between the borders! Added to results. Hyperbolic solved: 100 - [20250519_005139.]: Samplename: 100 + [20250519_010014.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. - [20250519_005139.]: Solving hyperbolic regression for CpG#3 + [20250519_010014.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 0 - [20250519_005139.]: Samplename: 0 + [20250519_010014.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 10.8467198180169 - [20250519_005139.]: Samplename: 12.5 + [20250519_010014.]: Samplename: 12.5 Root: 10.847 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1449700612555 - [20250519_005139.]: Samplename: 25 + [20250519_010014.]: Samplename: 25 Root: 26.145 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2865760013773 - [20250519_005139.]: Samplename: 37.5 + [20250519_010014.]: Samplename: 37.5 Root: 37.287 --> Root in between the borders! Added to results. Hyperbolic solved: 51.4114078772697 - [20250519_005139.]: Samplename: 50 + [20250519_010014.]: Samplename: 50 Root: 51.411 --> Root in between the borders! Added to results. Hyperbolic solved: 65.0139634978039 - [20250519_005139.]: Samplename: 62.5 + [20250519_010014.]: Samplename: 62.5 Root: 65.014 --> Root in between the borders! Added to results. Hyperbolic solved: 76.9921394830877 - [20250519_005139.]: Samplename: 75 + [20250519_010014.]: Samplename: 75 Root: 76.992 --> Root in between the borders! Added to results. Hyperbolic solved: 79.6808818589171 - [20250519_005139.]: Samplename: 87.5 + [20250519_010014.]: Samplename: 87.5 Root: 79.681 --> Root in between the borders! Added to results. Hyperbolic solved: 100 - [20250519_005139.]: Samplename: 100 + [20250519_010014.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. - [20250519_005139.]: Solving hyperbolic regression for CpG#4 + [20250519_010014.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 0 - [20250519_005139.]: Samplename: 0 + [20250519_010014.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 13.2494913489925 - [20250519_005139.]: Samplename: 12.5 + [20250519_010014.]: Samplename: 12.5 Root: 13.249 --> Root in between the borders! Added to results. Hyperbolic solved: 25.0914701740206 - [20250519_005139.]: Samplename: 25 + [20250519_010014.]: Samplename: 25 Root: 25.091 --> Root in between the borders! Added to results. Hyperbolic solved: 38.8081740444332 - [20250519_005139.]: Samplename: 37.5 + [20250519_010014.]: Samplename: 37.5 Root: 38.808 --> Root in between the borders! Added to results. Hyperbolic solved: 49.1133034150896 - [20250519_005139.]: Samplename: 50 + [20250519_010014.]: Samplename: 50 Root: 49.113 --> Root in between the borders! Added to results. Hyperbolic solved: 67.573566028728 - [20250519_005139.]: Samplename: 62.5 + [20250519_010014.]: Samplename: 62.5 Root: 67.574 --> Root in between the borders! Added to results. Hyperbolic solved: 73.7655861480302 - [20250519_005139.]: Samplename: 75 + [20250519_010014.]: Samplename: 75 Root: 73.766 --> Root in between the borders! Added to results. Hyperbolic solved: 82.0404940898918 - [20250519_005139.]: Samplename: 87.5 + [20250519_010014.]: Samplename: 87.5 Root: 82.04 --> Root in between the borders! Added to results. Hyperbolic solved: 100 - [20250519_005139.]: Samplename: 100 + [20250519_010014.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. - [20250519_005139.]: Solving hyperbolic regression for CpG#5 + [20250519_010014.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 0 - [20250519_005139.]: Samplename: 0 + [20250519_010014.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 8.36101271071658 - [20250519_005139.]: Samplename: 12.5 + [20250519_010014.]: Samplename: 12.5 Root: 8.361 --> Root in between the borders! Added to results. Hyperbolic solved: 23.0724671456609 - [20250519_005139.]: Samplename: 25 + [20250519_010014.]: Samplename: 25 Root: 23.072 --> Root in between the borders! Added to results. Hyperbolic solved: 36.9926902689013 - [20250519_005139.]: Samplename: 37.5 + [20250519_010014.]: Samplename: 37.5 Root: 36.993 --> Root in between the borders! Added to results. Hyperbolic solved: 50.9902012556783 - [20250519_005139.]: Samplename: 50 + [20250519_010014.]: Samplename: 50 Root: 50.99 --> Root in between the borders! Added to results. Hyperbolic solved: 62.7269420395599 - [20250519_005139.]: Samplename: 62.5 + [20250519_010014.]: Samplename: 62.5 Root: 62.727 --> Root in between the borders! Added to results. Hyperbolic solved: 76.6726263557269 - [20250519_005139.]: Samplename: 75 + [20250519_010014.]: Samplename: 75 Root: 76.673 --> Root in between the borders! Added to results. Hyperbolic solved: 86.2959101486824 - [20250519_005139.]: Samplename: 87.5 + [20250519_010014.]: Samplename: 87.5 Root: 86.296 --> Root in between the borders! Added to results. Hyperbolic solved: 100 - [20250519_005139.]: Samplename: 100 + [20250519_010014.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. - [20250519_005139.]: Solving hyperbolic regression for CpG#6 + [20250519_010014.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 0 - [20250519_005139.]: Samplename: 0 + [20250519_010014.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 11.8139244542506 - [20250519_005139.]: Samplename: 12.5 + [20250519_010014.]: Samplename: 12.5 Root: 11.814 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5330423964606 - [20250519_005139.]: Samplename: 25 + [20250519_010014.]: Samplename: 25 Root: 26.533 --> Root in between the borders! Added to results. Hyperbolic solved: 35.365455365759 - [20250519_005139.]: Samplename: 37.5 + [20250519_010014.]: Samplename: 37.5 Root: 35.365 --> Root in between the borders! Added to results. Hyperbolic solved: 50.1054345020203 - [20250519_005139.]: Samplename: 50 + [20250519_010014.]: Samplename: 50 Root: 50.105 --> Root in between the borders! Added to results. Hyperbolic solved: 64.9683746571925 - [20250519_005139.]: Samplename: 62.5 + [20250519_010014.]: Samplename: 62.5 Root: 64.968 --> Root in between the borders! Added to results. Hyperbolic solved: 73.6331726521876 - [20250519_005139.]: Samplename: 75 + [20250519_010014.]: Samplename: 75 Root: 73.633 --> Root in between the borders! Added to results. Hyperbolic solved: 86.9938602772804 - [20250519_005139.]: Samplename: 87.5 + [20250519_010014.]: Samplename: 87.5 Root: 86.994 --> Root in between the borders! Added to results. Hyperbolic solved: 100 - [20250519_005139.]: Samplename: 100 + [20250519_010014.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. - [20250519_005139.]: Solving hyperbolic regression for CpG#7 + [20250519_010014.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 0 - [20250519_005139.]: Samplename: 0 + [20250519_010014.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 11.7880491100164 - [20250519_005139.]: Samplename: 12.5 + [20250519_010014.]: Samplename: 12.5 Root: 11.788 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1959234034328 - [20250519_005139.]: Samplename: 25 + [20250519_010014.]: Samplename: 25 Root: 26.196 --> Root in between the borders! Added to results. Hyperbolic solved: 39.1978567589215 - [20250519_005139.]: Samplename: 37.5 + [20250519_010014.]: Samplename: 37.5 Root: 39.198 --> Root in between the borders! Added to results. Hyperbolic solved: 54.3513505291649 - [20250519_005139.]: Samplename: 50 + [20250519_010014.]: Samplename: 50 Root: 54.351 --> Root in between the borders! Added to results. Hyperbolic solved: 66.0567953704493 - [20250519_005139.]: Samplename: 62.5 + [20250519_010014.]: Samplename: 62.5 Root: 66.057 --> Root in between the borders! Added to results. Hyperbolic solved: 75.1900867269869 - [20250519_005139.]: Samplename: 75 + [20250519_010014.]: Samplename: 75 Root: 75.19 --> Root in between the borders! Added to results. Hyperbolic solved: 78.6051660349008 - [20250519_005139.]: Samplename: 87.5 + [20250519_010014.]: Samplename: 87.5 Root: 78.605 --> Root in between the borders! Added to results. Hyperbolic solved: 100 - [20250519_005139.]: Samplename: 100 + [20250519_010014.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. - [20250519_005139.]: Solving hyperbolic regression for CpG#8 + [20250519_010014.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 0 - [20250519_005139.]: Samplename: 0 + [20250519_010014.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 7.27382484148278 - [20250519_005139.]: Samplename: 12.5 + [20250519_010014.]: Samplename: 12.5 Root: 7.274 --> Root in between the borders! Added to results. Hyperbolic solved: 24.9765598282779 - [20250519_005139.]: Samplename: 25 + [20250519_010014.]: Samplename: 25 Root: 24.977 --> Root in between the borders! Added to results. Hyperbolic solved: 34.028313807905 - [20250519_005139.]: Samplename: 37.5 + [20250519_010014.]: Samplename: 37.5 Root: 34.028 --> Root in between the borders! Added to results. Hyperbolic solved: 52.2946423509726 - [20250519_005139.]: Samplename: 50 + [20250519_010014.]: Samplename: 50 Root: 52.295 --> Root in between the borders! Added to results. Hyperbolic solved: 65.0742432627639 - [20250519_005139.]: Samplename: 62.5 + [20250519_010014.]: Samplename: 62.5 Root: 65.074 --> Root in between the borders! Added to results. Hyperbolic solved: 78.3047550247862 - [20250519_005139.]: Samplename: 75 + [20250519_010014.]: Samplename: 75 Root: 78.305 --> Root in between the borders! Added to results. Hyperbolic solved: 81.0501991298613 - [20250519_005139.]: Samplename: 87.5 + [20250519_010014.]: Samplename: 87.5 Root: 81.05 --> Root in between the borders! Added to results. Hyperbolic solved: 100 - [20250519_005139.]: Samplename: 100 + [20250519_010014.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. - [20250519_005139.]: Solving hyperbolic regression for CpG#9 + [20250519_010014.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 0 - [20250519_005139.]: Samplename: 0 + [20250519_010014.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 12.2068234201026 - [20250519_005139.]: Samplename: 12.5 + [20250519_010014.]: Samplename: 12.5 Root: 12.207 --> Root in between the borders! Added to results. Hyperbolic solved: 28.0688737636578 - [20250519_005139.]: Samplename: 25 + [20250519_010014.]: Samplename: 25 Root: 28.069 --> Root in between the borders! Added to results. Hyperbolic solved: 37.6661823177513 - [20250519_005139.]: Samplename: 37.5 + [20250519_010014.]: Samplename: 37.5 Root: 37.666 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3684233460174 - [20250519_005139.]: Samplename: 50 + [20250519_010014.]: Samplename: 50 Root: 52.368 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8636916649907 - [20250519_005139.]: Samplename: 62.5 + [20250519_010014.]: Samplename: 62.5 Root: 64.864 --> Root in between the borders! Added to results. Hyperbolic solved: 74.2551330907062 - [20250519_005139.]: Samplename: 75 + [20250519_010014.]: Samplename: 75 Root: 74.255 --> Root in between the borders! Added to results. Hyperbolic solved: 83.9343288872042 - [20250519_005139.]: Samplename: 87.5 + [20250519_010014.]: Samplename: 87.5 Root: 83.934 --> Root in between the borders! Added to results. Hyperbolic solved: 100 - [20250519_005139.]: Samplename: 100 + [20250519_010014.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. - [20250519_005139.]: Solving hyperbolic regression for row_means + [20250519_010014.]: Solving hyperbolic regression for row_means Hyperbolic solved: 0.000228160853766634 - [20250519_005139.]: Samplename: 0 + [20250519_010014.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 11.1587839347378 - [20250519_005139.]: Samplename: 12.5 + [20250519_010014.]: Samplename: 12.5 Root: 11.159 --> Root in between the borders! Added to results. Hyperbolic solved: 25.8570776700364 - [20250519_005139.]: Samplename: 25 + [20250519_010014.]: Samplename: 25 Root: 25.857 --> Root in between the borders! Added to results. Hyperbolic solved: 37.065004454281 - [20250519_005139.]: Samplename: 37.5 + [20250519_010014.]: Samplename: 37.5 Root: 37.065 --> Root in between the borders! Added to results. Hyperbolic solved: 51.1881763075298 - [20250519_005139.]: Samplename: 50 + [20250519_010014.]: Samplename: 50 Root: 51.188 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4439754555105 - [20250519_005139.]: Samplename: 62.5 + [20250519_010014.]: Samplename: 62.5 Root: 65.444 --> Root in between the borders! Added to results. Hyperbolic solved: 75.3005883283082 - [20250519_005139.]: Samplename: 75 + [20250519_010014.]: Samplename: 75 Root: 75.301 --> Root in between the borders! Added to results. Hyperbolic solved: 82.6591025877361 - [20250519_005139.]: Samplename: 87.5 + [20250519_010014.]: Samplename: 87.5 Root: 82.659 --> Root in between the borders! Added to results. Hyperbolic solved: 100.000153506599 - [20250519_005139.]: Samplename: 100 + [20250519_010014.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 - [20250519_005139.]: + [20250519_010014.]: ### Starting with regression calculations ### - [20250519_005139.]: Entered 'regression_type1'-Function + [20250519_010014.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : @@ -9928,476 +9928,476 @@ singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient - [20250519_005139.]: # CpG-site: CpG#1 + [20250519_010014.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 14.1480167109277, 26.1398443790446, 39.3762039523382, 52.9476244028783, 65.4195105873419, 74.433837698331, 80.5559264925808, 100)c(0, 1.6480167109277, 1.1398443790446, 1.8762039523382, 2.9476244028783, 2.9195105873419, 0.566162301668996, 6.9440735074192, 0)c(NA, 13.1841336874216, 4.5593775161784, 5.00321053956854, 5.89524880575659, 4.67121693974705, 0.754883068891995, 7.93608400847909, 0) - [20250519_005139.]: Logging df_agg: CpG#1 - [20250519_005139.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005139.]: c(0, 14.1480167109277, 26.1398443790446, 39.3762039523382, 52.9476244028783, 65.4195105873419, 74.433837698331, 80.5559264925808, 100)[20250519_005139.]: c(0, 1.6480167109277, 1.1398443790446, 1.8762039523382, 2.9476244028783, 2.9195105873419, 0.566162301668996, 6.9440735074192, 0)[20250519_005139.]: c(NA, 13.1841336874216, 4.5593775161784, 5.00321053956854, 5.89524880575659, 4.67121693974705, 0.754883068891995, 7.93608400847909, 0) - [20250519_005139.]: Entered 'hyperbolic_regression'-Function - [20250519_005139.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005139.]: Entered 'cubic_regression'-Function - [20250519_005139.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005139.]: # CpG-site: CpG#2 + [20250519_010014.]: Logging df_agg: CpG#1 + [20250519_010014.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010014.]: c(0, 14.1480167109277, 26.1398443790446, 39.3762039523382, 52.9476244028783, 65.4195105873419, 74.433837698331, 80.5559264925808, 100)[20250519_010014.]: c(0, 1.6480167109277, 1.1398443790446, 1.8762039523382, 2.9476244028783, 2.9195105873419, 0.566162301668996, 6.9440735074192, 0)[20250519_010014.]: c(NA, 13.1841336874216, 4.5593775161784, 5.00321053956854, 5.89524880575659, 4.67121693974705, 0.754883068891995, 7.93608400847909, 0) + [20250519_010014.]: Entered 'hyperbolic_regression'-Function + [20250519_010014.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010015.]: Entered 'cubic_regression'-Function + [20250519_010015.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010015.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.7812303822548, 26.0648314401535, 35.1980950192158, 47.9203836090837, 67.275750545092, 75.7278143684244, 84.1258428690749, 100)c(0, 1.7187696177452, 1.0648314401535, 2.3019049807842, 2.0796163909163, 4.775750545092, 0.727814368424404, 3.3741571309251, 0)c(NA, 13.7501569419616, 4.259325760614, 6.1384132820912, 4.1592327818326, 7.64120087214719, 0.970419157899206, 3.85617957820011, 0) - [20250519_005139.]: Logging df_agg: CpG#2 - [20250519_005139.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005139.]: c(0, 10.7812303822548, 26.0648314401535, 35.1980950192158, 47.9203836090837, 67.275750545092, 75.7278143684244, 84.1258428690749, 100)[20250519_005139.]: c(0, 1.7187696177452, 1.0648314401535, 2.3019049807842, 2.0796163909163, 4.775750545092, 0.727814368424404, 3.3741571309251, 0)[20250519_005139.]: c(NA, 13.7501569419616, 4.259325760614, 6.1384132820912, 4.1592327818326, 7.64120087214719, 0.970419157899206, 3.85617957820011, 0) - [20250519_005139.]: Entered 'hyperbolic_regression'-Function - [20250519_005139.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005140.]: Entered 'cubic_regression'-Function - [20250519_005140.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005140.]: # CpG-site: CpG#3 + [20250519_010015.]: Logging df_agg: CpG#2 + [20250519_010015.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010015.]: c(0, 10.7812303822548, 26.0648314401535, 35.1980950192158, 47.9203836090837, 67.275750545092, 75.7278143684244, 84.1258428690749, 100)[20250519_010015.]: c(0, 1.7187696177452, 1.0648314401535, 2.3019049807842, 2.0796163909163, 4.775750545092, 0.727814368424404, 3.3741571309251, 0)[20250519_010015.]: c(NA, 13.7501569419616, 4.259325760614, 6.1384132820912, 4.1592327818326, 7.64120087214719, 0.970419157899206, 3.85617957820011, 0) + [20250519_010015.]: Entered 'hyperbolic_regression'-Function + [20250519_010015.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010015.]: Entered 'cubic_regression'-Function + [20250519_010015.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010015.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.8467198180169, 26.1449700612555, 37.2865760013773, 51.4114078772697, 65.0139634978039, 76.9921394830877, 79.6808818589171, 100)c(0, 1.6532801819831, 1.1449700612555, 0.213423998622702, 1.4114078772697, 2.5139634978039, 1.9921394830877, 7.8191181410829, 0)c(NA, 13.2262414558648, 4.579880245022, 0.569130662993871, 2.82281575453941, 4.02234159648624, 2.65618597745026, 8.93613501838045, 0) - [20250519_005140.]: Logging df_agg: CpG#3 - [20250519_005140.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005140.]: c(0, 10.8467198180169, 26.1449700612555, 37.2865760013773, 51.4114078772697, 65.0139634978039, 76.9921394830877, 79.6808818589171, 100)[20250519_005140.]: c(0, 1.6532801819831, 1.1449700612555, 0.213423998622702, 1.4114078772697, 2.5139634978039, 1.9921394830877, 7.8191181410829, 0)[20250519_005140.]: c(NA, 13.2262414558648, 4.579880245022, 0.569130662993871, 2.82281575453941, 4.02234159648624, 2.65618597745026, 8.93613501838045, 0) - [20250519_005140.]: Entered 'hyperbolic_regression'-Function - [20250519_005140.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005140.]: Entered 'cubic_regression'-Function - [20250519_005140.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005140.]: # CpG-site: CpG#4 + [20250519_010015.]: Logging df_agg: CpG#3 + [20250519_010015.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010015.]: c(0, 10.8467198180169, 26.1449700612555, 37.2865760013773, 51.4114078772697, 65.0139634978039, 76.9921394830877, 79.6808818589171, 100)[20250519_010015.]: c(0, 1.6532801819831, 1.1449700612555, 0.213423998622702, 1.4114078772697, 2.5139634978039, 1.9921394830877, 7.8191181410829, 0)[20250519_010015.]: c(NA, 13.2262414558648, 4.579880245022, 0.569130662993871, 2.82281575453941, 4.02234159648624, 2.65618597745026, 8.93613501838045, 0) + [20250519_010015.]: Entered 'hyperbolic_regression'-Function + [20250519_010015.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010016.]: Entered 'cubic_regression'-Function + [20250519_010016.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010016.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 13.2494913489925, 25.0914701740206, 38.8081740444332, 49.1133034150896, 67.573566028728, 73.7655861480302, 82.0404940898918, 100)c(0, 0.7494913489925, 0.0914701740205999, 1.3081740444332, 0.886696584910403, 5.073566028728, 1.2344138519698, 5.45950591010821, 0)c(NA, 5.99593079194, 0.365880696082399, 3.48846411848854, 1.77339316982081, 8.11770564596479, 1.64588513595974, 6.23943532583795, 0) - [20250519_005140.]: Logging df_agg: CpG#4 - [20250519_005140.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005140.]: c(0, 13.2494913489925, 25.0914701740206, 38.8081740444332, 49.1133034150896, 67.573566028728, 73.7655861480302, 82.0404940898918, 100)[20250519_005140.]: c(0, 0.7494913489925, 0.0914701740205999, 1.3081740444332, 0.886696584910403, 5.073566028728, 1.2344138519698, 5.45950591010821, 0)[20250519_005140.]: c(NA, 5.99593079194, 0.365880696082399, 3.48846411848854, 1.77339316982081, 8.11770564596479, 1.64588513595974, 6.23943532583795, 0) - [20250519_005140.]: Entered 'hyperbolic_regression'-Function - [20250519_005140.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005140.]: Entered 'cubic_regression'-Function - [20250519_005140.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005140.]: # CpG-site: CpG#5 + [20250519_010016.]: Logging df_agg: CpG#4 + [20250519_010016.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010016.]: c(0, 13.2494913489925, 25.0914701740206, 38.8081740444332, 49.1133034150896, 67.573566028728, 73.7655861480302, 82.0404940898918, 100)[20250519_010016.]: c(0, 0.7494913489925, 0.0914701740205999, 1.3081740444332, 0.886696584910403, 5.073566028728, 1.2344138519698, 5.45950591010821, 0)[20250519_010016.]: c(NA, 5.99593079194, 0.365880696082399, 3.48846411848854, 1.77339316982081, 8.11770564596479, 1.64588513595974, 6.23943532583795, 0) + [20250519_010016.]: Entered 'hyperbolic_regression'-Function + [20250519_010016.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010016.]: Entered 'cubic_regression'-Function + [20250519_010016.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010016.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.36101271071658, 23.0724671456609, 36.9926902689013, 50.9902012556783, 62.7269420395599, 76.6726263557269, 86.2959101486824, 100)c(0, 4.13898728928342, 1.9275328543391, 0.507309731098701, 0.990201255678301, 0.226942039559901, 1.6726263557269, 1.2040898513176, 0)c(NA, 33.1118983142674, 7.71013141735639, 1.35282594959654, 1.9804025113566, 0.363107263295842, 2.23016847430254, 1.37610268722012, 0) - [20250519_005141.]: Logging df_agg: CpG#5 - [20250519_005141.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005141.]: c(0, 8.36101271071658, 23.0724671456609, 36.9926902689013, 50.9902012556783, 62.7269420395599, 76.6726263557269, 86.2959101486824, 100)[20250519_005141.]: c(0, 4.13898728928342, 1.9275328543391, 0.507309731098701, 0.990201255678301, 0.226942039559901, 1.6726263557269, 1.2040898513176, 0)[20250519_005141.]: c(NA, 33.1118983142674, 7.71013141735639, 1.35282594959654, 1.9804025113566, 0.363107263295842, 2.23016847430254, 1.37610268722012, 0) - [20250519_005141.]: Entered 'hyperbolic_regression'-Function - [20250519_005141.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005141.]: Entered 'cubic_regression'-Function - [20250519_005141.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005141.]: # CpG-site: CpG#6 + [20250519_010016.]: Logging df_agg: CpG#5 + [20250519_010016.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010016.]: c(0, 8.36101271071658, 23.0724671456609, 36.9926902689013, 50.9902012556783, 62.7269420395599, 76.6726263557269, 86.2959101486824, 100)[20250519_010016.]: c(0, 4.13898728928342, 1.9275328543391, 0.507309731098701, 0.990201255678301, 0.226942039559901, 1.6726263557269, 1.2040898513176, 0)[20250519_010016.]: c(NA, 33.1118983142674, 7.71013141735639, 1.35282594959654, 1.9804025113566, 0.363107263295842, 2.23016847430254, 1.37610268722012, 0) + [20250519_010016.]: Entered 'hyperbolic_regression'-Function + [20250519_010016.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010016.]: Entered 'cubic_regression'-Function + [20250519_010016.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010016.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.8139244542506, 26.5330423964606, 35.365455365759, 50.1054345020203, 64.9683746571925, 73.6331726521876, 86.9938602772804, 100)c(0, 0.6860755457494, 1.5330423964606, 2.134544634241, 0.105434502020302, 2.4683746571925, 1.3668273478124, 0.506139722719595, 0)c(NA, 5.4886043659952, 6.13216958584241, 5.69211902464267, 0.210869004040603, 3.949399451508, 1.82243646374987, 0.578445397393823, 0) - [20250519_005141.]: Logging df_agg: CpG#6 - [20250519_005141.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005141.]: c(0, 11.8139244542506, 26.5330423964606, 35.365455365759, 50.1054345020203, 64.9683746571925, 73.6331726521876, 86.9938602772804, 100)[20250519_005141.]: c(0, 0.6860755457494, 1.5330423964606, 2.134544634241, 0.105434502020302, 2.4683746571925, 1.3668273478124, 0.506139722719595, 0)[20250519_005141.]: c(NA, 5.4886043659952, 6.13216958584241, 5.69211902464267, 0.210869004040603, 3.949399451508, 1.82243646374987, 0.578445397393823, 0) - [20250519_005141.]: Entered 'hyperbolic_regression'-Function - [20250519_005141.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005141.]: Entered 'cubic_regression'-Function - [20250519_005141.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005141.]: # CpG-site: CpG#7 + [20250519_010016.]: Logging df_agg: CpG#6 + [20250519_010016.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010016.]: c(0, 11.8139244542506, 26.5330423964606, 35.365455365759, 50.1054345020203, 64.9683746571925, 73.6331726521876, 86.9938602772804, 100)[20250519_010016.]: c(0, 0.6860755457494, 1.5330423964606, 2.134544634241, 0.105434502020302, 2.4683746571925, 1.3668273478124, 0.506139722719595, 0)[20250519_010016.]: c(NA, 5.4886043659952, 6.13216958584241, 5.69211902464267, 0.210869004040603, 3.949399451508, 1.82243646374987, 0.578445397393823, 0) + [20250519_010016.]: Entered 'hyperbolic_regression'-Function + [20250519_010016.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010017.]: Entered 'cubic_regression'-Function + [20250519_010017.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010017.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 11.7880491100164, 26.1959234034328, 39.1978567589215, 54.3513505291649, 66.0567953704493, 75.1900867269869, 78.6051660349008, 100)c(0, 0.7119508899836, 1.1959234034328, 1.6978567589215, 4.3513505291649, 3.55679537044929, 0.190086726986905, 8.8948339650992, 0)c(NA, 5.6956071198688, 4.7836936137312, 4.52761802379068, 8.70270105832979, 5.69087259271887, 0.253448969315874, 10.1655245315419, 0) - [20250519_005141.]: Logging df_agg: CpG#7 - [20250519_005141.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005141.]: c(0, 11.7880491100164, 26.1959234034328, 39.1978567589215, 54.3513505291649, 66.0567953704493, 75.1900867269869, 78.6051660349008, 100)[20250519_005141.]: c(0, 0.7119508899836, 1.1959234034328, 1.6978567589215, 4.3513505291649, 3.55679537044929, 0.190086726986905, 8.8948339650992, 0)[20250519_005141.]: c(NA, 5.6956071198688, 4.7836936137312, 4.52761802379068, 8.70270105832979, 5.69087259271887, 0.253448969315874, 10.1655245315419, 0) - [20250519_005141.]: Entered 'hyperbolic_regression'-Function - [20250519_005141.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005142.]: Entered 'cubic_regression'-Function - [20250519_005142.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005142.]: # CpG-site: CpG#8 + [20250519_010017.]: Logging df_agg: CpG#7 + [20250519_010017.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010017.]: c(0, 11.7880491100164, 26.1959234034328, 39.1978567589215, 54.3513505291649, 66.0567953704493, 75.1900867269869, 78.6051660349008, 100)[20250519_010017.]: c(0, 0.7119508899836, 1.1959234034328, 1.6978567589215, 4.3513505291649, 3.55679537044929, 0.190086726986905, 8.8948339650992, 0)[20250519_010017.]: c(NA, 5.6956071198688, 4.7836936137312, 4.52761802379068, 8.70270105832979, 5.69087259271887, 0.253448969315874, 10.1655245315419, 0) + [20250519_010017.]: Entered 'hyperbolic_regression'-Function + [20250519_010017.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010017.]: Entered 'cubic_regression'-Function + [20250519_010017.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010017.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 7.27382484148278, 24.9765598282779, 34.028313807905, 52.2946423509726, 65.0742432627639, 78.3047550247862, 81.0501991298613, 100)c(0, 5.22617515851722, 0.0234401717221004, 3.471686192095, 2.2946423509726, 2.5742432627639, 3.3047550247862, 6.4498008701387, 0)c(NA, 41.8094012681378, 0.0937606868884018, 9.25782984558666, 4.5892847019452, 4.11878922042224, 4.40634003304827, 7.37120099444423, 0) - [20250519_005142.]: Logging df_agg: CpG#8 - [20250519_005142.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005142.]: c(0, 7.27382484148278, 24.9765598282779, 34.028313807905, 52.2946423509726, 65.0742432627639, 78.3047550247862, 81.0501991298613, 100)[20250519_005142.]: c(0, 5.22617515851722, 0.0234401717221004, 3.471686192095, 2.2946423509726, 2.5742432627639, 3.3047550247862, 6.4498008701387, 0)[20250519_005142.]: c(NA, 41.8094012681378, 0.0937606868884018, 9.25782984558666, 4.5892847019452, 4.11878922042224, 4.40634003304827, 7.37120099444423, 0) - [20250519_005142.]: Entered 'hyperbolic_regression'-Function - [20250519_005142.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005142.]: Entered 'cubic_regression'-Function - [20250519_005142.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005142.]: # CpG-site: CpG#9 + [20250519_010017.]: Logging df_agg: CpG#8 + [20250519_010017.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010017.]: c(0, 7.27382484148278, 24.9765598282779, 34.028313807905, 52.2946423509726, 65.0742432627639, 78.3047550247862, 81.0501991298613, 100)[20250519_010017.]: c(0, 5.22617515851722, 0.0234401717221004, 3.471686192095, 2.2946423509726, 2.5742432627639, 3.3047550247862, 6.4498008701387, 0)[20250519_010017.]: c(NA, 41.8094012681378, 0.0937606868884018, 9.25782984558666, 4.5892847019452, 4.11878922042224, 4.40634003304827, 7.37120099444423, 0) + [20250519_010017.]: Entered 'hyperbolic_regression'-Function + [20250519_010017.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010017.]: Entered 'cubic_regression'-Function + [20250519_010018.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010018.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.2068234201026, 28.0688737636578, 37.6661823177513, 52.3684233460174, 64.8636916649907, 74.2551330907062, 83.9343288872042, 100)c(0, 0.2931765798974, 3.0688737636578, 0.1661823177513, 2.3684233460174, 2.3636916649907, 0.744866909293805, 3.5656711127958, 0)c(NA, 2.3454126391792, 12.2754950546312, 0.4431528473368, 4.7368466920348, 3.78190666398511, 0.993155879058406, 4.07505270033805, 0) - [20250519_005142.]: Logging df_agg: CpG#9 - [20250519_005142.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005142.]: c(0, 12.2068234201026, 28.0688737636578, 37.6661823177513, 52.3684233460174, 64.8636916649907, 74.2551330907062, 83.9343288872042, 100)[20250519_005142.]: c(0, 0.2931765798974, 3.0688737636578, 0.1661823177513, 2.3684233460174, 2.3636916649907, 0.744866909293805, 3.5656711127958, 0)[20250519_005142.]: c(NA, 2.3454126391792, 12.2754950546312, 0.4431528473368, 4.7368466920348, 3.78190666398511, 0.993155879058406, 4.07505270033805, 0) - [20250519_005142.]: Entered 'hyperbolic_regression'-Function - [20250519_005142.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005142.]: Entered 'cubic_regression'-Function - [20250519_005142.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005142.]: # CpG-site: row_means + [20250519_010018.]: Logging df_agg: CpG#9 + [20250519_010018.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010018.]: c(0, 12.2068234201026, 28.0688737636578, 37.6661823177513, 52.3684233460174, 64.8636916649907, 74.2551330907062, 83.9343288872042, 100)[20250519_010018.]: c(0, 0.2931765798974, 3.0688737636578, 0.1661823177513, 2.3684233460174, 2.3636916649907, 0.744866909293805, 3.5656711127958, 0)[20250519_010018.]: c(NA, 2.3454126391792, 12.2754950546312, 0.4431528473368, 4.7368466920348, 3.78190666398511, 0.993155879058406, 4.07505270033805, 0) + [20250519_010018.]: Entered 'hyperbolic_regression'-Function + [20250519_010018.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010018.]: Entered 'cubic_regression'-Function + [20250519_010018.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010018.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.000228160853766634, 11.1587839347378, 25.8570776700364, 37.065004454281, 51.1881763075298, 65.4439754555105, 75.3005883283082, 82.6591025877361, 100)c(0.000228160853766634, 1.3412160652622, 0.8570776700364, 0.434995545718998, 1.1881763075298, 2.9439754555105, 0.300588328308194, 4.8408974122639, 0)c(NA, 10.7297285220976, 3.4283106801456, 1.15998812191733, 2.3763526150596, 4.7103607288168, 0.400784437744259, 5.53245418544446, 0) - [20250519_005142.]: Logging df_agg: row_means - [20250519_005142.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005142.]: c(0.000228160853766634, 11.1587839347378, 25.8570776700364, 37.065004454281, 51.1881763075298, 65.4439754555105, 75.3005883283082, 82.6591025877361, 100)[20250519_005142.]: c(0.000228160853766634, 1.3412160652622, 0.8570776700364, 0.434995545718998, 1.1881763075298, 2.9439754555105, 0.300588328308194, 4.8408974122639, 0)[20250519_005142.]: c(NA, 10.7297285220976, 3.4283106801456, 1.15998812191733, 2.3763526150596, 4.7103607288168, 0.400784437744259, 5.53245418544446, 0) - [20250519_005142.]: Entered 'hyperbolic_regression'-Function - [20250519_005142.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005143.]: Entered 'cubic_regression'-Function - [20250519_005143.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005143.]: Entered 'solving_equations'-Function - [20250519_005143.]: Solving cubic regression for CpG#1 + [20250519_010018.]: Logging df_agg: row_means + [20250519_010018.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010018.]: c(0.000228160853766634, 11.1587839347378, 25.8570776700364, 37.065004454281, 51.1881763075298, 65.4439754555105, 75.3005883283082, 82.6591025877361, 100)[20250519_010018.]: c(0.000228160853766634, 1.3412160652622, 0.8570776700364, 0.434995545718998, 1.1881763075298, 2.9439754555105, 0.300588328308194, 4.8408974122639, 0)[20250519_010018.]: c(NA, 10.7297285220976, 3.4283106801456, 1.15998812191733, 2.3763526150596, 4.7103607288168, 0.400784437744259, 5.53245418544446, 0) + [20250519_010018.]: Entered 'hyperbolic_regression'-Function + [20250519_010018.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010018.]: Entered 'cubic_regression'-Function + [20250519_010018.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010019.]: Entered 'solving_equations'-Function + [20250519_010019.]: Solving cubic regression for CpG#1 Coefficients: 0Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 - [20250519_005143.]: Samplename: 0 + [20250519_010019.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -7.30533333333333Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 - [20250519_005143.]: Samplename: 12.5 + [20250519_010019.]: Samplename: 12.5 Root: 4.661 --> Root in between the borders! Added to results. Coefficients: -14.352Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 - [20250519_005143.]: Samplename: 25 + [20250519_010019.]: Samplename: 25 Root: 9.352 --> Root in between the borders! Added to results. Coefficients: -23.244Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 - [20250519_005143.]: Samplename: 37.5 + [20250519_010019.]: Samplename: 37.5 Root: 15.59 --> Root in between the borders! Added to results. Coefficients: -33.8645Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 - [20250519_005143.]: Samplename: 50 + [20250519_010019.]: Samplename: 50 Root: 23.601 --> Root in between the borders! Added to results. Coefficients: -45.318Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 - [20250519_005143.]: Samplename: 62.5 + [20250519_010019.]: Samplename: 62.5 Root: 33.121 --> Root in between the borders! Added to results. Coefficients: -54.857Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 - [20250519_005143.]: Samplename: 75 + [20250519_010019.]: Samplename: 75 Root: 42.001 --> Root in between the borders! Added to results. Coefficients: -62.062Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 - [20250519_005143.]: Samplename: 87.5 + [20250519_010019.]: Samplename: 87.5 Root: 49.51 --> Root in between the borders! Added to results. Coefficients: -90.01Coefficients: 1.6001Coefficients: -0.007Coefficients: 0 - [20250519_005143.]: Samplename: 100 + [20250519_010019.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 - [20250519_005143.]: Solving cubic regression for CpG#2 + [20250519_010019.]: Solving cubic regression for CpG#2 Coefficients: 0Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 - [20250519_005143.]: Samplename: 0 + [20250519_010019.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -6.05666666666666Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 - [20250519_005143.]: Samplename: 12.5 + [20250519_010019.]: Samplename: 12.5 Root: 8.415 --> Root in between the borders! Added to results. Coefficients: -15.656Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 - [20250519_005143.]: Samplename: 25 + [20250519_010019.]: Samplename: 25 Root: 21.016 --> Root in between the borders! Added to results. Coefficients: -22.054Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 - [20250519_005143.]: Samplename: 37.5 + [20250519_010019.]: Samplename: 37.5 Root: 28.984 --> Root in between the borders! Added to results. Coefficients: -31.945Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 - [20250519_005143.]: Samplename: 50 + [20250519_010019.]: Samplename: 50 Root: 40.726 --> Root in between the borders! Added to results. Coefficients: -49.68Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 - [20250519_005143.]: Samplename: 62.5 + [20250519_010019.]: Samplename: 62.5 Root: 60.322 --> Root in between the borders! Added to results. Coefficients: -58.6825Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 - [20250519_005143.]: Samplename: 75 + [20250519_010019.]: Samplename: 75 Root: 69.671 --> Root in between the borders! Added to results. Coefficients: -68.5533333333333Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 - [20250519_005143.]: Samplename: 87.5 + [20250519_010019.]: Samplename: 87.5 Root: 79.529 --> Root in between the borders! Added to results. Coefficients: -90.294Coefficients: 0.70294Coefficients: 0.002Coefficients: 0 - [20250519_005143.]: Samplename: 100 + [20250519_010019.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 - [20250519_005143.]: Solving cubic regression for CpG#3 + [20250519_010019.]: Solving cubic regression for CpG#3 Coefficients: 0Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 - [20250519_005143.]: Samplename: 0 + [20250519_010019.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -5.67Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 - [20250519_005143.]: Samplename: 12.5 + [20250519_010019.]: Samplename: 12.5 Root: 5.75 --> Root in between the borders! Added to results. Coefficients: -14.526Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 - [20250519_005143.]: Samplename: 25 + [20250519_010019.]: Samplename: 25 Root: 15.012 --> Root in between the borders! Added to results. Coefficients: -21.71Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 - [20250519_005143.]: Samplename: 37.5 + [20250519_010019.]: Samplename: 37.5 Root: 22.804 --> Root in between the borders! Added to results. Coefficients: -31.8725Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 - [20250519_005143.]: Samplename: 50 + [20250519_010019.]: Samplename: 50 Root: 34.308 --> Root in between the borders! Added to results. Coefficients: -42.986Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 - [20250519_005143.]: Samplename: 62.5 + [20250519_010019.]: Samplename: 62.5 Root: 47.638 --> Root in between the borders! Added to results. Coefficients: -54.0725Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 - [20250519_005143.]: Samplename: 75 + [20250519_010019.]: Samplename: 75 Root: 61.877 --> Root in between the borders! Added to results. Coefficients: -56.7533333333333Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 - [20250519_005143.]: Samplename: 87.5 + [20250519_010019.]: Samplename: 87.5 Root: 65.486 --> Root in between the borders! Added to results. Coefficients: -79.762Coefficients: 0.99762Coefficients: -0.002Coefficients: 0 - [20250519_005143.]: Samplename: 100 + [20250519_010019.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 - [20250519_005143.]: Solving cubic regression for CpG#4 + [20250519_010019.]: Solving cubic regression for CpG#4 Coefficients: 0Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 - [20250519_005143.]: Samplename: 0 + [20250519_010019.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -7.65533333333333Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 - [20250519_005143.]: Samplename: 12.5 + [20250519_010019.]: Samplename: 12.5 Root: 6.614 --> Root in between the borders! Added to results. Coefficients: -15.206Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 - [20250519_005143.]: Samplename: 25 + [20250519_010019.]: Samplename: 25 Root: 13.372 --> Root in between the borders! Added to results. Coefficients: -24.93Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 - [20250519_005143.]: Samplename: 37.5 + [20250519_010019.]: Samplename: 37.5 Root: 22.462 --> Root in between the borders! Added to results. Coefficients: -33.0395Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 - [20250519_005143.]: Samplename: 50 + [20250519_010019.]: Samplename: 50 Root: 30.424 --> Root in between the borders! Added to results. Coefficients: -49.658Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 - [20250519_005143.]: Samplename: 62.5 + [20250519_010019.]: Samplename: 62.5 Root: 48.07 --> Root in between the borders! Added to results. Coefficients: -55.942Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 - [20250519_005143.]: Samplename: 75 + [20250519_010019.]: Samplename: 75 Root: 55.318 --> Root in between the borders! Added to results. Coefficients: -64.9953333333333Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 - [20250519_005143.]: Samplename: 87.5 + [20250519_010019.]: Samplename: 87.5 Root: 66.469 --> Root in between the borders! Added to results. Coefficients: -87.724Coefficients: 1.17724Coefficients: -0.003Coefficients: 0 - [20250519_005143.]: Samplename: 100 + [20250519_010019.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. - [20250519_005143.]: Solving cubic regression for CpG#5 + [20250519_010019.]: Solving cubic regression for CpG#5 Coefficients: 0Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_005143.]: Samplename: 0 + [20250519_010019.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -4.144Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_005143.]: Samplename: 12.5 + [20250519_010019.]: Samplename: 12.5 Root: 10.457 --> Root in between the borders! Added to results. Coefficients: -12.102Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_005143.]: Samplename: 25 + [20250519_010019.]: Samplename: 25 Root: 26.323 --> Root in between the borders! Added to results. Coefficients: -20.536Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_005143.]: Samplename: 37.5 + [20250519_010019.]: Samplename: 37.5 Root: 39.937 --> Root in between the borders! Added to results. Coefficients: -30.0715Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_005143.]: Samplename: 50 + [20250519_010019.]: Samplename: 50 Root: 53.063 --> Root in between the borders! Added to results. Coefficients: -39.034Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_005143.]: Samplename: 62.5 + [20250519_010019.]: Samplename: 62.5 Root: 63.959 --> Root in between the borders! Added to results. Coefficients: -51.059Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_005143.]: Samplename: 75 + [20250519_010019.]: Samplename: 75 Root: 77.051 --> Root in between the borders! Added to results. Coefficients: -60.3906666666667Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_005143.]: Samplename: 87.5 + [20250519_010019.]: Samplename: 87.5 Root: 86.31 --> Root in between the borders! Added to results. Coefficients: -75.446Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_005143.]: Samplename: 100 + [20250519_010019.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. - [20250519_005143.]: Solving cubic regression for CpG#6 + [20250519_010019.]: Solving cubic regression for CpG#6 Coefficients: 0Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 - [20250519_005143.]: Samplename: 0 + [20250519_010019.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -6.54266666666667Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 - [20250519_005143.]: Samplename: 12.5 + [20250519_010019.]: Samplename: 12.5 Root: 8.15 --> Root in between the borders! Added to results. Coefficients: -15.692Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 - [20250519_005143.]: Samplename: 25 + [20250519_010019.]: Samplename: 25 Root: 19.28 --> Root in between the borders! Added to results. Coefficients: -21.804Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 - [20250519_005143.]: Samplename: 37.5 + [20250519_010019.]: Samplename: 37.5 Root: 26.553 --> Root in between the borders! Added to results. Coefficients: -33.2485Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 - [20250519_005143.]: Samplename: 50 + [20250519_010019.]: Samplename: 50 Root: 39.845 --> Root in between the borders! Added to results. Coefficients: -46.704Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 - [20250519_005143.]: Samplename: 62.5 + [20250519_010019.]: Samplename: 62.5 Root: 54.973 --> Root in between the borders! Added to results. Coefficients: -55.636Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 - [20250519_005143.]: Samplename: 75 + [20250519_010019.]: Samplename: 75 Root: 64.743 --> Root in between the borders! Added to results. Coefficients: -71.3493333333333Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 - [20250519_005143.]: Samplename: 87.5 + [20250519_010019.]: Samplename: 87.5 Root: 81.445 --> Root in between the borders! Added to results. Coefficients: -89.46Coefficients: 0.7946Coefficients: 0.001Coefficients: 0 - [20250519_005144.]: Samplename: 100 + [20250519_010019.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. - [20250519_005144.]: Solving cubic regression for CpG#7 + [20250519_010019.]: Solving cubic regression for CpG#7 Coefficients: 0Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 - [20250519_005144.]: Samplename: 0 + [20250519_010019.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -4.18066666666667Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 - [20250519_005144.]: Samplename: 12.5 + [20250519_010019.]: Samplename: 12.5 Root: 3.662 --> Root in between the borders! Added to results. Coefficients: -10.05Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 - [20250519_005144.]: Samplename: 25 + [20250519_010019.]: Samplename: 25 Root: 9.013 --> Root in between the borders! Added to results. Coefficients: -16.236Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 - [20250519_005144.]: Samplename: 37.5 + [20250519_010019.]: Samplename: 37.5 Root: 14.96 --> Root in between the borders! Added to results. Coefficients: -24.8165Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 - [20250519_005144.]: Samplename: 50 + [20250519_010019.]: Samplename: 50 Root: 23.842 --> Root in between the borders! Added to results. Coefficients: -32.75Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 - [20250519_005144.]: Samplename: 62.5 + [20250519_010019.]: Samplename: 62.5 Root: 32.894 --> Root in between the borders! Added to results. Coefficients: -39.954Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 - [20250519_005144.]: Samplename: 75 + [20250519_010019.]: Samplename: 75 Root: 42.068 --> Root in between the borders! Added to results. Coefficients: -42.9206666666667Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 - [20250519_005144.]: Samplename: 87.5 + [20250519_010019.]: Samplename: 87.5 Root: 46.196 --> Root in between the borders! Added to results. Coefficients: -66.008Coefficients: 1.16008Coefficients: -0.005Coefficients: 0 - [20250519_005144.]: Samplename: 100 + [20250519_010019.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. - [20250519_005144.]: Solving cubic regression for CpG#8 + [20250519_010019.]: Solving cubic regression for CpG#8 Coefficients: 0Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_005144.]: Samplename: 0 + [20250519_010019.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -4.35066666666667Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_005144.]: Samplename: 12.5 + [20250519_010019.]: Samplename: 12.5 Root: 8.961 --> Root in between the borders! Added to results. Coefficients: -15.834Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_005144.]: Samplename: 25 + [20250519_010019.]: Samplename: 25 Root: 28.16 --> Root in between the borders! Added to results. Coefficients: -22.254Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_005144.]: Samplename: 37.5 + [20250519_010019.]: Samplename: 37.5 Root: 37.189 --> Root in between the borders! Added to results. Coefficients: -36.529Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_005144.]: Samplename: 50 + [20250519_010019.]: Samplename: 50 Root: 54.661 --> Root in between the borders! Added to results. Coefficients: -47.73Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_005144.]: Samplename: 62.5 + [20250519_010019.]: Samplename: 62.5 Root: 66.642 --> Root in between the borders! Added to results. Coefficients: -60.5715Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_005144.]: Samplename: 75 + [20250519_010019.]: Samplename: 75 Root: 79.079 --> Root in between the borders! Added to results. Coefficients: -63.414Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_005144.]: Samplename: 87.5 + [20250519_010019.]: Samplename: 87.5 Root: 81.681 --> Root in between the borders! Added to results. Coefficients: -84.964Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_005144.]: Samplename: 100 + [20250519_010019.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. - [20250519_005144.]: Solving cubic regression for CpG#9 + [20250519_010019.]: Solving cubic regression for CpG#9 Coefficients: 0Coefficients: 1.52Coefficients: -0.006Coefficients: 0 - [20250519_005144.]: Samplename: 0 + [20250519_010019.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -5.406Coefficients: 1.52Coefficients: -0.006Coefficients: 0 - [20250519_005144.]: Samplename: 12.5 + [20250519_010019.]: Samplename: 12.5 Root: 3.608 --> Root in between the borders! Added to results. Coefficients: -13.716Coefficients: 1.52Coefficients: -0.006Coefficients: 0 - [20250519_005144.]: Samplename: 25 + [20250519_010019.]: Samplename: 25 Root: 9.37 --> Root in between the borders! Added to results. Coefficients: -19.634Coefficients: 1.52Coefficients: -0.006Coefficients: 0 - [20250519_005144.]: Samplename: 37.5 + [20250519_010019.]: Samplename: 37.5 Root: 13.653 --> Root in between the borders! Added to results. Coefficients: -30.406Coefficients: 1.52Coefficients: -0.006Coefficients: 0 - [20250519_005144.]: Samplename: 50 + [20250519_010019.]: Samplename: 50 Root: 21.897 --> Root in between the borders! Added to results. Coefficients: -41.696Coefficients: 1.52Coefficients: -0.006Coefficients: 0 - [20250519_005144.]: Samplename: 62.5 + [20250519_010019.]: Samplename: 62.5 Root: 31.298 --> Root in between the borders! Added to results. Coefficients: -51.9135Coefficients: 1.52Coefficients: -0.006Coefficients: 0 - [20250519_005144.]: Samplename: 75 + [20250519_010019.]: Samplename: 75 Root: 40.689 --> Root in between the borders! Added to results. Coefficients: -64.5026666666667Coefficients: 1.52Coefficients: -0.006Coefficients: 0 - [20250519_005144.]: Samplename: 87.5 + [20250519_010019.]: Samplename: 87.5 Root: 53.907 --> Root in between the borders! Added to results. Coefficients: -92Coefficients: 1.52Coefficients: -0.006Coefficients: 0 - [20250519_005144.]: Samplename: 100 + [20250519_010019.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. - [20250519_005144.]: Solving cubic regression for row_means + [20250519_010019.]: Solving cubic regression for row_means Coefficients: -0.000111111111110951Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 - [20250519_005144.]: Samplename: 0 + [20250519_010019.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -5.70137037037037Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 - [20250519_005144.]: Samplename: 12.5 + [20250519_010019.]: Samplename: 12.5 Root: 6.107 --> Root in between the borders! Added to results. Coefficients: -14.1261111111111Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 - [20250519_005144.]: Samplename: 25 + [20250519_010019.]: Samplename: 25 Root: 15.282 --> Root in between the borders! Added to results. Coefficients: -21.3781111111111Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 - [20250519_005144.]: Samplename: 37.5 + [20250519_010019.]: Samplename: 37.5 Root: 23.331 --> Root in between the borders! Added to results. Coefficients: -31.7548888888889Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 - [20250519_005144.]: Samplename: 50 + [20250519_010019.]: Samplename: 50 Root: 35.107 --> Root in between the borders! Added to results. Coefficients: -43.9507777777778Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 - [20250519_005144.]: Samplename: 62.5 + [20250519_010019.]: Samplename: 62.5 Root: 49.368 --> Root in between the borders! Added to results. Coefficients: -53.6321111111111Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 - [20250519_005144.]: Samplename: 75 + [20250519_010019.]: Samplename: 75 Root: 61.044 --> Root in between the borders! Added to results. Coefficients: -61.6602592592593Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 - [20250519_005144.]: Samplename: 87.5 + [20250519_010019.]: Samplename: 87.5 Root: 70.984 --> Root in between the borders! Added to results. Coefficients: -83.9632222222222Coefficients: 0.93963Coefficients: -0.001Coefficients: 0 - [20250519_005144.]: Samplename: 100 + [20250519_010019.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 - [20250519_005144.]: + [20250519_010019.]: ### Starting with regression calculations ### - [20250519_005144.]: Entered 'regression_type1'-Function + [20250519_010019.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : @@ -10940,910 +10940,910 @@ singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : step factor 0.000488281 reduced below 'minFactor' of 0.000976562 - [20250519_005144.]: # CpG-site: CpG#1 + [20250519_010019.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 4.66057108506072, 9.3520572622014, 15.5898391276712, 23.6006773278817, 33.1210812975699, 42.000790440049, 49.5097019721065, 100)c(0, 7.83942891493928, 15.6479427377986, 21.9101608723288, 26.3993226721183, 29.3789187024301, 32.999209559951, 37.9902980278935, 0)c(NA, 62.7154313195142, 62.5917709511944, 58.4270956595435, 52.7986453442366, 47.0062699238882, 43.9989460799347, 43.4174834604497, 0) - [20250519_005144.]: Logging df_agg: CpG#1 - [20250519_005144.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005144.]: c(0, 4.66057108506072, 9.3520572622014, 15.5898391276712, 23.6006773278817, 33.1210812975699, 42.000790440049, 49.5097019721065, 100)[20250519_005144.]: c(0, 7.83942891493928, 15.6479427377986, 21.9101608723288, 26.3993226721183, 29.3789187024301, 32.999209559951, 37.9902980278935, 0)[20250519_005144.]: c(NA, 62.7154313195142, 62.5917709511944, 58.4270956595435, 52.7986453442366, 47.0062699238882, 43.9989460799347, 43.4174834604497, 0) - [20250519_005144.]: Entered 'hyperbolic_regression'-Function - [20250519_005144.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005144.]: Entered 'cubic_regression'-Function - [20250519_005144.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005144.]: # CpG-site: CpG#2 + [20250519_010019.]: Logging df_agg: CpG#1 + [20250519_010019.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010019.]: c(0, 4.66057108506072, 9.3520572622014, 15.5898391276712, 23.6006773278817, 33.1210812975699, 42.000790440049, 49.5097019721065, 100)[20250519_010019.]: c(0, 7.83942891493928, 15.6479427377986, 21.9101608723288, 26.3993226721183, 29.3789187024301, 32.999209559951, 37.9902980278935, 0)[20250519_010019.]: c(NA, 62.7154313195142, 62.5917709511944, 58.4270956595435, 52.7986453442366, 47.0062699238882, 43.9989460799347, 43.4174834604497, 0) + [20250519_010019.]: Entered 'hyperbolic_regression'-Function + [20250519_010019.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010020.]: Entered 'cubic_regression'-Function + [20250519_010020.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010020.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.41473135558285, 21.0155787157944, 28.98380769729, 40.7258283847219, 60.321752127534, 69.670868358929, 79.5285048174076, 100)c(0, 4.08526864441715, 3.9844212842056, 8.51619230271, 9.2741716152781, 2.178247872466, 5.329131641071, 7.97149518259241, 0)c(NA, 32.6821491553372, 15.9376851368224, 22.70984614056, 18.5483432305562, 3.4851965959456, 7.10550885476133, 9.11028020867704, 0) - [20250519_005144.]: Logging df_agg: CpG#2 - [20250519_005144.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005144.]: c(0, 8.41473135558285, 21.0155787157944, 28.98380769729, 40.7258283847219, 60.321752127534, 69.670868358929, 79.5285048174076, 100)[20250519_005144.]: c(0, 4.08526864441715, 3.9844212842056, 8.51619230271, 9.2741716152781, 2.178247872466, 5.329131641071, 7.97149518259241, 0)[20250519_005144.]: c(NA, 32.6821491553372, 15.9376851368224, 22.70984614056, 18.5483432305562, 3.4851965959456, 7.10550885476133, 9.11028020867704, 0) - [20250519_005144.]: Entered 'hyperbolic_regression'-Function - [20250519_005144.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005144.]: Entered 'cubic_regression'-Function - [20250519_005144.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005145.]: # CpG-site: CpG#3 + [20250519_010020.]: Logging df_agg: CpG#2 + [20250519_010020.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010020.]: c(0, 8.41473135558285, 21.0155787157944, 28.98380769729, 40.7258283847219, 60.321752127534, 69.670868358929, 79.5285048174076, 100)[20250519_010020.]: c(0, 4.08526864441715, 3.9844212842056, 8.51619230271, 9.2741716152781, 2.178247872466, 5.329131641071, 7.97149518259241, 0)[20250519_010020.]: c(NA, 32.6821491553372, 15.9376851368224, 22.70984614056, 18.5483432305562, 3.4851965959456, 7.10550885476133, 9.11028020867704, 0) + [20250519_010020.]: Entered 'hyperbolic_regression'-Function + [20250519_010020.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010020.]: Entered 'cubic_regression'-Function + [20250519_010020.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010020.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 5.74980505230347, 15.0124787349221, 22.8043512255536, 34.3082682140428, 47.6381691654944, 61.8773912993076, 65.4860304554385, 100)c(0, 6.75019494769653, 9.9875212650779, 14.6956487744464, 15.6917317859572, 14.8618308345056, 13.1226087006924, 22.0139695445615, 0)c(NA, 54.0015595815722, 39.9500850603116, 39.1883967318571, 31.3834635719144, 23.778929335209, 17.4968116009232, 25.1588223366417, 0) - [20250519_005145.]: Logging df_agg: CpG#3 - [20250519_005145.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005145.]: c(0, 5.74980505230347, 15.0124787349221, 22.8043512255536, 34.3082682140428, 47.6381691654944, 61.8773912993076, 65.4860304554385, 100)[20250519_005145.]: c(0, 6.75019494769653, 9.9875212650779, 14.6956487744464, 15.6917317859572, 14.8618308345056, 13.1226087006924, 22.0139695445615, 0)[20250519_005145.]: c(NA, 54.0015595815722, 39.9500850603116, 39.1883967318571, 31.3834635719144, 23.778929335209, 17.4968116009232, 25.1588223366417, 0) - [20250519_005145.]: Entered 'hyperbolic_regression'-Function - [20250519_005145.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005145.]: Entered 'cubic_regression'-Function - [20250519_005145.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005145.]: # CpG-site: CpG#4 + [20250519_010020.]: Logging df_agg: CpG#3 + [20250519_010020.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010020.]: c(0, 5.74980505230347, 15.0124787349221, 22.8043512255536, 34.3082682140428, 47.6381691654944, 61.8773912993076, 65.4860304554385, 100)[20250519_010020.]: c(0, 6.75019494769653, 9.9875212650779, 14.6956487744464, 15.6917317859572, 14.8618308345056, 13.1226087006924, 22.0139695445615, 0)[20250519_010020.]: c(NA, 54.0015595815722, 39.9500850603116, 39.1883967318571, 31.3834635719144, 23.778929335209, 17.4968116009232, 25.1588223366417, 0) + [20250519_010020.]: Entered 'hyperbolic_regression'-Function + [20250519_010020.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010020.]: Entered 'cubic_regression'-Function + [20250519_010020.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010021.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 6.6142663267093, 13.3723444572473, 22.4624407354305, 30.4240096190196, 48.070278909378, 55.3176226954937, 66.4686809672836, 100)c(0, 5.8857336732907, 11.6276555427527, 15.0375592645695, 19.5759903809804, 14.429721090622, 19.6823773045063, 21.0313190327164, 0)c(NA, 47.0858693863256, 46.5106221710108, 40.100158038852, 39.1519807619608, 23.0875537449952, 26.2431697393417, 24.0357931802473, 0) - [20250519_005145.]: Logging df_agg: CpG#4 - [20250519_005145.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005145.]: c(0, 6.6142663267093, 13.3723444572473, 22.4624407354305, 30.4240096190196, 48.070278909378, 55.3176226954937, 66.4686809672836, 100)[20250519_005145.]: c(0, 5.8857336732907, 11.6276555427527, 15.0375592645695, 19.5759903809804, 14.429721090622, 19.6823773045063, 21.0313190327164, 0)[20250519_005145.]: c(NA, 47.0858693863256, 46.5106221710108, 40.100158038852, 39.1519807619608, 23.0875537449952, 26.2431697393417, 24.0357931802473, 0) - [20250519_005145.]: Entered 'hyperbolic_regression'-Function - [20250519_005145.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005145.]: Entered 'cubic_regression'-Function - [20250519_005145.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005145.]: # CpG-site: CpG#5 + [20250519_010021.]: Logging df_agg: CpG#4 + [20250519_010021.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010021.]: c(0, 6.6142663267093, 13.3723444572473, 22.4624407354305, 30.4240096190196, 48.070278909378, 55.3176226954937, 66.4686809672836, 100)[20250519_010021.]: c(0, 5.8857336732907, 11.6276555427527, 15.0375592645695, 19.5759903809804, 14.429721090622, 19.6823773045063, 21.0313190327164, 0)[20250519_010021.]: c(NA, 47.0858693863256, 46.5106221710108, 40.100158038852, 39.1519807619608, 23.0875537449952, 26.2431697393417, 24.0357931802473, 0) + [20250519_010021.]: Entered 'hyperbolic_regression'-Function + [20250519_010021.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010021.]: Entered 'cubic_regression'-Function + [20250519_010021.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010021.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.4570373964758, 26.3229081557653, 39.9371114374682, 53.0630784939681, 63.9590902125397, 77.051078420522, 86.3095786035144, 100)c(0, 2.0429626035242, 1.3229081557653, 2.4371114374682, 3.0630784939681, 1.4590902125397, 2.051078420522, 1.1904213964856, 0)c(NA, 16.3437008281936, 5.2916326230612, 6.49896383324852, 6.1261569879362, 2.33454434006352, 2.73477122736267, 1.36048159598355, 0) - [20250519_005145.]: Logging df_agg: CpG#5 - [20250519_005145.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005145.]: c(0, 10.4570373964758, 26.3229081557653, 39.9371114374682, 53.0630784939681, 63.9590902125397, 77.051078420522, 86.3095786035144, 100)[20250519_005145.]: c(0, 2.0429626035242, 1.3229081557653, 2.4371114374682, 3.0630784939681, 1.4590902125397, 2.051078420522, 1.1904213964856, 0)[20250519_005145.]: c(NA, 16.3437008281936, 5.2916326230612, 6.49896383324852, 6.1261569879362, 2.33454434006352, 2.73477122736267, 1.36048159598355, 0) - [20250519_005145.]: Entered 'hyperbolic_regression'-Function - [20250519_005145.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005146.]: Entered 'cubic_regression'-Function - [20250519_005146.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005146.]: # CpG-site: CpG#6 + [20250519_010021.]: Logging df_agg: CpG#5 + [20250519_010021.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010021.]: c(0, 10.4570373964758, 26.3229081557653, 39.9371114374682, 53.0630784939681, 63.9590902125397, 77.051078420522, 86.3095786035144, 100)[20250519_010021.]: c(0, 2.0429626035242, 1.3229081557653, 2.4371114374682, 3.0630784939681, 1.4590902125397, 2.051078420522, 1.1904213964856, 0)[20250519_010021.]: c(NA, 16.3437008281936, 5.2916326230612, 6.49896383324852, 6.1261569879362, 2.33454434006352, 2.73477122736267, 1.36048159598355, 0) + [20250519_010021.]: Entered 'hyperbolic_regression'-Function + [20250519_010021.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010021.]: Entered 'cubic_regression'-Function + [20250519_010021.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010021.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.15031343762292, 19.2804724179951, 26.5529108075111, 39.8450445790276, 54.9734681583699, 64.742519688394, 81.444841573602, 100)c(0, 4.34968656237708, 5.7195275820049, 10.9470891924889, 10.1549554209724, 7.5265318416301, 10.257480311606, 6.05515842639799, 0)c(NA, 34.7974924990166, 22.8781103280196, 29.1922378466371, 20.3099108419448, 12.0424509466082, 13.6766404154747, 6.92018105874057, 0) - [20250519_005146.]: Logging df_agg: CpG#6 - [20250519_005146.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005146.]: c(0, 8.15031343762292, 19.2804724179951, 26.5529108075111, 39.8450445790276, 54.9734681583699, 64.742519688394, 81.444841573602, 100)[20250519_005146.]: c(0, 4.34968656237708, 5.7195275820049, 10.9470891924889, 10.1549554209724, 7.5265318416301, 10.257480311606, 6.05515842639799, 0)[20250519_005146.]: c(NA, 34.7974924990166, 22.8781103280196, 29.1922378466371, 20.3099108419448, 12.0424509466082, 13.6766404154747, 6.92018105874057, 0) - [20250519_005146.]: Entered 'hyperbolic_regression'-Function - [20250519_005146.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005146.]: Entered 'cubic_regression'-Function - [20250519_005146.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005146.]: # CpG-site: CpG#7 + [20250519_010021.]: Logging df_agg: CpG#6 + [20250519_010021.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010021.]: c(0, 8.15031343762292, 19.2804724179951, 26.5529108075111, 39.8450445790276, 54.9734681583699, 64.742519688394, 81.444841573602, 100)[20250519_010021.]: c(0, 4.34968656237708, 5.7195275820049, 10.9470891924889, 10.1549554209724, 7.5265318416301, 10.257480311606, 6.05515842639799, 0)[20250519_010021.]: c(NA, 34.7974924990166, 22.8781103280196, 29.1922378466371, 20.3099108419448, 12.0424509466082, 13.6766404154747, 6.92018105874057, 0) + [20250519_010021.]: Entered 'hyperbolic_regression'-Function + [20250519_010021.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010022.]: Entered 'cubic_regression'-Function + [20250519_010022.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010022.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 3.66155933245296, 9.01334562885863, 14.9602090097958, 23.8420840440459, 32.8944868748769, 42.0684566960277, 46.1959470960303, 99.9999999999999)c(0, 8.83844066754704, 15.9866543711414, 22.5397909902042, 26.1579159559541, 29.6055131251231, 32.9315433039723, 41.3040529039697, 9.9475983006414e-14)c(NA, 70.7075253403763, 63.9466174845655, 60.1061093072112, 52.3158319119082, 47.368821000197, 43.9087244052964, 47.2046318902511, 9.9475983006414e-14) - [20250519_005146.]: Logging df_agg: CpG#7 - [20250519_005146.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005146.]: c(0, 3.66155933245296, 9.01334562885863, 14.9602090097958, 23.8420840440459, 32.8944868748769, 42.0684566960277, 46.1959470960303, 99.9999999999999)[20250519_005146.]: c(0, 8.83844066754704, 15.9866543711414, 22.5397909902042, 26.1579159559541, 29.6055131251231, 32.9315433039723, 41.3040529039697, 9.9475983006414e-14)[20250519_005146.]: c(NA, 70.7075253403763, 63.9466174845655, 60.1061093072112, 52.3158319119082, 47.368821000197, 43.9087244052964, 47.2046318902511, 9.9475983006414e-14) - [20250519_005146.]: Entered 'hyperbolic_regression'-Function - [20250519_005146.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005146.]: Entered 'cubic_regression'-Function - [20250519_005146.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005147.]: # CpG-site: CpG#8 + [20250519_010022.]: Logging df_agg: CpG#7 + [20250519_010022.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010022.]: c(0, 3.66155933245296, 9.01334562885863, 14.9602090097958, 23.8420840440459, 32.8944868748769, 42.0684566960277, 46.1959470960303, 99.9999999999999)[20250519_010022.]: c(0, 8.83844066754704, 15.9866543711414, 22.5397909902042, 26.1579159559541, 29.6055131251231, 32.9315433039723, 41.3040529039697, 9.9475983006414e-14)[20250519_010022.]: c(NA, 70.7075253403763, 63.9466174845655, 60.1061093072112, 52.3158319119082, 47.368821000197, 43.9087244052964, 47.2046318902511, 9.9475983006414e-14) + [20250519_010022.]: Entered 'hyperbolic_regression'-Function + [20250519_010022.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010022.]: Entered 'cubic_regression'-Function + [20250519_010022.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010022.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 8.96146907472175, 28.1602892189673, 37.1893361505397, 54.6609191320759, 66.6424746382684, 79.0794300908275, 81.6808296744085, 100)c(0, 3.53853092527825, 3.1602892189673, 0.310663849460298, 4.6609191320759, 4.1424746382684, 4.0794300908275, 5.8191703255915, 0)c(NA, 28.308247402226, 12.6411568758692, 0.828436931894127, 9.32183826415179, 6.62795942122943, 5.43924012110333, 6.65048037210457, 0) - [20250519_005147.]: Logging df_agg: CpG#8 - [20250519_005147.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005147.]: c(0, 8.96146907472175, 28.1602892189673, 37.1893361505397, 54.6609191320759, 66.6424746382684, 79.0794300908275, 81.6808296744085, 100)[20250519_005147.]: c(0, 3.53853092527825, 3.1602892189673, 0.310663849460298, 4.6609191320759, 4.1424746382684, 4.0794300908275, 5.8191703255915, 0)[20250519_005147.]: c(NA, 28.308247402226, 12.6411568758692, 0.828436931894127, 9.32183826415179, 6.62795942122943, 5.43924012110333, 6.65048037210457, 0) - [20250519_005147.]: Entered 'hyperbolic_regression'-Function - [20250519_005147.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005147.]: Entered 'cubic_regression'-Function - [20250519_005147.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005147.]: # CpG-site: CpG#9 + [20250519_010022.]: Logging df_agg: CpG#8 + [20250519_010022.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010022.]: c(0, 8.96146907472175, 28.1602892189673, 37.1893361505397, 54.6609191320759, 66.6424746382684, 79.0794300908275, 81.6808296744085, 100)[20250519_010022.]: c(0, 3.53853092527825, 3.1602892189673, 0.310663849460298, 4.6609191320759, 4.1424746382684, 4.0794300908275, 5.8191703255915, 0)[20250519_010022.]: c(NA, 28.308247402226, 12.6411568758692, 0.828436931894127, 9.32183826415179, 6.62795942122943, 5.43924012110333, 6.65048037210457, 0) + [20250519_010022.]: Entered 'hyperbolic_regression'-Function + [20250519_010022.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010022.]: Entered 'cubic_regression'-Function + [20250519_010022.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010023.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 3.60796342124625, 9.37027096567147, 13.6529015267019, 21.8965479410597, 31.2983747080124, 40.6887948365007, 53.9067876123417, 100)c(0, 8.89203657875375, 15.6297290343285, 23.8470984732981, 28.1034520589403, 31.2016252919876, 34.3112051634993, 33.5932123876583, 0)c(NA, 71.13629263003, 62.5189161373141, 63.5922625954616, 56.2069041178806, 49.9226004671802, 45.7482735513324, 38.3922427287523, 0) - [20250519_005147.]: Logging df_agg: CpG#9 - [20250519_005147.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005147.]: c(0, 3.60796342124625, 9.37027096567147, 13.6529015267019, 21.8965479410597, 31.2983747080124, 40.6887948365007, 53.9067876123417, 100)[20250519_005147.]: c(0, 8.89203657875375, 15.6297290343285, 23.8470984732981, 28.1034520589403, 31.2016252919876, 34.3112051634993, 33.5932123876583, 0)[20250519_005147.]: c(NA, 71.13629263003, 62.5189161373141, 63.5922625954616, 56.2069041178806, 49.9226004671802, 45.7482735513324, 38.3922427287523, 0) - [20250519_005147.]: Entered 'hyperbolic_regression'-Function - [20250519_005147.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005147.]: Entered 'cubic_regression'-Function - [20250519_005147.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005147.]: # CpG-site: row_means + [20250519_010023.]: Logging df_agg: CpG#9 + [20250519_010023.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010023.]: c(0, 3.60796342124625, 9.37027096567147, 13.6529015267019, 21.8965479410597, 31.2983747080124, 40.6887948365007, 53.9067876123417, 100)[20250519_010023.]: c(0, 8.89203657875375, 15.6297290343285, 23.8470984732981, 28.1034520589403, 31.2016252919876, 34.3112051634993, 33.5932123876583, 0)[20250519_010023.]: c(NA, 71.13629263003, 62.5189161373141, 63.5922625954616, 56.2069041178806, 49.9226004671802, 45.7482735513324, 38.3922427287523, 0) + [20250519_010023.]: Entered 'hyperbolic_regression'-Function + [20250519_010023.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010023.]: Entered 'cubic_regression'-Function + [20250519_010023.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010023.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.000118249869729556, 6.10737244290489, 15.2822474794705, 23.3309318476314, 35.1067698776441, 49.3683845454072, 61.0436420088989, 70.9843655625978, 100)c(0.000118249869729556, 6.39262755709511, 9.7177525205295, 14.1690681523686, 14.8932301223559, 13.1316154545928, 13.9563579911011, 16.5156344374022, 0)c(NA, 51.1410204567609, 38.871010082118, 37.7841817396496, 29.7864602447118, 21.0105847273485, 18.6084773214681, 18.8750107856025, 0) - [20250519_005147.]: Logging df_agg: row_means - [20250519_005147.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005147.]: c(0.000118249869729556, 6.10737244290489, 15.2822474794705, 23.3309318476314, 35.1067698776441, 49.3683845454072, 61.0436420088989, 70.9843655625978, 100)[20250519_005147.]: c(0.000118249869729556, 6.39262755709511, 9.7177525205295, 14.1690681523686, 14.8932301223559, 13.1316154545928, 13.9563579911011, 16.5156344374022, 0)[20250519_005147.]: c(NA, 51.1410204567609, 38.871010082118, 37.7841817396496, 29.7864602447118, 21.0105847273485, 18.6084773214681, 18.8750107856025, 0) - [20250519_005147.]: Entered 'hyperbolic_regression'-Function - [20250519_005147.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005148.]: Entered 'cubic_regression'-Function - [20250519_005148.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005148.]: Entered 'solving_equations'-Function - [20250519_005148.]: Solving hyperbolic regression for CpG#1 + [20250519_010023.]: Logging df_agg: row_means + [20250519_010023.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010023.]: c(0.000118249869729556, 6.10737244290489, 15.2822474794705, 23.3309318476314, 35.1067698776441, 49.3683845454072, 61.0436420088989, 70.9843655625978, 100)[20250519_010023.]: c(0.000118249869729556, 6.39262755709511, 9.7177525205295, 14.1690681523686, 14.8932301223559, 13.1316154545928, 13.9563579911011, 16.5156344374022, 0)[20250519_010023.]: c(NA, 51.1410204567609, 38.871010082118, 37.7841817396496, 29.7864602447118, 21.0105847273485, 18.6084773214681, 18.8750107856025, 0) + [20250519_010023.]: Entered 'hyperbolic_regression'-Function + [20250519_010023.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010023.]: Entered 'cubic_regression'-Function + [20250519_010023.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010024.]: Entered 'solving_equations'-Function + [20250519_010024.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 78.9992198550223 - [20250519_005148.]: Samplename: Sample#1 + [20250519_010024.]: Samplename: Sample#1 Root: 78.999 --> Root in between the borders! Added to results. Hyperbolic solved: 31.2870579236875 - [20250519_005148.]: Samplename: Sample#10 + [20250519_010024.]: Samplename: Sample#10 Root: 31.287 --> Root in between the borders! Added to results. Hyperbolic solved: 42.7215290847412 - [20250519_005148.]: Samplename: Sample#2 + [20250519_010024.]: Samplename: Sample#2 Root: 42.722 --> Root in between the borders! Added to results. Hyperbolic solved: 57.8350974981729 - [20250519_005148.]: Samplename: Sample#3 + [20250519_010024.]: Samplename: Sample#3 Root: 57.835 --> Root in between the borders! Added to results. Hyperbolic solved: 11.2415690196975 - [20250519_005148.]: Samplename: Sample#4 + [20250519_010024.]: Samplename: Sample#4 Root: 11.242 --> Root in between the borders! Added to results. Hyperbolic solved: 23.5440570752846 - [20250519_005148.]: Samplename: Sample#5 + [20250519_010024.]: Samplename: Sample#5 Root: 23.544 --> Root in between the borders! Added to results. Hyperbolic solved: 24.7858714956021 - [20250519_005148.]: Samplename: Sample#6 + [20250519_010024.]: Samplename: Sample#6 Root: 24.786 --> Root in between the borders! Added to results. Hyperbolic solved: 46.4156211496502 - [20250519_005148.]: Samplename: Sample#7 + [20250519_010024.]: Samplename: Sample#7 Root: 46.416 --> Root in between the borders! Added to results. Hyperbolic solved: 84.4614182359021 - [20250519_005148.]: Samplename: Sample#8 + [20250519_010024.]: Samplename: Sample#8 Root: 84.461 --> Root in between the borders! Added to results. Hyperbolic solved: -1.41454023853738 - [20250519_005148.]: Samplename: Sample#9 + [20250519_010024.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -1.415 --> '-10 < root < 0' --> substitute 0 - [20250519_005148.]: Solving hyperbolic regression for CpG#2 + [20250519_010024.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 76.6628425084681 - [20250519_005148.]: Samplename: Sample#1 + [20250519_010024.]: Samplename: Sample#1 Root: 76.663 --> Root in between the borders! Added to results. Hyperbolic solved: 31.002922067723 - [20250519_005148.]: Samplename: Sample#10 + [20250519_010024.]: Samplename: Sample#10 Root: 31.003 --> Root in between the borders! Added to results. Hyperbolic solved: 42.8794162472613 - [20250519_005148.]: Samplename: Sample#2 + [20250519_010024.]: Samplename: Sample#2 Root: 42.879 --> Root in between the borders! Added to results. Hyperbolic solved: 59.1562251576939 - [20250519_005148.]: Samplename: Sample#3 + [20250519_010024.]: Samplename: Sample#3 Root: 59.156 --> Root in between the borders! Added to results. Hyperbolic solved: 4.04613842773285 - [20250519_005148.]: Samplename: Sample#4 + [20250519_010024.]: Samplename: Sample#4 Root: 4.046 --> Root in between the borders! Added to results. Hyperbolic solved: 19.9574827771543 - [20250519_005148.]: Samplename: Sample#5 + [20250519_010024.]: Samplename: Sample#5 Root: 19.957 --> Root in between the borders! Added to results. Hyperbolic solved: 17.4372350357645 - [20250519_005148.]: Samplename: Sample#6 + [20250519_010024.]: Samplename: Sample#6 Root: 17.437 --> Root in between the borders! Added to results. Hyperbolic solved: 41.0968872148931 - [20250519_005148.]: Samplename: Sample#7 + [20250519_010024.]: Samplename: Sample#7 Root: 41.097 --> Root in between the borders! Added to results. Hyperbolic solved: 85.7111018674234 - [20250519_005148.]: Samplename: Sample#8 + [20250519_010024.]: Samplename: Sample#8 Root: 85.711 --> Root in between the borders! Added to results. Hyperbolic solved: 2.08554114625032 - [20250519_005148.]: Samplename: Sample#9 + [20250519_010024.]: Samplename: Sample#9 Root: 2.086 --> Root in between the borders! Added to results. - [20250519_005148.]: Solving hyperbolic regression for CpG#3 + [20250519_010024.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 74.5413598656994 - [20250519_005148.]: Samplename: Sample#1 + [20250519_010024.]: Samplename: Sample#1 Root: 74.541 --> Root in between the borders! Added to results. Hyperbolic solved: 28.3514323412579 - [20250519_005148.]: Samplename: Sample#10 + [20250519_010024.]: Samplename: Sample#10 Root: 28.351 --> Root in between the borders! Added to results. Hyperbolic solved: 42.6007641487945 - [20250519_005148.]: Samplename: Sample#2 + [20250519_010024.]: Samplename: Sample#2 Root: 42.601 --> Root in between the borders! Added to results. Hyperbolic solved: 56.3207791919885 - [20250519_005148.]: Samplename: Sample#3 + [20250519_010024.]: Samplename: Sample#3 Root: 56.321 --> Root in between the borders! Added to results. Hyperbolic solved: 7.98800399083 - [20250519_005148.]: Samplename: Sample#4 + [20250519_010024.]: Samplename: Sample#4 Root: 7.988 --> Root in between the borders! Added to results. Hyperbolic solved: 24.6963927948962 - [20250519_005148.]: Samplename: Sample#5 + [20250519_010024.]: Samplename: Sample#5 Root: 24.696 --> Root in between the borders! Added to results. Hyperbolic solved: 26.8806215804592 - [20250519_005148.]: Samplename: Sample#6 + [20250519_010024.]: Samplename: Sample#6 Root: 26.881 --> Root in between the borders! Added to results. Hyperbolic solved: 44.8239489304102 - [20250519_005148.]: Samplename: Sample#7 + [20250519_010024.]: Samplename: Sample#7 Root: 44.824 --> Root in between the borders! Added to results. Hyperbolic solved: 84.669654917361 - [20250519_005148.]: Samplename: Sample#8 + [20250519_010024.]: Samplename: Sample#8 Root: 84.67 --> Root in between the borders! Added to results. Hyperbolic solved: -1.26160051687973 - [20250519_005148.]: Samplename: Sample#9 + [20250519_010024.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -1.262 --> '-10 < root < 0' --> substitute 0 - [20250519_005148.]: Solving hyperbolic regression for CpG#4 + [20250519_010024.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 75.8530019120299 - [20250519_005148.]: Samplename: Sample#1 + [20250519_010024.]: Samplename: Sample#1 Root: 75.853 --> Root in between the borders! Added to results. Hyperbolic solved: 29.0711677507654 - [20250519_005148.]: Samplename: Sample#10 + [20250519_010024.]: Samplename: Sample#10 Root: 29.071 --> Root in between the borders! Added to results. Hyperbolic solved: 44.0485537238583 - [20250519_005148.]: Samplename: Sample#2 + [20250519_010024.]: Samplename: Sample#2 Root: 44.049 --> Root in between the borders! Added to results. Hyperbolic solved: 58.7878536128746 - [20250519_005148.]: Samplename: Sample#3 + [20250519_010024.]: Samplename: Sample#3 Root: 58.788 --> Root in between the borders! Added to results. Hyperbolic solved: 11.0370782280591 - [20250519_005148.]: Samplename: Sample#4 + [20250519_010024.]: Samplename: Sample#4 Root: 11.037 --> Root in between the borders! Added to results. Hyperbolic solved: 23.0042107924666 - [20250519_005148.]: Samplename: Sample#5 + [20250519_010024.]: Samplename: Sample#5 Root: 23.004 --> Root in between the borders! Added to results. Hyperbolic solved: 27.9521038368967 - [20250519_005148.]: Samplename: Sample#6 + [20250519_010024.]: Samplename: Sample#6 Root: 27.952 --> Root in between the borders! Added to results. Hyperbolic solved: 42.50025622677 - [20250519_005148.]: Samplename: Sample#7 + [20250519_010024.]: Samplename: Sample#7 Root: 42.5 --> Root in between the borders! Added to results. Hyperbolic solved: 84.6870942263369 - [20250519_005148.]: Samplename: Sample#8 + [20250519_010024.]: Samplename: Sample#8 Root: 84.687 --> Root in between the borders! Added to results. Hyperbolic solved: 3.01041300417756 - [20250519_005148.]: Samplename: Sample#9 + [20250519_010024.]: Samplename: Sample#9 Root: 3.01 --> Root in between the borders! Added to results. - [20250519_005148.]: Solving cubic regression for CpG#5 + [20250519_010024.]: Solving cubic regression for CpG#5 Coefficients: -47.8373333333333Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_005148.]: Samplename: Sample#1 + [20250519_010024.]: Samplename: Sample#1 Root: 73.686 --> Root in between the borders! Added to results. Coefficients: -13.588Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_005148.]: Samplename: Sample#10 + [20250519_010024.]: Samplename: Sample#10 Root: 28.906 --> Root in between the borders! Added to results. Coefficients: -25.3211428571429Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_005148.]: Samplename: Sample#2 + [20250519_010024.]: Samplename: Sample#2 Root: 46.761 --> Root in between the borders! Added to results. Coefficients: -32.064Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_005148.]: Samplename: Sample#3 + [20250519_010024.]: Samplename: Sample#3 Root: 55.588 --> Root in between the borders! Added to results. Coefficients: -4.074Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_005148.]: Samplename: Sample#4 + [20250519_010024.]: Samplename: Sample#4 Root: 10.297 --> Root in between the borders! Added to results. Coefficients: -11.434Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_005148.]: Samplename: Sample#5 + [20250519_010024.]: Samplename: Sample#5 Root: 25.131 --> Root in between the borders! Added to results. Coefficients: -13.294Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_005148.]: Samplename: Sample#6 + [20250519_010024.]: Samplename: Sample#6 Root: 28.402 --> Root in between the borders! Added to results. Coefficients: -24.288Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_005148.]: Samplename: Sample#7 + [20250519_010024.]: Samplename: Sample#7 Root: 45.332 --> Root in between the borders! Added to results. Coefficients: -63.134Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_005148.]: Samplename: Sample#8 + [20250519_010024.]: Samplename: Sample#8 Root: 88.909 --> Root in between the borders! Added to results. Coefficients: 0.0360000000000005Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_005148.]: Samplename: Sample#9 + [20250519_010024.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.102 --> '-10 < root < 0' --> substitute 0 - [20250519_005148.]: Solving hyperbolic regression for CpG#6 + [20250519_010024.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 79.2062097176894 - [20250519_005148.]: Samplename: Sample#1 + [20250519_010024.]: Samplename: Sample#1 Root: 79.206 --> Root in between the borders! Added to results. Hyperbolic solved: 30.234912908094 - [20250519_005148.]: Samplename: Sample#10 + [20250519_010024.]: Samplename: Sample#10 Root: 30.235 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8992139020538 - [20250519_005148.]: Samplename: Sample#2 + [20250519_010024.]: Samplename: Sample#2 Root: 41.899 --> Root in between the borders! Added to results. Hyperbolic solved: 56.8778124304882 - [20250519_005148.]: Samplename: Sample#3 + [20250519_010024.]: Samplename: Sample#3 Root: 56.878 --> Root in between the borders! Added to results. Hyperbolic solved: 8.80900690226663 - [20250519_005148.]: Samplename: Sample#4 + [20250519_010024.]: Samplename: Sample#4 Root: 8.809 --> Root in between the borders! Added to results. Hyperbolic solved: 18.6793858198319 - [20250519_005148.]: Samplename: Sample#5 + [20250519_010024.]: Samplename: Sample#5 Root: 18.679 --> Root in between the borders! Added to results. Hyperbolic solved: 29.9638526705022 - [20250519_005148.]: Samplename: Sample#6 + [20250519_010024.]: Samplename: Sample#6 Root: 29.964 --> Root in between the borders! Added to results. Hyperbolic solved: 42.8669225173731 - [20250519_005148.]: Samplename: Sample#7 + [20250519_010024.]: Samplename: Sample#7 Root: 42.867 --> Root in between the borders! Added to results. Hyperbolic solved: 86.6206311389839 - [20250519_005148.]: Samplename: Sample#8 + [20250519_010024.]: Samplename: Sample#8 Root: 86.621 --> Root in between the borders! Added to results. Hyperbolic solved: 1.38882504570857 - [20250519_005148.]: Samplename: Sample#9 + [20250519_010024.]: Samplename: Sample#9 Root: 1.389 --> Root in between the borders! Added to results. - [20250519_005148.]: Solving hyperbolic regression for CpG#7 + [20250519_010024.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 77.5203002291111 - [20250519_005148.]: Samplename: Sample#1 + [20250519_010024.]: Samplename: Sample#1 Root: 77.52 --> Root in between the borders! Added to results. Hyperbolic solved: 27.0810020209347 - [20250519_005148.]: Samplename: Sample#10 + [20250519_010024.]: Samplename: Sample#10 Root: 27.081 --> Root in between the borders! Added to results. Hyperbolic solved: 48.4274819445682 - [20250519_005148.]: Samplename: Sample#2 + [20250519_010024.]: Samplename: Sample#2 Root: 48.427 --> Root in between the borders! Added to results. Hyperbolic solved: 58.8711296078074 - [20250519_005148.]: Samplename: Sample#3 + [20250519_010024.]: Samplename: Sample#3 Root: 58.871 --> Root in between the borders! Added to results. Hyperbolic solved: 13.3245830390029 - [20250519_005148.]: Samplename: Sample#4 + [20250519_010024.]: Samplename: Sample#4 Root: 13.325 --> Root in between the borders! Added to results. Hyperbolic solved: 26.9731029843141 - [20250519_005148.]: Samplename: Sample#5 + [20250519_010024.]: Samplename: Sample#5 Root: 26.973 --> Root in between the borders! Added to results. Hyperbolic solved: 30.9519756409676 - [20250519_005148.]: Samplename: Sample#6 + [20250519_010024.]: Samplename: Sample#6 Root: 30.952 --> Root in between the borders! Added to results. Hyperbolic solved: 45.7349250650843 - [20250519_005148.]: Samplename: Sample#7 + [20250519_010024.]: Samplename: Sample#7 Root: 45.735 --> Root in between the borders! Added to results. Hyperbolic solved: 84.5977214697661 - [20250519_005148.]: Samplename: Sample#8 + [20250519_010024.]: Samplename: Sample#8 Root: 84.598 --> Root in between the borders! Added to results. Hyperbolic solved: -2.87252278311384 - [20250519_005148.]: Samplename: Sample#9 + [20250519_010024.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.873 --> '-10 < root < 0' --> substitute 0 - [20250519_005148.]: Solving cubic regression for CpG#8 + [20250519_010024.]: Solving cubic regression for CpG#8 Coefficients: -55.3573333333333Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_005148.]: Samplename: Sample#1 + [20250519_010024.]: Samplename: Sample#1 Root: 74.173 --> Root in between the borders! Added to results. Coefficients: -17.574Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_005148.]: Samplename: Sample#10 + [20250519_010024.]: Samplename: Sample#10 Root: 30.7 --> Root in between the borders! Added to results. Coefficients: -22.9425714285714Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_005148.]: Samplename: Sample#2 + [20250519_010024.]: Samplename: Sample#2 Root: 38.106 --> Root in between the borders! Added to results. Coefficients: -42.849Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_005148.]: Samplename: Sample#3 + [20250519_010024.]: Samplename: Sample#3 Root: 61.571 --> Root in between the borders! Added to results. Coefficients: -4.604Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_005149.]: Samplename: Sample#4 + [20250519_010024.]: Samplename: Sample#4 Root: 9.446 --> Root in between the borders! Added to results. Coefficients: -11.389Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_005149.]: Samplename: Sample#5 + [20250519_010024.]: Samplename: Sample#5 Root: 21.295 --> Root in between the borders! Added to results. Coefficients: -25.784Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_005149.]: Samplename: Sample#6 + [20250519_010024.]: Samplename: Sample#6 Root: 41.8 --> Root in between the borders! Added to results. Coefficients: -30.746Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_005149.]: Samplename: Sample#7 + [20250519_010024.]: Samplename: Sample#7 Root: 47.937 --> Root in between the borders! Added to results. Coefficients: -66.912Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_005149.]: Samplename: Sample#8 + [20250519_010024.]: Samplename: Sample#8 Root: 84.816 --> Root in between the borders! Added to results. Coefficients: 3.176Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_005149.]: Samplename: Sample#9 + [20250519_010024.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -7.574 --> '-10 < root < 0' --> substitute 0 - [20250519_005149.]: Solving hyperbolic regression for CpG#9 + [20250519_010024.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 80.5447416574246 - [20250519_005149.]: Samplename: Sample#1 + [20250519_010024.]: Samplename: Sample#1 Root: 80.545 --> Root in between the borders! Added to results. Hyperbolic solved: 27.8054725532271 - [20250519_005149.]: Samplename: Sample#10 + [20250519_010024.]: Samplename: Sample#10 Root: 27.805 --> Root in between the borders! Added to results. Hyperbolic solved: 46.257978181877 - [20250519_005149.]: Samplename: Sample#2 + [20250519_010024.]: Samplename: Sample#2 Root: 46.258 --> Root in between the borders! Added to results. Hyperbolic solved: 57.1842723611193 - [20250519_005149.]: Samplename: Sample#3 + [20250519_010024.]: Samplename: Sample#3 Root: 57.184 --> Root in between the borders! Added to results. Hyperbolic solved: 8.63689944440251 - [20250519_005149.]: Samplename: Sample#4 + [20250519_010024.]: Samplename: Sample#4 Root: 8.637 --> Root in between the borders! Added to results. Hyperbolic solved: 24.2116725772085 - [20250519_005149.]: Samplename: Sample#5 + [20250519_010024.]: Samplename: Sample#5 Root: 24.212 --> Root in between the borders! Added to results. Hyperbolic solved: 39.6334888107959 - [20250519_005149.]: Samplename: Sample#6 + [20250519_010024.]: Samplename: Sample#6 Root: 39.633 --> Root in between the borders! Added to results. Hyperbolic solved: 44.3019478766814 - [20250519_005149.]: Samplename: Sample#7 + [20250519_010024.]: Samplename: Sample#7 Root: 44.302 --> Root in between the borders! Added to results. Hyperbolic solved: 87.3231552867261 - [20250519_005149.]: Samplename: Sample#8 + [20250519_010024.]: Samplename: Sample#8 Root: 87.323 --> Root in between the borders! Added to results. Hyperbolic solved: -1.1792994162273 - [20250519_005149.]: Samplename: Sample#9 + [20250519_010024.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -1.179 --> '-10 < root < 0' --> substitute 0 - [20250519_005149.]: Solving hyperbolic regression for row_means + [20250519_010024.]: Solving hyperbolic regression for row_means Hyperbolic solved: 76.7712420256811 - [20250519_005149.]: Samplename: Sample#1 + [20250519_010024.]: Samplename: Sample#1 Root: 76.771 --> Root in between the borders! Added to results. Hyperbolic solved: 28.8491793177963 - [20250519_005149.]: Samplename: Sample#10 + [20250519_010024.]: Samplename: Sample#10 Root: 28.849 --> Root in between the borders! Added to results. Hyperbolic solved: 43.0342096405004 - [20250519_005149.]: Samplename: Sample#2 + [20250519_010024.]: Samplename: Sample#2 Root: 43.034 --> Root in between the borders! Added to results. Hyperbolic solved: 57.634070615517 - [20250519_005149.]: Samplename: Sample#3 + [20250519_010024.]: Samplename: Sample#3 Root: 57.634 --> Root in between the borders! Added to results. Hyperbolic solved: 8.87182398849477 - [20250519_005149.]: Samplename: Sample#4 + [20250519_010024.]: Samplename: Sample#4 Root: 8.872 --> Root in between the borders! Added to results. Hyperbolic solved: 22.1989155573519 - [20250519_005149.]: Samplename: Sample#5 + [20250519_010024.]: Samplename: Sample#5 Root: 22.199 --> Root in between the borders! Added to results. Hyperbolic solved: 29.2140219298625 - [20250519_005149.]: Samplename: Sample#6 + [20250519_010024.]: Samplename: Sample#6 Root: 29.214 --> Root in between the borders! Added to results. Hyperbolic solved: 43.9371957594463 - [20250519_005149.]: Samplename: Sample#7 + [20250519_010024.]: Samplename: Sample#7 Root: 43.937 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6706924479325 - [20250519_005149.]: Samplename: Sample#8 + [20250519_010024.]: Samplename: Sample#8 Root: 85.671 --> Root in between the borders! Added to results. Hyperbolic solved: -0.55137023392917 - [20250519_005149.]: Samplename: Sample#9 + [20250519_010024.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.551 --> '-10 < root < 0' --> substitute 0 - [20250519_005149.]: Entered 'solving_equations'-Function - [20250519_005149.]: Solving hyperbolic regression for CpG#1 + [20250519_010024.]: Entered 'solving_equations'-Function + [20250519_010024.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 0 - [20250519_005149.]: Samplename: 0 + [20250519_010024.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 14.1480167109277 - [20250519_005149.]: Samplename: 12.5 + [20250519_010024.]: Samplename: 12.5 Root: 14.148 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1398443790446 - [20250519_005149.]: Samplename: 25 + [20250519_010024.]: Samplename: 25 Root: 26.14 --> Root in between the borders! Added to results. Hyperbolic solved: 39.3762039523382 - [20250519_005149.]: Samplename: 37.5 + [20250519_010024.]: Samplename: 37.5 Root: 39.376 --> Root in between the borders! Added to results. Hyperbolic solved: 52.9476244028783 - [20250519_005149.]: Samplename: 50 + [20250519_010024.]: Samplename: 50 Root: 52.948 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4195105873419 - [20250519_005149.]: Samplename: 62.5 + [20250519_010024.]: Samplename: 62.5 Root: 65.42 --> Root in between the borders! Added to results. Hyperbolic solved: 74.433837698331 - [20250519_005149.]: Samplename: 75 + [20250519_010024.]: Samplename: 75 Root: 74.434 --> Root in between the borders! Added to results. Hyperbolic solved: 80.5559264925808 - [20250519_005149.]: Samplename: 87.5 + [20250519_010024.]: Samplename: 87.5 Root: 80.556 --> Root in between the borders! Added to results. Hyperbolic solved: 100 - [20250519_005149.]: Samplename: 100 + [20250519_010024.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. - [20250519_005149.]: Solving hyperbolic regression for CpG#2 + [20250519_010024.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 0 - [20250519_005149.]: Samplename: 0 + [20250519_010024.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 10.7812303822548 - [20250519_005149.]: Samplename: 12.5 + [20250519_010024.]: Samplename: 12.5 Root: 10.781 --> Root in between the borders! Added to results. Hyperbolic solved: 26.0648314401535 - [20250519_005149.]: Samplename: 25 + [20250519_010024.]: Samplename: 25 Root: 26.065 --> Root in between the borders! Added to results. Hyperbolic solved: 35.1980950192158 - [20250519_005149.]: Samplename: 37.5 + [20250519_010024.]: Samplename: 37.5 Root: 35.198 --> Root in between the borders! Added to results. Hyperbolic solved: 47.9203836090837 - [20250519_005149.]: Samplename: 50 + [20250519_010024.]: Samplename: 50 Root: 47.92 --> Root in between the borders! Added to results. Hyperbolic solved: 67.275750545092 - [20250519_005149.]: Samplename: 62.5 + [20250519_010024.]: Samplename: 62.5 Root: 67.276 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7278143684244 - [20250519_005149.]: Samplename: 75 + [20250519_010024.]: Samplename: 75 Root: 75.728 --> Root in between the borders! Added to results. Hyperbolic solved: 84.1258428690749 - [20250519_005149.]: Samplename: 87.5 + [20250519_010024.]: Samplename: 87.5 Root: 84.126 --> Root in between the borders! Added to results. Hyperbolic solved: 100 - [20250519_005149.]: Samplename: 100 + [20250519_010024.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. - [20250519_005149.]: Solving hyperbolic regression for CpG#3 + [20250519_010024.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 0 - [20250519_005149.]: Samplename: 0 + [20250519_010024.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 10.8467198180169 - [20250519_005149.]: Samplename: 12.5 + [20250519_010024.]: Samplename: 12.5 Root: 10.847 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1449700612555 - [20250519_005149.]: Samplename: 25 + [20250519_010024.]: Samplename: 25 Root: 26.145 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2865760013773 - [20250519_005149.]: Samplename: 37.5 + [20250519_010024.]: Samplename: 37.5 Root: 37.287 --> Root in between the borders! Added to results. Hyperbolic solved: 51.4114078772697 - [20250519_005149.]: Samplename: 50 + [20250519_010024.]: Samplename: 50 Root: 51.411 --> Root in between the borders! Added to results. Hyperbolic solved: 65.0139634978039 - [20250519_005149.]: Samplename: 62.5 + [20250519_010024.]: Samplename: 62.5 Root: 65.014 --> Root in between the borders! Added to results. Hyperbolic solved: 76.9921394830877 - [20250519_005149.]: Samplename: 75 + [20250519_010024.]: Samplename: 75 Root: 76.992 --> Root in between the borders! Added to results. Hyperbolic solved: 79.6808818589171 - [20250519_005149.]: Samplename: 87.5 + [20250519_010024.]: Samplename: 87.5 Root: 79.681 --> Root in between the borders! Added to results. Hyperbolic solved: 100 - [20250519_005149.]: Samplename: 100 + [20250519_010024.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. - [20250519_005149.]: Solving hyperbolic regression for CpG#4 + [20250519_010024.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 0 - [20250519_005149.]: Samplename: 0 + [20250519_010024.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 13.2494913489925 - [20250519_005149.]: Samplename: 12.5 + [20250519_010024.]: Samplename: 12.5 Root: 13.249 --> Root in between the borders! Added to results. Hyperbolic solved: 25.0914701740206 - [20250519_005149.]: Samplename: 25 + [20250519_010024.]: Samplename: 25 Root: 25.091 --> Root in between the borders! Added to results. Hyperbolic solved: 38.8081740444332 - [20250519_005149.]: Samplename: 37.5 + [20250519_010024.]: Samplename: 37.5 Root: 38.808 --> Root in between the borders! Added to results. Hyperbolic solved: 49.1133034150896 - [20250519_005149.]: Samplename: 50 + [20250519_010024.]: Samplename: 50 Root: 49.113 --> Root in between the borders! Added to results. Hyperbolic solved: 67.573566028728 - [20250519_005149.]: Samplename: 62.5 + [20250519_010024.]: Samplename: 62.5 Root: 67.574 --> Root in between the borders! Added to results. Hyperbolic solved: 73.7655861480302 - [20250519_005149.]: Samplename: 75 + [20250519_010024.]: Samplename: 75 Root: 73.766 --> Root in between the borders! Added to results. Hyperbolic solved: 82.0404940898918 - [20250519_005149.]: Samplename: 87.5 + [20250519_010024.]: Samplename: 87.5 Root: 82.04 --> Root in between the borders! Added to results. Hyperbolic solved: 100 - [20250519_005149.]: Samplename: 100 + [20250519_010024.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. - [20250519_005149.]: Solving cubic regression for CpG#5 + [20250519_010024.]: Solving cubic regression for CpG#5 Coefficients: 0Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_005149.]: Samplename: 0 + [20250519_010024.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -4.144Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_005149.]: Samplename: 12.5 + [20250519_010024.]: Samplename: 12.5 Root: 10.457 --> Root in between the borders! Added to results. Coefficients: -12.102Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_005149.]: Samplename: 25 + [20250519_010024.]: Samplename: 25 Root: 26.323 --> Root in between the borders! Added to results. Coefficients: -20.536Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_005149.]: Samplename: 37.5 + [20250519_010024.]: Samplename: 37.5 Root: 39.937 --> Root in between the borders! Added to results. Coefficients: -30.0715Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_005149.]: Samplename: 50 + [20250519_010024.]: Samplename: 50 Root: 53.063 --> Root in between the borders! Added to results. Coefficients: -39.034Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_005149.]: Samplename: 62.5 + [20250519_010024.]: Samplename: 62.5 Root: 63.959 --> Root in between the borders! Added to results. Coefficients: -51.059Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_005149.]: Samplename: 75 + [20250519_010024.]: Samplename: 75 Root: 77.051 --> Root in between the borders! Added to results. Coefficients: -60.3906666666667Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_005149.]: Samplename: 87.5 + [20250519_010024.]: Samplename: 87.5 Root: 86.31 --> Root in between the borders! Added to results. Coefficients: -75.446Coefficients: 0.35446Coefficients: 0.004Coefficients: 0 - [20250519_005149.]: Samplename: 100 + [20250519_010024.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. - [20250519_005149.]: Solving hyperbolic regression for CpG#6 + [20250519_010024.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 0 - [20250519_005149.]: Samplename: 0 + [20250519_010024.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 11.8139244542506 - [20250519_005149.]: Samplename: 12.5 + [20250519_010024.]: Samplename: 12.5 Root: 11.814 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5330423964606 - [20250519_005149.]: Samplename: 25 + [20250519_010024.]: Samplename: 25 Root: 26.533 --> Root in between the borders! Added to results. Hyperbolic solved: 35.365455365759 - [20250519_005149.]: Samplename: 37.5 + [20250519_010024.]: Samplename: 37.5 Root: 35.365 --> Root in between the borders! Added to results. Hyperbolic solved: 50.1054345020203 - [20250519_005149.]: Samplename: 50 + [20250519_010024.]: Samplename: 50 Root: 50.105 --> Root in between the borders! Added to results. Hyperbolic solved: 64.9683746571925 - [20250519_005149.]: Samplename: 62.5 + [20250519_010024.]: Samplename: 62.5 Root: 64.968 --> Root in between the borders! Added to results. Hyperbolic solved: 73.6331726521876 - [20250519_005149.]: Samplename: 75 + [20250519_010024.]: Samplename: 75 Root: 73.633 --> Root in between the borders! Added to results. Hyperbolic solved: 86.9938602772804 - [20250519_005149.]: Samplename: 87.5 + [20250519_010024.]: Samplename: 87.5 Root: 86.994 --> Root in between the borders! Added to results. Hyperbolic solved: 100 - [20250519_005149.]: Samplename: 100 + [20250519_010024.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. - [20250519_005149.]: Solving hyperbolic regression for CpG#7 + [20250519_010024.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 0 - [20250519_005149.]: Samplename: 0 + [20250519_010024.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 11.7880491100164 - [20250519_005149.]: Samplename: 12.5 + [20250519_010024.]: Samplename: 12.5 Root: 11.788 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1959234034328 - [20250519_005149.]: Samplename: 25 + [20250519_010024.]: Samplename: 25 Root: 26.196 --> Root in between the borders! Added to results. Hyperbolic solved: 39.1978567589215 - [20250519_005149.]: Samplename: 37.5 + [20250519_010024.]: Samplename: 37.5 Root: 39.198 --> Root in between the borders! Added to results. Hyperbolic solved: 54.3513505291649 - [20250519_005149.]: Samplename: 50 + [20250519_010024.]: Samplename: 50 Root: 54.351 --> Root in between the borders! Added to results. Hyperbolic solved: 66.0567953704493 - [20250519_005149.]: Samplename: 62.5 + [20250519_010024.]: Samplename: 62.5 Root: 66.057 --> Root in between the borders! Added to results. Hyperbolic solved: 75.1900867269869 - [20250519_005149.]: Samplename: 75 + [20250519_010024.]: Samplename: 75 Root: 75.19 --> Root in between the borders! Added to results. Hyperbolic solved: 78.6051660349008 - [20250519_005149.]: Samplename: 87.5 + [20250519_010024.]: Samplename: 87.5 Root: 78.605 --> Root in between the borders! Added to results. Hyperbolic solved: 100 - [20250519_005149.]: Samplename: 100 + [20250519_010024.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. - [20250519_005149.]: Solving cubic regression for CpG#8 + [20250519_010024.]: Solving cubic regression for CpG#8 Coefficients: 0Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_005149.]: Samplename: 0 + [20250519_010024.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Coefficients: -4.35066666666667Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_005149.]: Samplename: 12.5 + [20250519_010024.]: Samplename: 12.5 Root: 8.961 --> Root in between the borders! Added to results. Coefficients: -15.834Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_005149.]: Samplename: 25 + [20250519_010024.]: Samplename: 25 Root: 28.16 --> Root in between the borders! Added to results. Coefficients: -22.254Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_005149.]: Samplename: 37.5 + [20250519_010024.]: Samplename: 37.5 Root: 37.189 --> Root in between the borders! Added to results. Coefficients: -36.529Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_005149.]: Samplename: 50 + [20250519_010024.]: Samplename: 50 Root: 54.661 --> Root in between the borders! Added to results. Coefficients: -47.73Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_005149.]: Samplename: 62.5 + [20250519_010024.]: Samplename: 62.5 Root: 66.642 --> Root in between the borders! Added to results. Coefficients: -60.5715Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_005149.]: Samplename: 75 + [20250519_010024.]: Samplename: 75 Root: 79.079 --> Root in between the borders! Added to results. Coefficients: -63.414Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_005149.]: Samplename: 87.5 + [20250519_010024.]: Samplename: 87.5 Root: 81.681 --> Root in between the borders! Added to results. Coefficients: -84.964Coefficients: 0.44964Coefficients: 0.004Coefficients: 0 - [20250519_005149.]: Samplename: 100 + [20250519_010024.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. - [20250519_005149.]: Solving hyperbolic regression for CpG#9 + [20250519_010024.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 0 - [20250519_005149.]: Samplename: 0 + [20250519_010025.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 12.2068234201026 - [20250519_005149.]: Samplename: 12.5 + [20250519_010025.]: Samplename: 12.5 Root: 12.207 --> Root in between the borders! Added to results. Hyperbolic solved: 28.0688737636578 - [20250519_005149.]: Samplename: 25 + [20250519_010025.]: Samplename: 25 Root: 28.069 --> Root in between the borders! Added to results. Hyperbolic solved: 37.6661823177513 - [20250519_005149.]: Samplename: 37.5 + [20250519_010025.]: Samplename: 37.5 Root: 37.666 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3684233460174 - [20250519_005149.]: Samplename: 50 + [20250519_010025.]: Samplename: 50 Root: 52.368 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8636916649907 - [20250519_005149.]: Samplename: 62.5 + [20250519_010025.]: Samplename: 62.5 Root: 64.864 --> Root in between the borders! Added to results. Hyperbolic solved: 74.2551330907062 - [20250519_005149.]: Samplename: 75 + [20250519_010025.]: Samplename: 75 Root: 74.255 --> Root in between the borders! Added to results. Hyperbolic solved: 83.9343288872042 - [20250519_005149.]: Samplename: 87.5 + [20250519_010025.]: Samplename: 87.5 Root: 83.934 --> Root in between the borders! Added to results. Hyperbolic solved: 100 - [20250519_005149.]: Samplename: 100 + [20250519_010025.]: Samplename: 100 Root: 100 --> Root in between the borders! Added to results. - [20250519_005149.]: Solving hyperbolic regression for row_means + [20250519_010025.]: Solving hyperbolic regression for row_means Hyperbolic solved: 0.000228160853766634 - [20250519_005149.]: Samplename: 0 + [20250519_010025.]: Samplename: 0 Root: 0 --> Root in between the borders! Added to results. Hyperbolic solved: 11.1587839347378 - [20250519_005149.]: Samplename: 12.5 + [20250519_010025.]: Samplename: 12.5 Root: 11.159 --> Root in between the borders! Added to results. Hyperbolic solved: 25.8570776700364 - [20250519_005149.]: Samplename: 25 + [20250519_010025.]: Samplename: 25 Root: 25.857 --> Root in between the borders! Added to results. Hyperbolic solved: 37.065004454281 - [20250519_005149.]: Samplename: 37.5 + [20250519_010025.]: Samplename: 37.5 Root: 37.065 --> Root in between the borders! Added to results. Hyperbolic solved: 51.1881763075298 - [20250519_005149.]: Samplename: 50 + [20250519_010025.]: Samplename: 50 Root: 51.188 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4439754555105 - [20250519_005149.]: Samplename: 62.5 + [20250519_010025.]: Samplename: 62.5 Root: 65.444 --> Root in between the borders! Added to results. Hyperbolic solved: 75.3005883283082 - [20250519_005149.]: Samplename: 75 + [20250519_010025.]: Samplename: 75 Root: 75.301 --> Root in between the borders! Added to results. Hyperbolic solved: 82.6591025877361 - [20250519_005149.]: Samplename: 87.5 + [20250519_010025.]: Samplename: 87.5 Root: 82.659 --> Root in between the borders! Added to results. Hyperbolic solved: 100.000153506599 - [20250519_005149.]: Samplename: 100 + [20250519_010025.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100 --> '100 < root < 110' --> substitute 100 - [20250519_005151.]: on_start: using future::plan("sequential") - [20250519_005151.]: Entered 'clean_dt'-Function - [20250519_005151.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_005151.]: got experimental data - [20250519_005151.]: Entered 'clean_dt'-Function - [20250519_005151.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_005151.]: got calibration data - [20250519_005151.]: + [20250519_010027.]: on_start: using future::plan("sequential") + [20250519_010027.]: Entered 'clean_dt'-Function + [20250519_010027.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_010027.]: got experimental data + [20250519_010027.]: Entered 'clean_dt'-Function + [20250519_010027.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_010027.]: got calibration data + [20250519_010027.]: ### Starting with regression calculations ### - [20250519_005151.]: Entered 'regression_type1'-Function + [20250519_010027.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : @@ -12368,141 +12368,141 @@ singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient - [20250519_005151.]: # CpG-site: CpG#1 + [20250519_010027.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_005151.]: Logging df_agg: CpG#1 - [20250519_005151.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005151.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005151.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005151.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005151.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_005151.]: Entered 'hyperbolic_regression'-Function - [20250519_005151.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005152.]: Entered 'cubic_regression'-Function - [20250519_005152.]: 'cubic_regression': minmax = FALSE - [20250519_005152.]: # CpG-site: CpG#2 + [20250519_010027.]: Logging df_agg: CpG#1 + [20250519_010027.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010027.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_010027.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_010027.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_010027.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) + [20250519_010027.]: Entered 'hyperbolic_regression'-Function + [20250519_010027.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010027.]: Entered 'cubic_regression'-Function + [20250519_010027.]: 'cubic_regression': minmax = FALSE + [20250519_010027.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_005152.]: Logging df_agg: CpG#2 - [20250519_005152.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005152.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005152.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005152.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005152.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_005152.]: Entered 'hyperbolic_regression'-Function - [20250519_005152.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005152.]: Entered 'cubic_regression'-Function - [20250519_005152.]: 'cubic_regression': minmax = FALSE - [20250519_005152.]: # CpG-site: CpG#3 + [20250519_010027.]: Logging df_agg: CpG#2 + [20250519_010027.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010027.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_010027.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_010027.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_010027.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) + [20250519_010027.]: Entered 'hyperbolic_regression'-Function + [20250519_010027.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010027.]: Entered 'cubic_regression'-Function + [20250519_010027.]: 'cubic_regression': minmax = FALSE + [20250519_010027.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_005152.]: Logging df_agg: CpG#3 - [20250519_005152.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005152.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005152.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005152.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005152.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_005152.]: Entered 'hyperbolic_regression'-Function - [20250519_005152.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005152.]: Entered 'cubic_regression'-Function - [20250519_005152.]: 'cubic_regression': minmax = FALSE - [20250519_005152.]: # CpG-site: CpG#4 + [20250519_010027.]: Logging df_agg: CpG#3 + [20250519_010027.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010027.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_010027.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_010027.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_010027.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) + [20250519_010027.]: Entered 'hyperbolic_regression'-Function + [20250519_010027.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010028.]: Entered 'cubic_regression'-Function + [20250519_010028.]: 'cubic_regression': minmax = FALSE + [20250519_010028.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_005152.]: Logging df_agg: CpG#4 - [20250519_005152.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005152.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005152.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005152.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005152.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_005152.]: Entered 'hyperbolic_regression'-Function - [20250519_005152.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005152.]: Entered 'cubic_regression'-Function - [20250519_005152.]: 'cubic_regression': minmax = FALSE - [20250519_005153.]: # CpG-site: CpG#5 + [20250519_010028.]: Logging df_agg: CpG#4 + [20250519_010028.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010028.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_010028.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_010028.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_010028.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) + [20250519_010028.]: Entered 'hyperbolic_regression'-Function + [20250519_010028.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010028.]: Entered 'cubic_regression'-Function + [20250519_010028.]: 'cubic_regression': minmax = FALSE + [20250519_010028.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_005153.]: Logging df_agg: CpG#5 - [20250519_005153.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005153.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005153.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005153.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005153.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_005153.]: Entered 'hyperbolic_regression'-Function - [20250519_005153.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005153.]: Entered 'cubic_regression'-Function - [20250519_005153.]: 'cubic_regression': minmax = FALSE - [20250519_005153.]: # CpG-site: CpG#6 + [20250519_010028.]: Logging df_agg: CpG#5 + [20250519_010028.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010028.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_010028.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_010028.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_010028.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) + [20250519_010028.]: Entered 'hyperbolic_regression'-Function + [20250519_010028.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010028.]: Entered 'cubic_regression'-Function + [20250519_010028.]: 'cubic_regression': minmax = FALSE + [20250519_010028.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_005153.]: Logging df_agg: CpG#6 - [20250519_005153.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005153.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005153.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005153.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005153.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_005153.]: Entered 'hyperbolic_regression'-Function - [20250519_005153.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005153.]: Entered 'cubic_regression'-Function - [20250519_005153.]: 'cubic_regression': minmax = FALSE - [20250519_005153.]: # CpG-site: CpG#7 + [20250519_010028.]: Logging df_agg: CpG#6 + [20250519_010028.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010028.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_010028.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_010028.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_010028.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) + [20250519_010028.]: Entered 'hyperbolic_regression'-Function + [20250519_010028.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010028.]: Entered 'cubic_regression'-Function + [20250519_010028.]: 'cubic_regression': minmax = FALSE + [20250519_010028.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_005153.]: Logging df_agg: CpG#7 - [20250519_005153.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005153.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005153.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005153.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005153.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_005153.]: Entered 'hyperbolic_regression'-Function - [20250519_005153.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005153.]: Entered 'cubic_regression'-Function - [20250519_005153.]: 'cubic_regression': minmax = FALSE - [20250519_005153.]: # CpG-site: CpG#8 + [20250519_010028.]: Logging df_agg: CpG#7 + [20250519_010028.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010028.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_010028.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_010028.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_010028.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) + [20250519_010028.]: Entered 'hyperbolic_regression'-Function + [20250519_010028.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010029.]: Entered 'cubic_regression'-Function + [20250519_010029.]: 'cubic_regression': minmax = FALSE + [20250519_010029.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_005153.]: Logging df_agg: CpG#8 - [20250519_005153.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005153.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005153.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005153.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005153.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_005153.]: Entered 'hyperbolic_regression'-Function - [20250519_005153.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005154.]: Entered 'cubic_regression'-Function - [20250519_005154.]: 'cubic_regression': minmax = FALSE - [20250519_005154.]: # CpG-site: CpG#9 + [20250519_010029.]: Logging df_agg: CpG#8 + [20250519_010029.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010029.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_010029.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_010029.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_010029.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) + [20250519_010029.]: Entered 'hyperbolic_regression'-Function + [20250519_010029.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010029.]: Entered 'cubic_regression'-Function + [20250519_010029.]: 'cubic_regression': minmax = FALSE + [20250519_010029.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_005154.]: Logging df_agg: CpG#9 - [20250519_005154.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005154.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005154.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005154.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005154.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_005154.]: Entered 'hyperbolic_regression'-Function - [20250519_005154.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005154.]: Entered 'cubic_regression'-Function - [20250519_005154.]: 'cubic_regression': minmax = FALSE - [20250519_005154.]: # CpG-site: row_means + [20250519_010029.]: Logging df_agg: CpG#9 + [20250519_010029.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010029.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_010029.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_010029.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_010029.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) + [20250519_010029.]: Entered 'hyperbolic_regression'-Function + [20250519_010029.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010029.]: Entered 'cubic_regression'-Function + [20250519_010029.]: 'cubic_regression': minmax = FALSE + [20250519_010029.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723122, 2.47760998435345)c(5.94911111111111, 0.84962962962963, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703704, 19.6995555555555, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_005154.]: Logging df_agg: row_means - [20250519_005154.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005154.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005154.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723122, 2.47760998435345)[20250519_005154.]: c(5.94911111111111, 0.84962962962963, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005154.]: c(NA, 6.79703703703704, 19.6995555555555, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_005154.]: Entered 'hyperbolic_regression'-Function - [20250519_005154.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005154.]: Entered 'cubic_regression'-Function - [20250519_005154.]: 'cubic_regression': minmax = FALSE - [20250519_005155.]: ### Starting with plotting ### - [20250519_005155.]: Creating plot No. 1 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG1.png - [20250519_005155.]: # CpG-site: CpG#1 + [20250519_010029.]: Logging df_agg: row_means + [20250519_010029.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010029.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_010029.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723122, 2.47760998435345)[20250519_010029.]: c(5.94911111111111, 0.84962962962963, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_010029.]: c(NA, 6.79703703703704, 19.6995555555555, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) + [20250519_010029.]: Entered 'hyperbolic_regression'-Function + [20250519_010029.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010030.]: Entered 'cubic_regression'-Function + [20250519_010030.]: 'cubic_regression': minmax = FALSE + [20250519_010030.]: ### Starting with plotting ### + [20250519_010030.]: Creating plot No. 1 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG1.png + [20250519_010030.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -937.719419567728 , a = -108.56796364622 , b = -937.719419567728 , d = -232.057111323599 - [20250519_005155.]: # CpG-site: CpG#1 + [20250519_010030.]: # CpG-site: CpG#1 Cubic: Using a = 6.53413423120091e-05 , b = -0.0055806968734969 , c = 0.784061853455188 , d = 1.93182659932656 - [20250519_005158.]: Creating plot No. 2 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG2.png - [20250519_005158.]: # CpG-site: CpG#2 + [20250519_010033.]: Creating plot No. 2 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG2.png + [20250519_010033.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = -2393.1426335766 , a = -141.862264327279 , b = -2393.1426335766 , d = -268.331583599043 - [20250519_005158.]: # CpG-site: CpG#2 + [20250519_010033.]: # CpG-site: CpG#2 Cubic: Using a = 8.04237934904601e-06 , b = 0.00293158941798942 , c = 0.514821742825076 , d = 9.27667003367003 - [20250519_005200.]: Creating plot No. 3 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG3.png - [20250519_005200.]: # CpG-site: CpG#3 + [20250519_010035.]: Creating plot No. 3 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG3.png + [20250519_010035.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = -1477.46855437885 , a = -170.424507883158 , b = -1477.46855437885 , d = -327.190157171162 - [20250519_005200.]: # CpG-site: CpG#3 + [20250519_010035.]: # CpG-site: CpG#3 Cubic: Using a = 2.30555869809204e-05 , b = -0.000797009331409346 , c = 0.62783129228796 , d = 3.88705218855218 - [20250519_005202.]: Creating plot No. 4 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG4.png - [20250519_005202.]: # CpG-site: CpG#4 + [20250519_010038.]: Creating plot No. 4 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG4.png + [20250519_010038.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = -2115.87792930847 , a = -162.169768036114 , b = -2115.87792930847 , d = -299.662268971294 - [20250519_005202.]: # CpG-site: CpG#4 + [20250519_010038.]: # CpG-site: CpG#4 Cubic: Using a = 3.2166356902357e-05 , b = -0.0015913142857143 , c = 0.697398364598366 , d = 6.17055050505048 - [20250519_005204.]: Creating plot No. 5 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG5.png - [20250519_005204.]: # CpG-site: CpG#5 + [20250519_010040.]: Creating plot No. 5 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG5.png + [20250519_010040.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = -1383.32738741788 , a = -151.440067063617 , b = -1383.32738741788 , d = -304.200253800186 - [20250519_005204.]: # CpG-site: CpG#5 + [20250519_010040.]: # CpG-site: CpG#5 Cubic: Using a = -4.48459708193036e-06 , b = 0.00375371236171235 , c = 0.422446384479718 , d = 5.17203872053872 - [20250519_005207.]: Creating plot No. 6 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG6.png - [20250519_005207.]: # CpG-site: CpG#6 + [20250519_010042.]: Creating plot No. 6 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG6.png + [20250519_010042.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = -1550.59307603208 , a = -125.016146072952 , b = -1550.59307603208 , d = -247.279679418643 - [20250519_005207.]: # CpG-site: CpG#6 + [20250519_010042.]: # CpG-site: CpG#6 Cubic: Using a = 2.38852884399552e-05 , b = 0.000918637037037021 , c = 0.561022132435467 , d = 6.1479276094276 - [20250519_005209.]: Creating plot No. 7 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG7.png - [20250519_005209.]: # CpG-site: CpG#7 + [20250519_010044.]: Creating plot No. 7 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG7.png + [20250519_010044.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -772.844192345493 , a = -75.2724875322984 , b = -772.844192345493 , d = -226.673147125363 - [20250519_005209.]: # CpG-site: CpG#7 + [20250519_010044.]: # CpG-site: CpG#7 Cubic: Using a = 4.98010505050505e-05 , b = -0.00436152150072151 , c = 0.579588917748918 , d = 1.72104545454544 - [20250519_005211.]: Creating plot No. 8 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG8.png - [20250519_005211.]: # CpG-site: CpG#8 + [20250519_010046.]: Creating plot No. 8 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG8.png + [20250519_010046.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = -3872.73616547768 , a = -278.017275815885 , b = -3872.73616547768 , d = -438.894268920994 - [20250519_005211.]: # CpG-site: CpG#8 + [20250519_010046.]: # CpG-site: CpG#8 Cubic: Using a = -1.50060965207632e-05 , b = 0.00447978143338143 , c = 0.534897737694404 , d = 9.56981481481482 - [20250519_005213.]: Creating plot No. 9 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG9.png - [20250519_005213.]: # CpG-site: CpG#9 + [20250519_010048.]: Creating plot No. 9 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG9.png + [20250519_010048.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -843.925451494751 , a = -70.6796095478879 , b = -843.925451494751 , d = -184.482153986057 - [20250519_005213.]: # CpG-site: CpG#9 + [20250519_010048.]: # CpG-site: CpG#9 Cubic: Using a = 7.72300426487093e-05 , b = -0.00542281173641174 , c = 0.67899229597563 , d = 2.72413468013467 - [20250519_005215.]: Creating plot No. 10 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_rowmeans.png - [20250519_005215.]: # CpG-site: row_means + [20250519_010051.]: Creating plot No. 10 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_rowmeans.png + [20250519_010051.]: # CpG-site: row_means Hyperbolic: Using bias_weight = -1525.75758254501 , a = -127.67501720702 , b = -1525.75758254501 , d = -263.003030149428 - [20250519_005215.]: # CpG-site: row_means + [20250519_010051.]: # CpG-site: row_means Cubic: Using a = 2.88923726150392e-05 , b = -0.0006299592752926 , c = 0.600117857944524 , d = 5.17789562289563 - [20250519_005218.]: Entered 'solving_equations'-Function - [20250519_005218.]: Solving hyperbolic regression for CpG#1 + [20250519_010053.]: Entered 'solving_equations'-Function + [20250519_010053.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: -2.23222990163966 - [20250519_005218.]: Samplename: 0 + [20250519_010053.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. @@ -12510,126 +12510,126 @@ Root: -2.232 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.1698489850618 - [20250519_005218.]: Samplename: 12.5 + [20250519_010053.]: Samplename: 12.5 Root: 12.17 --> Root in between the borders! Added to results. Hyperbolic solved: 24.4781920312644 - [20250519_005218.]: Samplename: 25 + [20250519_010053.]: Samplename: 25 Root: 24.478 --> Root in between the borders! Added to results. Hyperbolic solved: 38.173044740918 - [20250519_005218.]: Samplename: 37.5 + [20250519_010053.]: Samplename: 37.5 Root: 38.173 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3349371964438 - [20250519_005218.]: Samplename: 50 + [20250519_010053.]: Samplename: 50 Root: 52.335 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4582773627666 - [20250519_005218.]: Samplename: 62.5 + [20250519_010053.]: Samplename: 62.5 Root: 65.458 --> Root in between the borders! Added to results. Hyperbolic solved: 75.0090795260796 - [20250519_005218.]: Samplename: 75 + [20250519_010053.]: Samplename: 75 Root: 75.009 --> Root in between the borders! Added to results. Hyperbolic solved: 81.5271920968417 - [20250519_005218.]: Samplename: 87.5 + [20250519_010053.]: Samplename: 87.5 Root: 81.527 --> Root in between the borders! Added to results. Hyperbolic solved: 102.400893095062 - [20250519_005218.]: Samplename: 100 + [20250519_010053.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.401 --> '100 < root < 110' --> substitute 100 - [20250519_005218.]: Solving hyperbolic regression for CpG#2 + [20250519_010053.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 1.13660501904968 - [20250519_005218.]: Samplename: 0 + [20250519_010053.]: Samplename: 0 Root: 1.137 --> Root in between the borders! Added to results. Hyperbolic solved: 11.4129696733689 - [20250519_005218.]: Samplename: 12.5 + [20250519_010053.]: Samplename: 12.5 Root: 11.413 --> Root in between the borders! Added to results. Hyperbolic solved: 26.174000526428 - [20250519_005218.]: Samplename: 25 + [20250519_010053.]: Samplename: 25 Root: 26.174 --> Root in between the borders! Added to results. Hyperbolic solved: 35.1050449117028 - [20250519_005218.]: Samplename: 37.5 + [20250519_010053.]: Samplename: 37.5 Root: 35.105 --> Root in between the borders! Added to results. Hyperbolic solved: 47.685500330611 - [20250519_005218.]: Samplename: 50 + [20250519_010053.]: Samplename: 50 Root: 47.686 --> Root in between the borders! Added to results. Hyperbolic solved: 67.1440494417104 - [20250519_005218.]: Samplename: 62.5 + [20250519_010053.]: Samplename: 62.5 Root: 67.144 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7644668894086 - [20250519_005218.]: Samplename: 75 + [20250519_010053.]: Samplename: 75 Root: 75.764 --> Root in between the borders! Added to results. Hyperbolic solved: 84.4054158616395 - [20250519_005218.]: Samplename: 87.5 + [20250519_010053.]: Samplename: 87.5 Root: 84.405 --> Root in between the borders! Added to results. Hyperbolic solved: 100.94827248399 - [20250519_005218.]: Samplename: 100 + [20250519_010053.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.948 --> '100 < root < 110' --> substitute 100 - [20250519_005218.]: Solving hyperbolic regression for CpG#3 + [20250519_010053.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 0.51235653688495 - [20250519_005218.]: Samplename: 0 + [20250519_010053.]: Samplename: 0 Root: 0.512 --> Root in between the borders! Added to results. Hyperbolic solved: 10.7523884294604 - [20250519_005218.]: Samplename: 12.5 + [20250519_010053.]: Samplename: 12.5 Root: 10.752 --> Root in between the borders! Added to results. Hyperbolic solved: 25.5218907947761 - [20250519_005218.]: Samplename: 25 + [20250519_010053.]: Samplename: 25 Root: 25.522 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5270462675211 - [20250519_005218.]: Samplename: 37.5 + [20250519_010053.]: Samplename: 37.5 Root: 36.527 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7909245028224 - [20250519_005218.]: Samplename: 50 + [20250519_010053.]: Samplename: 50 Root: 50.791 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8686317550184 - [20250519_005218.]: Samplename: 62.5 + [20250519_010053.]: Samplename: 62.5 Root: 64.869 --> Root in between the borders! Added to results. Hyperbolic solved: 77.5524188495235 - [20250519_005218.]: Samplename: 75 + [20250519_010053.]: Samplename: 75 Root: 77.552 --> Root in between the borders! Added to results. Hyperbolic solved: 80.4374617358174 - [20250519_005218.]: Samplename: 87.5 + [20250519_010053.]: Samplename: 87.5 Root: 80.437 --> Root in between the borders! Added to results. Hyperbolic solved: 102.704024900825 - [20250519_005218.]: Samplename: 100 + [20250519_010053.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.704 --> '100 < root < 110' --> substitute 100 - [20250519_005218.]: Solving hyperbolic regression for CpG#4 + [20250519_010053.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: -0.519503092357606 - [20250519_005218.]: Samplename: 0 + [20250519_010053.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. @@ -12637,126 +12637,126 @@ Root: -0.52 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.4934147844872 - [20250519_005218.]: Samplename: 12.5 + [20250519_010053.]: Samplename: 12.5 Root: 12.493 --> Root in between the borders! Added to results. Hyperbolic solved: 24.2685420024115 - [20250519_005218.]: Samplename: 25 + [20250519_010053.]: Samplename: 25 Root: 24.269 --> Root in between the borders! Added to results. Hyperbolic solved: 38.0817128465023 - [20250519_005218.]: Samplename: 37.5 + [20250519_010053.]: Samplename: 37.5 Root: 38.082 --> Root in between the borders! Added to results. Hyperbolic solved: 48.5843181174811 - [20250519_005218.]: Samplename: 50 + [20250519_010053.]: Samplename: 50 Root: 48.584 --> Root in between the borders! Added to results. Hyperbolic solved: 67.6722399183037 - [20250519_005218.]: Samplename: 62.5 + [20250519_010053.]: Samplename: 62.5 Root: 67.672 --> Root in between the borders! Added to results. Hyperbolic solved: 74.1549277799119 - [20250519_005218.]: Samplename: 75 + [20250519_010053.]: Samplename: 75 Root: 74.155 --> Root in between the borders! Added to results. Hyperbolic solved: 82.8821797890026 - [20250519_005218.]: Samplename: 87.5 + [20250519_010053.]: Samplename: 87.5 Root: 82.882 --> Root in between the borders! Added to results. Hyperbolic solved: 102.0791269023 - [20250519_005218.]: Samplename: 100 + [20250519_010053.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.079 --> '100 < root < 110' --> substitute 100 - [20250519_005218.]: Solving hyperbolic regression for CpG#5 + [20250519_010053.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 2.41558626275183 - [20250519_005218.]: Samplename: 0 + [20250519_010053.]: Samplename: 0 Root: 2.416 --> Root in between the borders! Added to results. Hyperbolic solved: 10.1649674907454 - [20250519_005218.]: Samplename: 12.5 + [20250519_010053.]: Samplename: 12.5 Root: 10.165 --> Root in between the borders! Added to results. Hyperbolic solved: 23.9830820412762 - [20250519_005218.]: Samplename: 25 + [20250519_010053.]: Samplename: 25 Root: 23.983 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2773619900429 - [20250519_005218.]: Samplename: 37.5 + [20250519_010053.]: Samplename: 37.5 Root: 37.277 --> Root in between the borders! Added to results. Hyperbolic solved: 50.8659386543864 - [20250519_005218.]: Samplename: 50 + [20250519_010053.]: Samplename: 50 Root: 50.866 --> Root in between the borders! Added to results. Hyperbolic solved: 62.4342273571069 - [20250519_005218.]: Samplename: 62.5 + [20250519_010053.]: Samplename: 62.5 Root: 62.434 --> Root in between the borders! Added to results. Hyperbolic solved: 76.3915260534323 - [20250519_005218.]: Samplename: 75 + [20250519_010053.]: Samplename: 75 Root: 76.392 --> Root in between the borders! Added to results. Hyperbolic solved: 86.159788778566 - [20250519_005218.]: Samplename: 87.5 + [20250519_010053.]: Samplename: 87.5 Root: 86.16 --> Root in between the borders! Added to results. Hyperbolic solved: 100.267759893323 - [20250519_005218.]: Samplename: 100 + [20250519_010053.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.268 --> '100 < root < 110' --> substitute 100 - [20250519_005218.]: Solving hyperbolic regression for CpG#6 + [20250519_010053.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 0.138163748613034 - [20250519_005218.]: Samplename: 0 + [20250519_010053.]: Samplename: 0 Root: 0.138 --> Root in between the borders! Added to results. Hyperbolic solved: 11.8635558881981 - [20250519_005218.]: Samplename: 12.5 + [20250519_010053.]: Samplename: 12.5 Root: 11.864 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5107449550797 - [20250519_005218.]: Samplename: 25 + [20250519_010053.]: Samplename: 25 Root: 26.511 --> Root in between the borders! Added to results. Hyperbolic solved: 35.3205073050661 - [20250519_005218.]: Samplename: 37.5 + [20250519_010053.]: Samplename: 37.5 Root: 35.321 --> Root in between the borders! Added to results. Hyperbolic solved: 50.0570767570666 - [20250519_005218.]: Samplename: 50 + [20250519_010053.]: Samplename: 50 Root: 50.057 --> Root in between the borders! Added to results. Hyperbolic solved: 64.9602944381018 - [20250519_005218.]: Samplename: 62.5 + [20250519_010053.]: Samplename: 62.5 Root: 64.96 --> Root in between the borders! Added to results. Hyperbolic solved: 73.66890571617 - [20250519_005218.]: Samplename: 75 + [20250519_010053.]: Samplename: 75 Root: 73.669 --> Root in between the borders! Added to results. Hyperbolic solved: 87.1266086585036 - [20250519_005218.]: Samplename: 87.5 + [20250519_010053.]: Samplename: 87.5 Root: 87.127 --> Root in between the borders! Added to results. Hyperbolic solved: 100.261637014212 - [20250519_005218.]: Samplename: 100 + [20250519_010053.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.262 --> '100 < root < 110' --> substitute 100 - [20250519_005218.]: Solving hyperbolic regression for CpG#7 + [20250519_010053.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: -1.37238087287012 - [20250519_005218.]: Samplename: 0 + [20250519_010053.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. @@ -12764,85 +12764,85 @@ Root: -1.372 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.1993162352498 - [20250519_005218.]: Samplename: 12.5 + [20250519_010053.]: Samplename: 12.5 Root: 10.199 --> Root in between the borders! Added to results. Hyperbolic solved: 24.595178967123 - [20250519_005218.]: Samplename: 25 + [20250519_010053.]: Samplename: 25 Root: 24.595 --> Root in between the borders! Added to results. Hyperbolic solved: 37.8310421041787 - [20250519_005218.]: Samplename: 37.5 + [20250519_010053.]: Samplename: 37.5 Root: 37.831 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5588739724067 - [20250519_005218.]: Samplename: 50 + [20250519_010053.]: Samplename: 50 Root: 53.559 --> Root in between the borders! Added to results. Hyperbolic solved: 65.9364947980258 - [20250519_005218.]: Samplename: 62.5 + [20250519_010053.]: Samplename: 62.5 Root: 65.936 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7361094434913 - [20250519_005218.]: Samplename: 75 + [20250519_010053.]: Samplename: 75 Root: 75.736 --> Root in between the borders! Added to results. Hyperbolic solved: 79.432823759854 - [20250519_005218.]: Samplename: 87.5 + [20250519_010053.]: Samplename: 87.5 Root: 79.433 --> Root in between the borders! Added to results. Hyperbolic solved: 103.004237013737 - [20250519_005218.]: Samplename: 100 + [20250519_010053.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 103.004 --> '100 < root < 110' --> substitute 100 - [20250519_005218.]: Solving hyperbolic regression for CpG#8 + [20250519_010053.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 2.80068218205093 - [20250519_005218.]: Samplename: 0 + [20250519_010053.]: Samplename: 0 Root: 2.801 --> Root in between the borders! Added to results. Hyperbolic solved: 9.27535134596596 - [20250519_005218.]: Samplename: 12.5 + [20250519_010053.]: Samplename: 12.5 Root: 9.275 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4762621928197 - [20250519_005218.]: Samplename: 25 + [20250519_010053.]: Samplename: 25 Root: 25.476 --> Root in between the borders! Added to results. Hyperbolic solved: 34.0122075735416 - [20250519_005218.]: Samplename: 37.5 + [20250519_010053.]: Samplename: 37.5 Root: 34.012 --> Root in between the borders! Added to results. Hyperbolic solved: 51.7842655662325 - [20250519_005218.]: Samplename: 50 + [20250519_010053.]: Samplename: 50 Root: 51.784 --> Root in between the borders! Added to results. Hyperbolic solved: 64.6732311906145 - [20250519_005218.]: Samplename: 62.5 + [20250519_010053.]: Samplename: 62.5 Root: 64.673 --> Root in between the borders! Added to results. Hyperbolic solved: 78.4326978859189 - [20250519_005218.]: Samplename: 75 + [20250519_010053.]: Samplename: 75 Root: 78.433 --> Root in between the borders! Added to results. Hyperbolic solved: 81.3427232852719 - [20250519_005218.]: Samplename: 87.5 + [20250519_010053.]: Samplename: 87.5 Root: 81.343 --> Root in between the borders! Added to results. Hyperbolic solved: 101.964406640583 - [20250519_005218.]: Samplename: 100 + [20250519_010053.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.964 --> '100 < root < 110' --> substitute 100 - [20250519_005218.]: Solving hyperbolic regression for CpG#9 + [20250519_010053.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: -2.13403721845678 - [20250519_005218.]: Samplename: 0 + [20250519_010053.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. @@ -12850,85 +12850,85 @@ Root: -2.134 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.5082192457956 - [20250519_005218.]: Samplename: 12.5 + [20250519_010053.]: Samplename: 12.5 Root: 10.508 --> Root in between the borders! Added to results. Hyperbolic solved: 26.9164567253388 - [20250519_005218.]: Samplename: 25 + [20250519_010053.]: Samplename: 25 Root: 26.916 --> Root in between the borders! Added to results. Hyperbolic solved: 36.8334779159501 - [20250519_005218.]: Samplename: 37.5 + [20250519_010053.]: Samplename: 37.5 Root: 36.833 --> Root in between the borders! Added to results. Hyperbolic solved: 52.0097895977263 - [20250519_005218.]: Samplename: 50 + [20250519_010053.]: Samplename: 50 Root: 52.01 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8930527921581 - [20250519_005218.]: Samplename: 62.5 + [20250519_010053.]: Samplename: 62.5 Root: 64.893 --> Root in between the borders! Added to results. Hyperbolic solved: 74.5671055499357 - [20250519_005218.]: Samplename: 75 + [20250519_010053.]: Samplename: 75 Root: 74.567 --> Root in between the borders! Added to results. Hyperbolic solved: 84.5294954832669 - [20250519_005218.]: Samplename: 87.5 + [20250519_010053.]: Samplename: 87.5 Root: 84.529 --> Root in between the borders! Added to results. Hyperbolic solved: 101.047146466811 - [20250519_005218.]: Samplename: 100 + [20250519_010053.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.047 --> '100 < root < 110' --> substitute 100 - [20250519_005218.]: Solving hyperbolic regression for row_means + [20250519_010053.]: Solving hyperbolic regression for row_means Hyperbolic solved: 0.290940467580009 - [20250519_005218.]: Samplename: 0 + [20250519_010053.]: Samplename: 0 Root: 0.291 --> Root in between the borders! Added to results. Hyperbolic solved: 11.041240609297 - [20250519_005218.]: Samplename: 12.5 + [20250519_010053.]: Samplename: 12.5 Root: 11.041 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4081503210647 - [20250519_005218.]: Samplename: 25 + [20250519_010053.]: Samplename: 25 Root: 25.408 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5243723188613 - [20250519_005218.]: Samplename: 37.5 + [20250519_010053.]: Samplename: 37.5 Root: 36.524 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7348829528732 - [20250519_005218.]: Samplename: 50 + [20250519_010053.]: Samplename: 50 Root: 50.735 --> Root in between the borders! Added to results. Hyperbolic solved: 65.3135214250794 - [20250519_005218.]: Samplename: 62.5 + [20250519_010053.]: Samplename: 62.5 Root: 65.314 --> Root in between the borders! Added to results. Hyperbolic solved: 75.5342711952113 - [20250519_005218.]: Samplename: 75 + [20250519_010053.]: Samplename: 75 Root: 75.534 --> Root in between the borders! Added to results. Hyperbolic solved: 83.2411229564887 - [20250519_005218.]: Samplename: 87.5 + [20250519_010053.]: Samplename: 87.5 Root: 83.241 --> Root in between the borders! Added to results. Hyperbolic solved: 101.666942268285 - [20250519_005218.]: Samplename: 100 + [20250519_010053.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.667 --> '100 < root < 110' --> substitute 100 - [20250519_005218.]: + [20250519_010053.]: ### Starting with regression calculations ### - [20250519_005218.]: Entered 'regression_type1'-Function + [20250519_010053.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : @@ -13413,561 +13413,561 @@ singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient - [20250519_005218.]: # CpG-site: CpG#1 + [20250519_010053.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.1698489850618, 24.4781920312644, 38.173044740918, 52.3349371964438, 65.4582773627666, 75.0090795260796, 81.5271920968417, 100)c(0, 0.330151014938201, 0.521807968735601, 0.673044740918002, 2.3349371964438, 2.95827736276659, 0.00907952607960283, 5.9728079031583, 0)c(NA, 2.64120811950561, 2.0872318749424, 1.79478597578134, 4.66987439288761, 4.73324378042655, 0.0121060347728038, 6.82606617503806, 0) - [20250519_005218.]: Logging df_agg: CpG#1 - [20250519_005218.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005218.]: c(0, 12.1698489850618, 24.4781920312644, 38.173044740918, 52.3349371964438, 65.4582773627666, 75.0090795260796, 81.5271920968417, 100)[20250519_005218.]: c(0, 0.330151014938201, 0.521807968735601, 0.673044740918002, 2.3349371964438, 2.95827736276659, 0.00907952607960283, 5.9728079031583, 0)[20250519_005218.]: c(NA, 2.64120811950561, 2.0872318749424, 1.79478597578134, 4.66987439288761, 4.73324378042655, 0.0121060347728038, 6.82606617503806, 0) - [20250519_005218.]: Entered 'hyperbolic_regression'-Function - [20250519_005218.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005218.]: Entered 'cubic_regression'-Function - [20250519_005218.]: 'cubic_regression': minmax = FALSE - [20250519_005218.]: # CpG-site: CpG#2 + [20250519_010053.]: Logging df_agg: CpG#1 + [20250519_010053.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010053.]: c(0, 12.1698489850618, 24.4781920312644, 38.173044740918, 52.3349371964438, 65.4582773627666, 75.0090795260796, 81.5271920968417, 100)[20250519_010053.]: c(0, 0.330151014938201, 0.521807968735601, 0.673044740918002, 2.3349371964438, 2.95827736276659, 0.00907952607960283, 5.9728079031583, 0)[20250519_010053.]: c(NA, 2.64120811950561, 2.0872318749424, 1.79478597578134, 4.66987439288761, 4.73324378042655, 0.0121060347728038, 6.82606617503806, 0) + [20250519_010053.]: Entered 'hyperbolic_regression'-Function + [20250519_010053.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010054.]: Entered 'cubic_regression'-Function + [20250519_010054.]: 'cubic_regression': minmax = FALSE + [20250519_010054.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.13660501904968, 11.4129696733689, 26.174000526428, 35.1050449117028, 47.685500330611, 67.1440494417104, 75.7644668894086, 84.4054158616395, 100)c(1.13660501904968, 1.0870303266311, 1.174000526428, 2.3949550882972, 2.314499669389, 4.64404944171039, 0.764466889408595, 3.0945841383605, 0)c(NA, 8.6962426130488, 4.696002105712, 6.38654690212587, 4.628999338778, 7.43047910673663, 1.01928918587813, 3.53666758669772, 0) - [20250519_005218.]: Logging df_agg: CpG#2 - [20250519_005218.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005218.]: c(1.13660501904968, 11.4129696733689, 26.174000526428, 35.1050449117028, 47.685500330611, 67.1440494417104, 75.7644668894086, 84.4054158616395, 100)[20250519_005218.]: c(1.13660501904968, 1.0870303266311, 1.174000526428, 2.3949550882972, 2.314499669389, 4.64404944171039, 0.764466889408595, 3.0945841383605, 0)[20250519_005218.]: c(NA, 8.6962426130488, 4.696002105712, 6.38654690212587, 4.628999338778, 7.43047910673663, 1.01928918587813, 3.53666758669772, 0) - [20250519_005218.]: Entered 'hyperbolic_regression'-Function - [20250519_005218.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005219.]: Entered 'cubic_regression'-Function - [20250519_005219.]: 'cubic_regression': minmax = FALSE - [20250519_005219.]: # CpG-site: CpG#3 + [20250519_010054.]: Logging df_agg: CpG#2 + [20250519_010054.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010054.]: c(1.13660501904968, 11.4129696733689, 26.174000526428, 35.1050449117028, 47.685500330611, 67.1440494417104, 75.7644668894086, 84.4054158616395, 100)[20250519_010054.]: c(1.13660501904968, 1.0870303266311, 1.174000526428, 2.3949550882972, 2.314499669389, 4.64404944171039, 0.764466889408595, 3.0945841383605, 0)[20250519_010054.]: c(NA, 8.6962426130488, 4.696002105712, 6.38654690212587, 4.628999338778, 7.43047910673663, 1.01928918587813, 3.53666758669772, 0) + [20250519_010054.]: Entered 'hyperbolic_regression'-Function + [20250519_010054.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010054.]: Entered 'cubic_regression'-Function + [20250519_010054.]: 'cubic_regression': minmax = FALSE + [20250519_010054.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.51235653688495, 10.7523884294604, 25.5218907947761, 36.5270462675211, 50.7909245028224, 64.8686317550184, 77.5524188495235, 80.4374617358174, 100)c(0.51235653688495, 1.7476115705396, 0.521890794776098, 0.972953732478899, 0.790924502822399, 2.3686317550184, 2.55241884952351, 7.06253826418261, 0)c(NA, 13.9808925643168, 2.08756317910439, 2.5945432866104, 1.5818490056448, 3.78981080802944, 3.40322513269801, 8.07147230192298, 0) - [20250519_005219.]: Logging df_agg: CpG#3 - [20250519_005219.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005219.]: c(0.51235653688495, 10.7523884294604, 25.5218907947761, 36.5270462675211, 50.7909245028224, 64.8686317550184, 77.5524188495235, 80.4374617358174, 100)[20250519_005219.]: c(0.51235653688495, 1.7476115705396, 0.521890794776098, 0.972953732478899, 0.790924502822399, 2.3686317550184, 2.55241884952351, 7.06253826418261, 0)[20250519_005219.]: c(NA, 13.9808925643168, 2.08756317910439, 2.5945432866104, 1.5818490056448, 3.78981080802944, 3.40322513269801, 8.07147230192298, 0) - [20250519_005219.]: Entered 'hyperbolic_regression'-Function - [20250519_005219.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005219.]: Entered 'cubic_regression'-Function - [20250519_005219.]: 'cubic_regression': minmax = FALSE - [20250519_005219.]: # CpG-site: CpG#4 + [20250519_010054.]: Logging df_agg: CpG#3 + [20250519_010054.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010054.]: c(0.51235653688495, 10.7523884294604, 25.5218907947761, 36.5270462675211, 50.7909245028224, 64.8686317550184, 77.5524188495235, 80.4374617358174, 100)[20250519_010054.]: c(0.51235653688495, 1.7476115705396, 0.521890794776098, 0.972953732478899, 0.790924502822399, 2.3686317550184, 2.55241884952351, 7.06253826418261, 0)[20250519_010054.]: c(NA, 13.9808925643168, 2.08756317910439, 2.5945432866104, 1.5818490056448, 3.78981080802944, 3.40322513269801, 8.07147230192298, 0) + [20250519_010054.]: Entered 'hyperbolic_regression'-Function + [20250519_010054.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010054.]: Entered 'cubic_regression'-Function + [20250519_010054.]: 'cubic_regression': minmax = FALSE + [20250519_010054.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.4934147844872, 24.2685420024115, 38.0817128465023, 48.5843181174811, 67.6722399183037, 74.1549277799119, 82.8821797890026, 100)c(0, 0.00658521551279989, 0.7314579975885, 0.581712846502299, 1.4156818825189, 5.1722399183037, 0.845072220088099, 4.61782021099739, 0)c(NA, 0.0526817241023991, 2.925831990354, 1.55123425733946, 2.83136376503781, 8.27558386928592, 1.12676296011747, 5.27750881256845, 0) - [20250519_005219.]: Logging df_agg: CpG#4 - [20250519_005219.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005219.]: c(0, 12.4934147844872, 24.2685420024115, 38.0817128465023, 48.5843181174811, 67.6722399183037, 74.1549277799119, 82.8821797890026, 100)[20250519_005219.]: c(0, 0.00658521551279989, 0.7314579975885, 0.581712846502299, 1.4156818825189, 5.1722399183037, 0.845072220088099, 4.61782021099739, 0)[20250519_005219.]: c(NA, 0.0526817241023991, 2.925831990354, 1.55123425733946, 2.83136376503781, 8.27558386928592, 1.12676296011747, 5.27750881256845, 0) - [20250519_005219.]: Entered 'hyperbolic_regression'-Function - [20250519_005219.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005219.]: Entered 'cubic_regression'-Function - [20250519_005219.]: 'cubic_regression': minmax = FALSE - [20250519_005219.]: # CpG-site: CpG#5 + [20250519_010054.]: Logging df_agg: CpG#4 + [20250519_010054.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010054.]: c(0, 12.4934147844872, 24.2685420024115, 38.0817128465023, 48.5843181174811, 67.6722399183037, 74.1549277799119, 82.8821797890026, 100)[20250519_010054.]: c(0, 0.00658521551279989, 0.7314579975885, 0.581712846502299, 1.4156818825189, 5.1722399183037, 0.845072220088099, 4.61782021099739, 0)[20250519_010054.]: c(NA, 0.0526817241023991, 2.925831990354, 1.55123425733946, 2.83136376503781, 8.27558386928592, 1.12676296011747, 5.27750881256845, 0) + [20250519_010054.]: Entered 'hyperbolic_regression'-Function + [20250519_010054.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010054.]: Entered 'cubic_regression'-Function + [20250519_010054.]: 'cubic_regression': minmax = FALSE + [20250519_010054.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.41558626275183, 10.1649674907454, 23.9830820412762, 37.2773619900429, 50.8659386543864, 62.4342273571069, 76.3915260534323, 86.159788778566, 100)c(2.41558626275183, 2.3350325092546, 1.0169179587238, 0.222638009957102, 0.865938654386397, 0.0657726428931014, 1.3915260534323, 1.340211221434, 0)c(NA, 18.6802600740368, 4.06767183489519, 0.593701359885604, 1.73187730877279, 0.105236228628962, 1.85536807124307, 1.53166996735315, 0) - [20250519_005219.]: Logging df_agg: CpG#5 - [20250519_005219.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005219.]: c(2.41558626275183, 10.1649674907454, 23.9830820412762, 37.2773619900429, 50.8659386543864, 62.4342273571069, 76.3915260534323, 86.159788778566, 100)[20250519_005219.]: c(2.41558626275183, 2.3350325092546, 1.0169179587238, 0.222638009957102, 0.865938654386397, 0.0657726428931014, 1.3915260534323, 1.340211221434, 0)[20250519_005219.]: c(NA, 18.6802600740368, 4.06767183489519, 0.593701359885604, 1.73187730877279, 0.105236228628962, 1.85536807124307, 1.53166996735315, 0) - [20250519_005219.]: Entered 'hyperbolic_regression'-Function - [20250519_005219.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005219.]: Entered 'cubic_regression'-Function - [20250519_005219.]: 'cubic_regression': minmax = FALSE - [20250519_005219.]: # CpG-site: CpG#6 + [20250519_010054.]: Logging df_agg: CpG#5 + [20250519_010054.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010054.]: c(2.41558626275183, 10.1649674907454, 23.9830820412762, 37.2773619900429, 50.8659386543864, 62.4342273571069, 76.3915260534323, 86.159788778566, 100)[20250519_010054.]: c(2.41558626275183, 2.3350325092546, 1.0169179587238, 0.222638009957102, 0.865938654386397, 0.0657726428931014, 1.3915260534323, 1.340211221434, 0)[20250519_010054.]: c(NA, 18.6802600740368, 4.06767183489519, 0.593701359885604, 1.73187730877279, 0.105236228628962, 1.85536807124307, 1.53166996735315, 0) + [20250519_010054.]: Entered 'hyperbolic_regression'-Function + [20250519_010054.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010055.]: Entered 'cubic_regression'-Function + [20250519_010055.]: 'cubic_regression': minmax = FALSE + [20250519_010055.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.138163748613034, 11.8635558881981, 26.5107449550797, 35.3205073050661, 50.0570767570666, 64.9602944381018, 73.66890571617, 87.1266086585036, 100)c(0.138163748613034, 0.6364441118019, 1.5107449550797, 2.1794926949339, 0.0570767570666035, 2.4602944381018, 1.33109428383, 0.373391341496401, 0)c(NA, 5.0915528944152, 6.04297982031881, 5.81198051982373, 0.114153514133207, 3.93647110096288, 1.77479237844, 0.426732961710172, 0) - [20250519_005219.]: Logging df_agg: CpG#6 - [20250519_005219.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005219.]: c(0.138163748613034, 11.8635558881981, 26.5107449550797, 35.3205073050661, 50.0570767570666, 64.9602944381018, 73.66890571617, 87.1266086585036, 100)[20250519_005219.]: c(0.138163748613034, 0.6364441118019, 1.5107449550797, 2.1794926949339, 0.0570767570666035, 2.4602944381018, 1.33109428383, 0.373391341496401, 0)[20250519_005219.]: c(NA, 5.0915528944152, 6.04297982031881, 5.81198051982373, 0.114153514133207, 3.93647110096288, 1.77479237844, 0.426732961710172, 0) - [20250519_005219.]: Entered 'hyperbolic_regression'-Function - [20250519_005219.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005220.]: Entered 'cubic_regression'-Function - [20250519_005220.]: 'cubic_regression': minmax = FALSE - [20250519_005220.]: # CpG-site: CpG#7 + [20250519_010055.]: Logging df_agg: CpG#6 + [20250519_010055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010055.]: c(0.138163748613034, 11.8635558881981, 26.5107449550797, 35.3205073050661, 50.0570767570666, 64.9602944381018, 73.66890571617, 87.1266086585036, 100)[20250519_010055.]: c(0.138163748613034, 0.6364441118019, 1.5107449550797, 2.1794926949339, 0.0570767570666035, 2.4602944381018, 1.33109428383, 0.373391341496401, 0)[20250519_010055.]: c(NA, 5.0915528944152, 6.04297982031881, 5.81198051982373, 0.114153514133207, 3.93647110096288, 1.77479237844, 0.426732961710172, 0) + [20250519_010055.]: Entered 'hyperbolic_regression'-Function + [20250519_010055.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010055.]: Entered 'cubic_regression'-Function + [20250519_010055.]: 'cubic_regression': minmax = FALSE + [20250519_010055.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.1993162352498, 24.595178967123, 37.8310421041787, 53.5588739724067, 65.9364947980258, 75.7361094434913, 79.432823759854, 100)c(0, 2.3006837647502, 0.404821032876999, 0.3310421041787, 3.5588739724067, 3.43649479802581, 0.736109443491301, 8.067176240146, 0)c(NA, 18.4054701180016, 1.619284131508, 0.882778944476532, 7.1177479448134, 5.49839167684129, 0.981479257988402, 9.21962998873828, 0) - [20250519_005220.]: Logging df_agg: CpG#7 - [20250519_005220.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005220.]: c(0, 10.1993162352498, 24.595178967123, 37.8310421041787, 53.5588739724067, 65.9364947980258, 75.7361094434913, 79.432823759854, 100)[20250519_005220.]: c(0, 2.3006837647502, 0.404821032876999, 0.3310421041787, 3.5588739724067, 3.43649479802581, 0.736109443491301, 8.067176240146, 0)[20250519_005220.]: c(NA, 18.4054701180016, 1.619284131508, 0.882778944476532, 7.1177479448134, 5.49839167684129, 0.981479257988402, 9.21962998873828, 0) - [20250519_005220.]: Entered 'hyperbolic_regression'-Function - [20250519_005220.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005220.]: Entered 'cubic_regression'-Function - [20250519_005220.]: 'cubic_regression': minmax = FALSE - [20250519_005220.]: # CpG-site: CpG#8 + [20250519_010055.]: Logging df_agg: CpG#7 + [20250519_010055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010055.]: c(0, 10.1993162352498, 24.595178967123, 37.8310421041787, 53.5588739724067, 65.9364947980258, 75.7361094434913, 79.432823759854, 100)[20250519_010055.]: c(0, 2.3006837647502, 0.404821032876999, 0.3310421041787, 3.5588739724067, 3.43649479802581, 0.736109443491301, 8.067176240146, 0)[20250519_010055.]: c(NA, 18.4054701180016, 1.619284131508, 0.882778944476532, 7.1177479448134, 5.49839167684129, 0.981479257988402, 9.21962998873828, 0) + [20250519_010055.]: Entered 'hyperbolic_regression'-Function + [20250519_010055.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010055.]: Entered 'cubic_regression'-Function + [20250519_010055.]: 'cubic_regression': minmax = FALSE + [20250519_010055.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.80068218205093, 9.27535134596596, 25.4762621928197, 34.0122075735416, 51.7842655662325, 64.6732311906145, 78.4326978859189, 81.3427232852719, 100)c(2.80068218205093, 3.22464865403404, 0.4762621928197, 3.4877924264584, 1.7842655662325, 2.1732311906145, 3.4326978859189, 6.1572767147281, 0)c(NA, 25.7971892322723, 1.9050487712788, 9.30077980388906, 3.568531132465, 3.4771699049832, 4.57693051455853, 7.03688767397497, 0) - [20250519_005220.]: Logging df_agg: CpG#8 - [20250519_005220.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005220.]: c(2.80068218205093, 9.27535134596596, 25.4762621928197, 34.0122075735416, 51.7842655662325, 64.6732311906145, 78.4326978859189, 81.3427232852719, 100)[20250519_005220.]: c(2.80068218205093, 3.22464865403404, 0.4762621928197, 3.4877924264584, 1.7842655662325, 2.1732311906145, 3.4326978859189, 6.1572767147281, 0)[20250519_005220.]: c(NA, 25.7971892322723, 1.9050487712788, 9.30077980388906, 3.568531132465, 3.4771699049832, 4.57693051455853, 7.03688767397497, 0) - [20250519_005220.]: Entered 'hyperbolic_regression'-Function - [20250519_005220.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005220.]: Entered 'cubic_regression'-Function - [20250519_005220.]: 'cubic_regression': minmax = FALSE - [20250519_005220.]: # CpG-site: CpG#9 + [20250519_010055.]: Logging df_agg: CpG#8 + [20250519_010055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010055.]: c(2.80068218205093, 9.27535134596596, 25.4762621928197, 34.0122075735416, 51.7842655662325, 64.6732311906145, 78.4326978859189, 81.3427232852719, 100)[20250519_010055.]: c(2.80068218205093, 3.22464865403404, 0.4762621928197, 3.4877924264584, 1.7842655662325, 2.1732311906145, 3.4326978859189, 6.1572767147281, 0)[20250519_010055.]: c(NA, 25.7971892322723, 1.9050487712788, 9.30077980388906, 3.568531132465, 3.4771699049832, 4.57693051455853, 7.03688767397497, 0) + [20250519_010055.]: Entered 'hyperbolic_regression'-Function + [20250519_010055.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010055.]: Entered 'cubic_regression'-Function + [20250519_010055.]: 'cubic_regression': minmax = FALSE + [20250519_010055.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.5082192457956, 26.9164567253388, 36.8334779159501, 52.0097895977263, 64.8930527921581, 74.5671055499357, 84.5294954832669, 100)c(0, 1.9917807542044, 1.9164567253388, 0.666522084049902, 2.0097895977263, 2.3930527921581, 0.432894450064296, 2.9705045167331, 0)c(NA, 15.9342460336352, 7.6658269013552, 1.77739222413307, 4.01957919545259, 3.82888446745296, 0.577192600085728, 3.39486230483783, 0) - [20250519_005220.]: Logging df_agg: CpG#9 - [20250519_005220.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005220.]: c(0, 10.5082192457956, 26.9164567253388, 36.8334779159501, 52.0097895977263, 64.8930527921581, 74.5671055499357, 84.5294954832669, 100)[20250519_005220.]: c(0, 1.9917807542044, 1.9164567253388, 0.666522084049902, 2.0097895977263, 2.3930527921581, 0.432894450064296, 2.9705045167331, 0)[20250519_005220.]: c(NA, 15.9342460336352, 7.6658269013552, 1.77739222413307, 4.01957919545259, 3.82888446745296, 0.577192600085728, 3.39486230483783, 0) - [20250519_005220.]: Entered 'hyperbolic_regression'-Function - [20250519_005220.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005220.]: Entered 'cubic_regression'-Function - [20250519_005220.]: 'cubic_regression': minmax = FALSE - [20250519_005220.]: # CpG-site: row_means + [20250519_010055.]: Logging df_agg: CpG#9 + [20250519_010055.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010055.]: c(0, 10.5082192457956, 26.9164567253388, 36.8334779159501, 52.0097895977263, 64.8930527921581, 74.5671055499357, 84.5294954832669, 100)[20250519_010055.]: c(0, 1.9917807542044, 1.9164567253388, 0.666522084049902, 2.0097895977263, 2.3930527921581, 0.432894450064296, 2.9705045167331, 0)[20250519_010055.]: c(NA, 15.9342460336352, 7.6658269013552, 1.77739222413307, 4.01957919545259, 3.82888446745296, 0.577192600085728, 3.39486230483783, 0) + [20250519_010055.]: Entered 'hyperbolic_regression'-Function + [20250519_010055.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010056.]: Entered 'cubic_regression'-Function + [20250519_010056.]: 'cubic_regression': minmax = FALSE + [20250519_010056.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.290940467580009, 11.041240609297, 25.4081503210647, 36.5243723188613, 50.7348829528732, 65.3135214250794, 75.5342711952113, 83.2411229564887, 100)c(0.290940467580009, 1.458759390703, 0.408150321064699, 0.975627681138697, 0.7348829528732, 2.8135214250794, 0.534271195211304, 4.2588770435113, 0)c(NA, 11.670075125624, 1.6326012842588, 2.60167381636986, 1.4697659057464, 4.50163428012704, 0.712361593615071, 4.8672880497272, 0) - [20250519_005220.]: Logging df_agg: row_means - [20250519_005220.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005220.]: c(0.290940467580009, 11.041240609297, 25.4081503210647, 36.5243723188613, 50.7348829528732, 65.3135214250794, 75.5342711952113, 83.2411229564887, 100)[20250519_005220.]: c(0.290940467580009, 1.458759390703, 0.408150321064699, 0.975627681138697, 0.7348829528732, 2.8135214250794, 0.534271195211304, 4.2588770435113, 0)[20250519_005220.]: c(NA, 11.670075125624, 1.6326012842588, 2.60167381636986, 1.4697659057464, 4.50163428012704, 0.712361593615071, 4.8672880497272, 0) - [20250519_005220.]: Entered 'hyperbolic_regression'-Function - [20250519_005220.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005220.]: Entered 'cubic_regression'-Function - [20250519_005220.]: 'cubic_regression': minmax = FALSE - [20250519_005221.]: ### Starting with plotting ### - [20250519_005221.]: Creating BiasCorrected (hyperbolic) plot No. 1 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG1_corrected_h.png - [20250519_005221.]: # CpG-site: CpG#1 + [20250519_010056.]: Logging df_agg: row_means + [20250519_010056.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010056.]: c(0.290940467580009, 11.041240609297, 25.4081503210647, 36.5243723188613, 50.7348829528732, 65.3135214250794, 75.5342711952113, 83.2411229564887, 100)[20250519_010056.]: c(0.290940467580009, 1.458759390703, 0.408150321064699, 0.975627681138697, 0.7348829528732, 2.8135214250794, 0.534271195211304, 4.2588770435113, 0)[20250519_010056.]: c(NA, 11.670075125624, 1.6326012842588, 2.60167381636986, 1.4697659057464, 4.50163428012704, 0.712361593615071, 4.8672880497272, 0) + [20250519_010056.]: Entered 'hyperbolic_regression'-Function + [20250519_010056.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010056.]: Entered 'cubic_regression'-Function + [20250519_010056.]: 'cubic_regression': minmax = FALSE + [20250519_010056.]: ### Starting with plotting ### + [20250519_010056.]: Creating BiasCorrected (hyperbolic) plot No. 1 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG1_corrected_h.png + [20250519_010056.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -657.853491622191 , a = 985.267531706584 , b = -657.853491622191 , d = 903.45414536045 - [20250519_005221.]: # CpG-site: CpG#1 + [20250519_010056.]: # CpG-site: CpG#1 Cubic: Using a = 5.18463232357963e-06 , b = -0.00180592068110451 , c = 1.11404020909943 , d = -0.858600112691325 - [20250519_005223.]: Creating BiasCorrected (hyperbolic) plot No. 2 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG2_corrected_h.png - [20250519_005223.]: # CpG-site: CpG#2 + [20250519_010058.]: Creating BiasCorrected (hyperbolic) plot No. 2 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG2_corrected_h.png + [20250519_010058.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = 3399.55230393578 , a = 30276.0421015763 , b = 3399.55230393578 , d = 30352.4984502595 - [20250519_005223.]: # CpG-site: CpG#2 + [20250519_010058.]: # CpG-site: CpG#2 Cubic: Using a = -2.58097549676629e-05 , b = 0.00383753278164476 , c = 0.8516077608074 , d = 0.957127237506517 - [20250519_005225.]: Creating BiasCorrected (hyperbolic) plot No. 3 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG3_corrected_h.png - [20250519_005225.]: # CpG-site: CpG#3 + [20250519_010100.]: Creating BiasCorrected (hyperbolic) plot No. 3 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG3_corrected_h.png + [20250519_010100.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = -945.757057025882 , a = 1333.61975246108 , b = -945.757057025882 , d = 1252.95856205348 - [20250519_005225.]: # CpG-site: CpG#3 + [20250519_010100.]: # CpG-site: CpG#3 Cubic: Using a = -1.13948611491254e-05 , b = 0.000937180276062016 , c = 0.998630620080636 , d = -0.383337686690576 - [20250519_005227.]: Creating BiasCorrected (hyperbolic) plot No. 4 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG4_corrected_h.png - [20250519_005227.]: # CpG-site: CpG#4 + [20250519_010102.]: Creating BiasCorrected (hyperbolic) plot No. 4 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG4_corrected_h.png + [20250519_010102.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = -754.050736416394 , a = 1637.26786961468 , b = -754.050736416394 , d = 1558.27361713715 - [20250519_005227.]: # CpG-site: CpG#4 + [20250519_010102.]: # CpG-site: CpG#4 Cubic: Using a = -3.25779988054422e-06 , b = -0.000129596030700936 , c = 1.03077246222439 , d = -0.370343704072989 - [20250519_005229.]: Creating BiasCorrected (hyperbolic) plot No. 5 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG5_corrected_h.png - [20250519_005229.]: # CpG-site: CpG#5 + [20250519_010104.]: Creating BiasCorrected (hyperbolic) plot No. 5 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG5_corrected_h.png + [20250519_010104.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = -1917.44420974597 , a = -5621.4534701009 , b = -1917.44420974597 , d = -5734.47300613195 - [20250519_005229.]: # CpG-site: CpG#5 + [20250519_010104.]: # CpG-site: CpG#5 Cubic: Using a = -3.16782834876228e-05 , b = 0.00493527189107616 , c = 0.800194601834967 , d = 1.38653015028503 - [20250519_005231.]: Creating BiasCorrected (hyperbolic) plot No. 6 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG6_corrected_h.png - [20250519_005231.]: # CpG-site: CpG#6 + [20250519_010106.]: Creating BiasCorrected (hyperbolic) plot No. 6 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG6_corrected_h.png + [20250519_010106.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = -1255.67712347228 , a = 13775.5934089325 , b = -1255.67712347228 , d = 13690.4825730065 - [20250519_005231.]: # CpG-site: CpG#6 + [20250519_010106.]: # CpG-site: CpG#6 Cubic: Using a = -3.70201684653088e-06 , b = 0.000482210694203131 , c = 0.985293133118367 , d = 0.0293568698433451 - [20250519_005232.]: Creating BiasCorrected (hyperbolic) plot No. 7 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG7_corrected_h.png - [20250519_005232.]: # CpG-site: CpG#7 + [20250519_010108.]: Creating BiasCorrected (hyperbolic) plot No. 7 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG7_corrected_h.png + [20250519_010108.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -1065.05415478934 , a = 722.083975601617 , b = -1065.05415478934 , d = 634.402060442185 - [20250519_005232.]: # CpG-site: CpG#7 + [20250519_010108.]: # CpG-site: CpG#7 Cubic: Using a = -1.04111972402046e-06 , b = -0.00130150084904922 , c = 1.12399516228677 , d = -1.5985899870939 - [20250519_005234.]: Creating BiasCorrected (hyperbolic) plot No. 8 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG8_corrected_h.png - [20250519_005234.]: # CpG-site: CpG#8 + [20250519_010110.]: Creating BiasCorrected (hyperbolic) plot No. 8 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG8_corrected_h.png + [20250519_010110.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = 68.5784871630731 , a = 4784.90969361316 , b = 68.5784871630731 , d = 4739.12296431021 - [20250519_005234.]: # CpG-site: CpG#8 + [20250519_010110.]: # CpG-site: CpG#8 Cubic: Using a = -4.65730502627556e-05 , b = 0.00676525570440644 , c = 0.747405744075959 , d = 1.52337158754213 - [20250519_005236.]: Creating BiasCorrected (hyperbolic) plot No. 9 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG9_corrected_h.png - [20250519_005236.]: # CpG-site: CpG#9 + [20250519_010111.]: Creating BiasCorrected (hyperbolic) plot No. 9 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG9_corrected_h.png + [20250519_010111.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -1022.70169872734 , a = 1273.73039431947 , b = -1022.70169872734 , d = 1181.86496720377 - [20250519_005236.]: # CpG-site: CpG#9 + [20250519_010111.]: # CpG-site: CpG#9 Cubic: Using a = 4.24790097543708e-06 , b = -0.00144169637811555 , c = 1.09837791472274 , d = -0.97898795630014 - [20250519_005238.]: Creating BiasCorrected (hyperbolic) plot No. 10 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_rowmeans_corrected_h.png - [20250519_005238.]: # CpG-site: row_means + [20250519_010113.]: Creating BiasCorrected (hyperbolic) plot No. 10 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_rowmeans_corrected_h.png + [20250519_010113.]: # CpG-site: row_means Hyperbolic: Using bias_weight = -1166.73858297355 , a = 1740.63784318208 , b = -1166.73858297355 , d = 1653.86071043116 - [20250519_005238.]: # CpG-site: row_means + [20250519_010113.]: # CpG-site: row_means Cubic: Using a = -8.71186803343537e-06 , b = 0.000715553560638339 , c = 1.00248252890793 , d = -0.420554394203132 - [20250519_005240.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG1_corrected_h.png - [20250519_005242.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG2_corrected_h.png - [20250519_005244.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG3_corrected_h.png - [20250519_005245.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG4_corrected_h.png - [20250519_005247.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG5_corrected_h.png - [20250519_005249.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG6_corrected_h.png - [20250519_005251.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG7_corrected_h.png - [20250519_005252.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG8_corrected_h.png - [20250519_005254.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG9_corrected_h.png - [20250519_005256.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_rowmeans_corrected_h.png - [20250519_005258.]: Entered 'solving_equations'-Function - [20250519_005258.]: Solving cubic regression for CpG#1 + [20250519_010115.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG1_corrected_h.png + [20250519_010117.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG2_corrected_h.png + [20250519_010119.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG3_corrected_h.png + [20250519_010121.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG4_corrected_h.png + [20250519_010122.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG5_corrected_h.png + [20250519_010124.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG6_corrected_h.png + [20250519_010126.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG7_corrected_h.png + [20250519_010128.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG8_corrected_h.png + [20250519_010129.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG9_corrected_h.png + [20250519_010131.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_rowmeans_corrected_h.png + [20250519_010133.]: Entered 'solving_equations'-Function + [20250519_010133.]: Solving cubic regression for CpG#1 Coefficients: -1.03617340067344Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_005258.]: Samplename: 0 + [20250519_010133.]: Samplename: 0 Root: 1.334 --> Root in between the borders! Added to results. Coefficients: -8.34150673400678Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_005258.]: Samplename: 12.5 + [20250519_010133.]: Samplename: 12.5 Root: 11.446 --> Root in between the borders! Added to results. Coefficients: -15.3881734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_005258.]: Samplename: 25 + [20250519_010133.]: Samplename: 25 Root: 22.228 --> Root in between the borders! Added to results. Coefficients: -24.2801734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_005258.]: Samplename: 37.5 + [20250519_010133.]: Samplename: 37.5 Root: 36.374 --> Root in between the borders! Added to results. Coefficients: -34.9006734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_005258.]: Samplename: 50 + [20250519_010133.]: Samplename: 50 Root: 52.044 --> Root in between the borders! Added to results. Coefficients: -46.3541734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_005258.]: Samplename: 62.5 + [20250519_010133.]: Samplename: 62.5 Root: 66.144 --> Root in between the borders! Added to results. Coefficients: -55.8931734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_005258.]: Samplename: 75 + [20250519_010133.]: Samplename: 75 Root: 75.864 --> Root in between the borders! Added to results. Coefficients: -63.0981734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_005258.]: Samplename: 87.5 + [20250519_010133.]: Samplename: 87.5 Root: 82.254 --> Root in between the borders! Added to results. Coefficients: -91.0461734006735Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_005258.]: Samplename: 100 + [20250519_010133.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.877 --> '100 < root < 110' --> substitute 100 - [20250519_005258.]: Solving cubic regression for CpG#2 + [20250519_010133.]: Solving cubic regression for CpG#2 Coefficients: -0.283329966329966Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_005258.]: Samplename: 0 + [20250519_010133.]: Samplename: 0 Root: 0.549 --> Root in between the borders! Added to results. Coefficients: -6.33999663299663Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_005258.]: Samplename: 12.5 + [20250519_010133.]: Samplename: 12.5 Root: 11.533 --> Root in between the borders! Added to results. Coefficients: -15.93932996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_005258.]: Samplename: 25 + [20250519_010133.]: Samplename: 25 Root: 26.628 --> Root in between the borders! Added to results. Coefficients: -22.33732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_005258.]: Samplename: 37.5 + [20250519_010133.]: Samplename: 37.5 Root: 35.509 --> Root in between the borders! Added to results. Coefficients: -32.22832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_005258.]: Samplename: 50 + [20250519_010133.]: Samplename: 50 Root: 47.851 --> Root in between the borders! Added to results. Coefficients: -49.96332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_005258.]: Samplename: 62.5 + [20250519_010133.]: Samplename: 62.5 Root: 66.893 --> Root in between the borders! Added to results. Coefficients: -58.96582996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_005258.]: Samplename: 75 + [20250519_010133.]: Samplename: 75 Root: 75.431 --> Root in between the borders! Added to results. Coefficients: -68.8366632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_005258.]: Samplename: 87.5 + [20250519_010133.]: Samplename: 87.5 Root: 84.118 --> Root in between the borders! Added to results. Coefficients: -90.57732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_005258.]: Samplename: 100 + [20250519_010133.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.287 --> '100 < root < 110' --> substitute 100 - [20250519_005258.]: Solving cubic regression for CpG#3 + [20250519_010133.]: Solving cubic regression for CpG#3 Coefficients: -0.90294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_005258.]: Samplename: 0 + [20250519_010133.]: Samplename: 0 Root: 1.441 --> Root in between the borders! Added to results. Coefficients: -6.57294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_005258.]: Samplename: 12.5 + [20250519_010133.]: Samplename: 12.5 Root: 10.568 --> Root in between the borders! Added to results. Coefficients: -15.4289478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_005258.]: Samplename: 25 + [20250519_010133.]: Samplename: 25 Root: 24.796 --> Root in between the borders! Added to results. Coefficients: -22.6129478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_005258.]: Samplename: 37.5 + [20250519_010133.]: Samplename: 37.5 Root: 35.952 --> Root in between the borders! Added to results. Coefficients: -32.7754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_005258.]: Samplename: 50 + [20250519_010133.]: Samplename: 50 Root: 50.684 --> Root in between the borders! Added to results. Coefficients: -43.8889478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_005258.]: Samplename: 62.5 + [20250519_010133.]: Samplename: 62.5 Root: 65.142 --> Root in between the borders! Added to results. Coefficients: -54.9754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_005258.]: Samplename: 75 + [20250519_010133.]: Samplename: 75 Root: 77.905 --> Root in between the borders! Added to results. Coefficients: -57.6562811447812Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_005258.]: Samplename: 87.5 + [20250519_010133.]: Samplename: 87.5 Root: 80.767 --> Root in between the borders! Added to results. Coefficients: -80.6649478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_005258.]: Samplename: 100 + [20250519_010133.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.38 --> '100 < root < 110' --> substitute 100 - [20250519_005258.]: Solving cubic regression for CpG#4 + [20250519_010133.]: Solving cubic regression for CpG#4 Coefficients: -0.597449494949524Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_005258.]: Samplename: 0 + [20250519_010133.]: Samplename: 0 Root: 0.858 --> Root in between the borders! Added to results. Coefficients: -8.25278282828286Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_005258.]: Samplename: 12.5 + [20250519_010133.]: Samplename: 12.5 Root: 12.086 --> Root in between the borders! Added to results. Coefficients: -15.8034494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_005258.]: Samplename: 25 + [20250519_010133.]: Samplename: 25 Root: 23.316 --> Root in between the borders! Added to results. Coefficients: -25.5274494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_005258.]: Samplename: 37.5 + [20250519_010133.]: Samplename: 37.5 Root: 37.383 --> Root in between the borders! Added to results. Coefficients: -33.6369494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_005258.]: Samplename: 50 + [20250519_010133.]: Samplename: 50 Root: 48.353 --> Root in between the borders! Added to results. Coefficients: -50.2554494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_005258.]: Samplename: 62.5 + [20250519_010133.]: Samplename: 62.5 Root: 68.082 --> Root in between the borders! Added to results. Coefficients: -56.5394494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_005258.]: Samplename: 75 + [20250519_010133.]: Samplename: 75 Root: 74.615 --> Root in between the borders! Added to results. Coefficients: -65.5927828282829Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_005258.]: Samplename: 87.5 + [20250519_010133.]: Samplename: 87.5 Root: 83.254 --> Root in between the borders! Added to results. Coefficients: -88.3214494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_005258.]: Samplename: 100 + [20250519_010133.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.715 --> '100 < root < 110' --> substitute 100 - [20250519_005258.]: Solving cubic regression for CpG#5 + [20250519_010133.]: Solving cubic regression for CpG#5 Coefficients: -0.623961279461278Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_005258.]: Samplename: 0 + [20250519_010133.]: Samplename: 0 Root: 1.458 --> Root in between the borders! Added to results. Coefficients: -4.76796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_005258.]: Samplename: 12.5 + [20250519_010133.]: Samplename: 12.5 Root: 10.347 --> Root in between the borders! Added to results. Coefficients: -12.7259612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_005258.]: Samplename: 25 + [20250519_010133.]: Samplename: 25 Root: 24.815 --> Root in between the borders! Added to results. Coefficients: -21.1599612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_005258.]: Samplename: 37.5 + [20250519_010133.]: Samplename: 37.5 Root: 37.902 --> Root in between the borders! Added to results. Coefficients: -30.6954612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_005258.]: Samplename: 50 + [20250519_010133.]: Samplename: 50 Root: 50.977 --> Root in between the borders! Added to results. Coefficients: -39.6579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_005258.]: Samplename: 62.5 + [20250519_010133.]: Samplename: 62.5 Root: 62.126 --> Root in between the borders! Added to results. Coefficients: -51.6829612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_005258.]: Samplename: 75 + [20250519_010133.]: Samplename: 75 Root: 75.852 --> Root in between the borders! Added to results. Coefficients: -61.0146279461279Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_005258.]: Samplename: 87.5 + [20250519_010133.]: Samplename: 87.5 Root: 85.767 --> Root in between the borders! Added to results. Coefficients: -76.0699612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_005258.]: Samplename: 100 + [20250519_010133.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.743 --> '100 < root < 110' --> substitute 100 - [20250519_005258.]: Solving cubic regression for CpG#6 + [20250519_010133.]: Solving cubic regression for CpG#6 Coefficients: -0.196072390572403Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_005258.]: Samplename: 0 + [20250519_010133.]: Samplename: 0 Root: 0.349 --> Root in between the borders! Added to results. Coefficients: -6.73873905723907Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_005258.]: Samplename: 12.5 + [20250519_010133.]: Samplename: 12.5 Root: 11.718 --> Root in between the borders! Added to results. Coefficients: -15.8880723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_005258.]: Samplename: 25 + [20250519_010133.]: Samplename: 25 Root: 26.396 --> Root in between the borders! Added to results. Coefficients: -22.0000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_005258.]: Samplename: 37.5 + [20250519_010133.]: Samplename: 37.5 Root: 35.301 --> Root in between the borders! Added to results. Coefficients: -33.4445723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_005258.]: Samplename: 50 + [20250519_010133.]: Samplename: 50 Root: 50.134 --> Root in between the borders! Added to results. Coefficients: -46.9000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_005258.]: Samplename: 62.5 + [20250519_010133.]: Samplename: 62.5 Root: 64.993 --> Root in between the borders! Added to results. Coefficients: -55.8320723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_005258.]: Samplename: 75 + [20250519_010133.]: Samplename: 75 Root: 73.639 --> Root in between the borders! Added to results. Coefficients: -71.5454057239057Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_005258.]: Samplename: 87.5 + [20250519_010133.]: Samplename: 87.5 Root: 87.043 --> Root in between the borders! Added to results. Coefficients: -89.6560723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_005258.]: Samplename: 100 + [20250519_010133.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.329 --> '100 < root < 110' --> substitute 100 - [20250519_005258.]: Solving cubic regression for CpG#7 + [20250519_010133.]: Solving cubic regression for CpG#7 Coefficients: -1.21495454545456Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_005258.]: Samplename: 0 + [20250519_010133.]: Samplename: 0 Root: 2.13 --> Root in between the borders! Added to results. Coefficients: -5.39562121212123Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_005258.]: Samplename: 12.5 + [20250519_010133.]: Samplename: 12.5 Root: 9.973 --> Root in between the borders! Added to results. Coefficients: -11.2649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_005258.]: Samplename: 25 + [20250519_010133.]: Samplename: 25 Root: 22.206 --> Root in between the borders! Added to results. Coefficients: -17.4509545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_005258.]: Samplename: 37.5 + [20250519_010133.]: Samplename: 37.5 Root: 35.814 --> Root in between the borders! Added to results. Coefficients: -26.0314545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_005258.]: Samplename: 50 + [20250519_010133.]: Samplename: 50 Root: 53.28 --> Root in between the borders! Added to results. Coefficients: -33.9649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_005258.]: Samplename: 62.5 + [20250519_010133.]: Samplename: 62.5 Root: 66.598 --> Root in between the borders! Added to results. Coefficients: -41.1689545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_005258.]: Samplename: 75 + [20250519_010133.]: Samplename: 75 Root: 76.575 --> Root in between the borders! Added to results. Coefficients: -44.1356212121212Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_005258.]: Samplename: 87.5 + [20250519_010133.]: Samplename: 87.5 Root: 80.219 --> Root in between the borders! Added to results. Coefficients: -67.2229545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_005258.]: Samplename: 100 + [20250519_010133.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.506 --> '100 < root < 110' --> substitute 100 - [20250519_005258.]: Solving cubic regression for CpG#8 + [20250519_010133.]: Solving cubic regression for CpG#8 Coefficients: -1.09618518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_005258.]: Samplename: 0 + [20250519_010133.]: Samplename: 0 Root: 2.016 --> Root in between the borders! Added to results. Coefficients: -5.44685185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_005258.]: Samplename: 12.5 + [20250519_010133.]: Samplename: 12.5 Root: 9.458 --> Root in between the borders! Added to results. Coefficients: -16.9301851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_005258.]: Samplename: 25 + [20250519_010133.]: Samplename: 25 Root: 26.35 --> Root in between the borders! Added to results. Coefficients: -23.3501851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_005258.]: Samplename: 37.5 + [20250519_010133.]: Samplename: 37.5 Root: 34.728 --> Root in between the borders! Added to results. Coefficients: -37.6251851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_005258.]: Samplename: 50 + [20250519_010133.]: Samplename: 50 Root: 51.781 --> Root in between the borders! Added to results. Coefficients: -48.8261851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_005258.]: Samplename: 62.5 + [20250519_010133.]: Samplename: 62.5 Root: 64.192 --> Root in between the borders! Added to results. Coefficients: -61.6676851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_005258.]: Samplename: 75 + [20250519_010133.]: Samplename: 75 Root: 77.804 --> Root in between the borders! Added to results. Coefficients: -64.5101851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_005258.]: Samplename: 87.5 + [20250519_010133.]: Samplename: 87.5 Root: 80.758 --> Root in between the borders! Added to results. Coefficients: -86.0601851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_005258.]: Samplename: 100 + [20250519_010133.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.834 --> '100 < root < 110' --> substitute 100 - [20250519_005258.]: Solving cubic regression for CpG#9 + [20250519_010133.]: Solving cubic regression for CpG#9 Coefficients: -0.989865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_005258.]: Samplename: 0 + [20250519_010133.]: Samplename: 0 Root: 1.475 --> Root in between the borders! Added to results. Coefficients: -6.39586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_005258.]: Samplename: 12.5 + [20250519_010133.]: Samplename: 12.5 Root: 10.12 --> Root in between the borders! Added to results. Coefficients: -14.7058653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_005258.]: Samplename: 25 + [20250519_010133.]: Samplename: 25 Root: 24.844 --> Root in between the borders! Added to results. Coefficients: -20.6238653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_005258.]: Samplename: 37.5 + [20250519_010133.]: Samplename: 37.5 Root: 35.327 --> Root in between the borders! Added to results. Coefficients: -31.3958653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_005258.]: Samplename: 50 + [20250519_010133.]: Samplename: 50 Root: 51.855 --> Root in between the borders! Added to results. Coefficients: -42.6858653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_005258.]: Samplename: 62.5 + [20250519_010133.]: Samplename: 62.5 Root: 65.265 --> Root in between the borders! Added to results. Coefficients: -52.9033653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_005258.]: Samplename: 75 + [20250519_010133.]: Samplename: 75 Root: 74.915 --> Root in between the borders! Added to results. Coefficients: -65.492531986532Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_005258.]: Samplename: 87.5 + [20250519_010133.]: Samplename: 87.5 Root: 84.67 --> Root in between the borders! Added to results. Coefficients: -92.9898653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_005258.]: Samplename: 100 + [20250519_010133.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.082 --> '100 < root < 110' --> substitute 100 - [20250519_005258.]: Solving cubic regression for row_means + [20250519_010133.]: Solving cubic regression for row_means Coefficients: -0.771215488215478Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_005258.]: Samplename: 0 + [20250519_010133.]: Samplename: 0 Root: 1.287 --> Root in between the borders! Added to results. Coefficients: -6.47247474747474Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_005258.]: Samplename: 12.5 + [20250519_010133.]: Samplename: 12.5 Root: 10.847 --> Root in between the borders! Added to results. Coefficients: -14.8972154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_005258.]: Samplename: 25 + [20250519_010133.]: Samplename: 25 Root: 24.737 --> Root in between the borders! Added to results. Coefficients: -22.1492154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_005258.]: Samplename: 37.5 + [20250519_010133.]: Samplename: 37.5 Root: 36.02 --> Root in between the borders! Added to results. Coefficients: -32.5259932659933Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_005258.]: Samplename: 50 + [20250519_010133.]: Samplename: 50 Root: 50.639 --> Root in between the borders! Added to results. Coefficients: -44.7218821548821Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_005258.]: Samplename: 62.5 + [20250519_010133.]: Samplename: 62.5 Root: 65.497 --> Root in between the borders! Added to results. Coefficients: -54.4032154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_005258.]: Samplename: 75 + [20250519_010133.]: Samplename: 75 Root: 75.751 --> Root in between the borders! Added to results. Coefficients: -62.4313636363636Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_005258.]: Samplename: 87.5 + [20250519_010133.]: Samplename: 87.5 Root: 83.403 --> Root in between the borders! Added to results. Coefficients: -84.7343265993266Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_005258.]: Samplename: 100 + [20250519_010133.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.573 --> '100 < root < 110' --> substitute 100 - [20250519_005258.]: + [20250519_010133.]: ### Starting with regression calculations ### - [20250519_005258.]: Entered 'regression_type1'-Function + [20250519_010133.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : @@ -14446,592 +14446,592 @@ singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient - [20250519_005258.]: # CpG-site: CpG#1 + [20250519_010133.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) - [20250519_005258.]: Logging df_agg: CpG#1 - [20250519_005258.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005258.]: c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)[20250519_005258.]: c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)[20250519_005258.]: c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) - [20250519_005258.]: Entered 'hyperbolic_regression'-Function - [20250519_005258.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005258.]: Entered 'cubic_regression'-Function - [20250519_005258.]: 'cubic_regression': minmax = FALSE - [20250519_005258.]: # CpG-site: CpG#2 + [20250519_010133.]: Logging df_agg: CpG#1 + [20250519_010133.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010133.]: c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)[20250519_010133.]: c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)[20250519_010133.]: c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) + [20250519_010133.]: Entered 'hyperbolic_regression'-Function + [20250519_010133.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010133.]: Entered 'cubic_regression'-Function + [20250519_010133.]: 'cubic_regression': minmax = FALSE + [20250519_010133.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) - [20250519_005258.]: Logging df_agg: CpG#2 - [20250519_005258.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005258.]: c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)[20250519_005258.]: c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)[20250519_005258.]: c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) - [20250519_005258.]: Entered 'hyperbolic_regression'-Function - [20250519_005258.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005259.]: Entered 'cubic_regression'-Function - [20250519_005259.]: 'cubic_regression': minmax = FALSE - [20250519_005259.]: # CpG-site: CpG#3 + [20250519_010133.]: Logging df_agg: CpG#2 + [20250519_010133.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010133.]: c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)[20250519_010133.]: c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)[20250519_010133.]: c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) + [20250519_010133.]: Entered 'hyperbolic_regression'-Function + [20250519_010133.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010134.]: Entered 'cubic_regression'-Function + [20250519_010134.]: 'cubic_regression': minmax = FALSE + [20250519_010134.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) - [20250519_005259.]: Logging df_agg: CpG#3 - [20250519_005259.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005259.]: c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)[20250519_005259.]: c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)[20250519_005259.]: c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) - [20250519_005259.]: Entered 'hyperbolic_regression'-Function - [20250519_005259.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005259.]: Entered 'cubic_regression'-Function - [20250519_005259.]: 'cubic_regression': minmax = FALSE - [20250519_005259.]: # CpG-site: CpG#4 + [20250519_010134.]: Logging df_agg: CpG#3 + [20250519_010134.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010134.]: c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)[20250519_010134.]: c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)[20250519_010134.]: c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) + [20250519_010134.]: Entered 'hyperbolic_regression'-Function + [20250519_010134.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010134.]: Entered 'cubic_regression'-Function + [20250519_010134.]: 'cubic_regression': minmax = FALSE + [20250519_010134.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) - [20250519_005259.]: Logging df_agg: CpG#4 - [20250519_005259.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005259.]: c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)[20250519_005259.]: c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)[20250519_005259.]: c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) - [20250519_005259.]: Entered 'hyperbolic_regression'-Function - [20250519_005259.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005259.]: Entered 'cubic_regression'-Function - [20250519_005259.]: 'cubic_regression': minmax = FALSE - [20250519_005259.]: # CpG-site: CpG#5 + [20250519_010134.]: Logging df_agg: CpG#4 + [20250519_010134.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010134.]: c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)[20250519_010134.]: c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)[20250519_010134.]: c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) + [20250519_010134.]: Entered 'hyperbolic_regression'-Function + [20250519_010134.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010134.]: Entered 'cubic_regression'-Function + [20250519_010134.]: 'cubic_regression': minmax = FALSE + [20250519_010134.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) - [20250519_005259.]: Logging df_agg: CpG#5 - [20250519_005259.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005259.]: c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)[20250519_005259.]: c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)[20250519_005259.]: c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) - [20250519_005259.]: Entered 'hyperbolic_regression'-Function - [20250519_005259.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005259.]: Entered 'cubic_regression'-Function - [20250519_005259.]: 'cubic_regression': minmax = FALSE - [20250519_005259.]: # CpG-site: CpG#6 + [20250519_010134.]: Logging df_agg: CpG#5 + [20250519_010134.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010134.]: c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)[20250519_010134.]: c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)[20250519_010134.]: c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) + [20250519_010134.]: Entered 'hyperbolic_regression'-Function + [20250519_010134.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010134.]: Entered 'cubic_regression'-Function + [20250519_010134.]: 'cubic_regression': minmax = FALSE + [20250519_010134.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) - [20250519_005259.]: Logging df_agg: CpG#6 - [20250519_005259.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005259.]: c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)[20250519_005259.]: c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)[20250519_005259.]: c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) - [20250519_005259.]: Entered 'hyperbolic_regression'-Function - [20250519_005259.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005300.]: Entered 'cubic_regression'-Function - [20250519_005300.]: 'cubic_regression': minmax = FALSE - [20250519_005300.]: # CpG-site: CpG#7 + [20250519_010134.]: Logging df_agg: CpG#6 + [20250519_010134.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010134.]: c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)[20250519_010134.]: c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)[20250519_010134.]: c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) + [20250519_010134.]: Entered 'hyperbolic_regression'-Function + [20250519_010134.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010135.]: Entered 'cubic_regression'-Function + [20250519_010135.]: 'cubic_regression': minmax = FALSE + [20250519_010135.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) - [20250519_005300.]: Logging df_agg: CpG#7 - [20250519_005300.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005300.]: c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)[20250519_005300.]: c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)[20250519_005300.]: c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) - [20250519_005300.]: Entered 'hyperbolic_regression'-Function - [20250519_005300.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005300.]: Entered 'cubic_regression'-Function - [20250519_005300.]: 'cubic_regression': minmax = FALSE - [20250519_005300.]: # CpG-site: CpG#8 + [20250519_010135.]: Logging df_agg: CpG#7 + [20250519_010135.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010135.]: c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)[20250519_010135.]: c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)[20250519_010135.]: c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) + [20250519_010135.]: Entered 'hyperbolic_regression'-Function + [20250519_010135.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010135.]: Entered 'cubic_regression'-Function + [20250519_010135.]: 'cubic_regression': minmax = FALSE + [20250519_010135.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) - [20250519_005300.]: Logging df_agg: CpG#8 - [20250519_005300.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005300.]: c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)[20250519_005300.]: c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)[20250519_005300.]: c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) - [20250519_005300.]: Entered 'hyperbolic_regression'-Function - [20250519_005300.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005300.]: Entered 'cubic_regression'-Function - [20250519_005300.]: 'cubic_regression': minmax = FALSE - [20250519_005300.]: # CpG-site: CpG#9 + [20250519_010135.]: Logging df_agg: CpG#8 + [20250519_010135.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010135.]: c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)[20250519_010135.]: c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)[20250519_010135.]: c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) + [20250519_010135.]: Entered 'hyperbolic_regression'-Function + [20250519_010135.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010135.]: Entered 'cubic_regression'-Function + [20250519_010135.]: 'cubic_regression': minmax = FALSE + [20250519_010135.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) - [20250519_005300.]: Logging df_agg: CpG#9 - [20250519_005300.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005300.]: c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)[20250519_005300.]: c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)[20250519_005300.]: c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) - [20250519_005300.]: Entered 'hyperbolic_regression'-Function - [20250519_005300.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005300.]: Entered 'cubic_regression'-Function - [20250519_005300.]: 'cubic_regression': minmax = FALSE - [20250519_005300.]: # CpG-site: row_means + [20250519_010135.]: Logging df_agg: CpG#9 + [20250519_010135.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010135.]: c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)[20250519_010135.]: c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)[20250519_010135.]: c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) + [20250519_010135.]: Entered 'hyperbolic_regression'-Function + [20250519_010135.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010135.]: Entered 'cubic_regression'-Function + [20250519_010135.]: 'cubic_regression': minmax = FALSE + [20250519_010135.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) - [20250519_005300.]: Logging df_agg: row_means - [20250519_005300.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005300.]: c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)[20250519_005300.]: c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)[20250519_005300.]: c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) - [20250519_005300.]: Entered 'hyperbolic_regression'-Function - [20250519_005300.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005300.]: Entered 'cubic_regression'-Function - [20250519_005300.]: 'cubic_regression': minmax = FALSE - [20250519_005301.]: ### Starting with plotting ### - [20250519_005301.]: Creating BiasCorrected (cubic) plot No. 1 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG1_corrected_c.png - [20250519_005301.]: # CpG-site: CpG#1 + [20250519_010135.]: Logging df_agg: row_means + [20250519_010135.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010135.]: c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)[20250519_010135.]: c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)[20250519_010135.]: c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) + [20250519_010135.]: Entered 'hyperbolic_regression'-Function + [20250519_010135.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010136.]: Entered 'cubic_regression'-Function + [20250519_010136.]: 'cubic_regression': minmax = FALSE + [20250519_010136.]: ### Starting with plotting ### + [20250519_010136.]: Creating BiasCorrected (cubic) plot No. 1 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG1_corrected_c.png + [20250519_010136.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -1164.77580684101 , a = 2039.49844669959 , b = -1164.77580684101 , d = 1955.83553683067 - [20250519_005301.]: # CpG-site: CpG#1 + [20250519_010136.]: # CpG-site: CpG#1 Cubic: Using a = -3.42821196240366e-05 , b = 0.00462173155013798 , c = 0.850326258865826 , d = 0.500073874211125 - [20250519_005303.]: Creating BiasCorrected (cubic) plot No. 2 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG2_corrected_c.png - [20250519_005303.]: # CpG-site: CpG#2 + [20250519_010138.]: Creating BiasCorrected (cubic) plot No. 2 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG2_corrected_c.png + [20250519_010138.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = -464.128853555559 , a = 4988.85258428155 , b = -464.128853555559 , d = 4925.32559808582 - [20250519_005303.]: # CpG-site: CpG#2 + [20250519_010138.]: # CpG-site: CpG#2 Cubic: Using a = -1.40811796906314e-05 , b = 0.00190849431754485 , c = 0.933455870941473 , d = 0.363096210099674 - [20250519_005305.]: Creating BiasCorrected (cubic) plot No. 3 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG3_corrected_c.png - [20250519_005305.]: # CpG-site: CpG#3 + [20250519_010140.]: Creating BiasCorrected (cubic) plot No. 3 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG3_corrected_c.png + [20250519_010140.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = -745.611435752166 , a = 2026.53033232932 , b = -745.611435752166 , d = 1953.28002286922 - [20250519_005305.]: # CpG-site: CpG#3 + [20250519_010140.]: # CpG-site: CpG#3 Cubic: Using a = -2.78835051857186e-05 , b = 0.00366863707889089 , c = 0.880665171220893 , d = 0.510779308833653 - [20250519_005306.]: Creating BiasCorrected (cubic) plot No. 4 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG4_corrected_c.png - [20250519_005306.]: # CpG-site: CpG#4 + [20250519_010141.]: Creating BiasCorrected (cubic) plot No. 4 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG4_corrected_c.png + [20250519_010141.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = -833.073059054674 , a = 2871.1407505286 , b = -833.073059054674 , d = 2796.8155099796 - [20250519_005306.]: # CpG-site: CpG#4 + [20250519_010141.]: # CpG-site: CpG#4 Cubic: Using a = -2.30028865643e-05 , b = 0.00309017851940692 , c = 0.897073107777645 , d = 0.443455761841866 - [20250519_005308.]: Creating BiasCorrected (cubic) plot No. 5 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG5_corrected_c.png - [20250519_005308.]: # CpG-site: CpG#5 + [20250519_010143.]: Creating BiasCorrected (cubic) plot No. 5 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG5_corrected_c.png + [20250519_010143.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = 222.127659665624 , a = 12477.5957294815 , b = 222.127659665624 , d = 12432.2636419037 - [20250519_005308.]: # CpG-site: CpG#5 + [20250519_010143.]: # CpG-site: CpG#5 Cubic: Using a = -1.28632696989134e-05 , b = 0.00184822285299014 , c = 0.931001046631176 , d = 0.437963095185599 - [20250519_005310.]: Creating BiasCorrected (cubic) plot No. 6 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG6_corrected_c.png - [20250519_005310.]: # CpG-site: CpG#6 + [20250519_010145.]: Creating BiasCorrected (cubic) plot No. 6 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG6_corrected_c.png + [20250519_010145.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = -700.510534379779 , a = 21299.1825568909 , b = -700.510534379779 , d = 21234.5878606004 - [20250519_005310.]: # CpG-site: CpG#6 + [20250519_010145.]: # CpG-site: CpG#6 Cubic: Using a = -5.838335151375e-06 , b = 0.000828447163218526 , c = 0.970047764475166 , d = 0.158074701412251 - [20250519_005312.]: Creating BiasCorrected (cubic) plot No. 7 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG7_corrected_c.png - [20250519_005312.]: # CpG-site: CpG#7 + [20250519_010147.]: Creating BiasCorrected (cubic) plot No. 7 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG7_corrected_c.png + [20250519_010147.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -1144.90822837929 , a = 1477.91227480711 , b = -1144.90822837929 , d = 1394.70075909546 - [20250519_005312.]: # CpG-site: CpG#7 + [20250519_010147.]: # CpG-site: CpG#7 Cubic: Using a = -4.54220535614047e-05 , b = 0.00607993130565131 , c = 0.805411493452389 , d = 0.615125382790254 - [20250519_005314.]: Creating BiasCorrected (cubic) plot No. 8 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG8_corrected_c.png - [20250519_005314.]: # CpG-site: CpG#8 + [20250519_010149.]: Creating BiasCorrected (cubic) plot No. 8 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG8_corrected_c.png + [20250519_010149.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = -349.378022452652 , a = 2377.37834502072 , b = -349.378022452652 , d = 2315.2055766781 - [20250519_005314.]: # CpG-site: CpG#8 + [20250519_010149.]: # CpG-site: CpG#8 Cubic: Using a = -2.64622306669639e-05 , b = 0.00353621593528015 , c = 0.877916844709074 , d = 0.698684606493051 - [20250519_005316.]: Creating BiasCorrected (cubic) plot No. 9 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG9_corrected_c.png - [20250519_005316.]: # CpG-site: CpG#9 + [20250519_010151.]: Creating BiasCorrected (cubic) plot No. 9 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG9_corrected_c.png + [20250519_010151.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -1536.91601393605 , a = 3745.17687624221 , b = -1536.91601393605 , d = 3656.2687244524 - [20250519_005316.]: # CpG-site: CpG#9 + [20250519_010151.]: # CpG-site: CpG#9 Cubic: Using a = -2.70976130611725e-05 , b = 0.00378478247182342 , c = 0.871825263051793 , d = 0.455475740974113 - [20250519_005317.]: Creating BiasCorrected (cubic) plot No. 10 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_rowmeans_corrected_c.png - [20250519_005317.]: # CpG-site: row_means + [20250519_010153.]: Creating BiasCorrected (cubic) plot No. 10 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_rowmeans_corrected_c.png + [20250519_010153.]: # CpG-site: row_means Hyperbolic: Using bias_weight = -820.650348722241 , a = 3198.46441212347 , b = -820.650348722241 , d = 3124.20895615508 - [20250519_005317.]: # CpG-site: row_means + [20250519_010153.]: # CpG-site: row_means Cubic: Using a = -2.34328362100009e-05 , b = 0.00319139073176266 , c = 0.891842168032129 , d = 0.493517689787135 - [20250519_005319.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG1_corrected_c.png - [20250519_005321.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG2_corrected_c.png - [20250519_005323.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG3_corrected_c.png - [20250519_005325.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG4_corrected_c.png - [20250519_005327.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG5_corrected_c.png - [20250519_005328.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG6_corrected_c.png - [20250519_005330.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG7_corrected_c.png - [20250519_005332.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG8_corrected_c.png - [20250519_005334.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG9_corrected_c.png - [20250519_005335.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_rowmeans_corrected_c.png - [20250519_005337.]: Entered 'solving_equations'-Function - [20250519_005337.]: Solving hyperbolic regression for CpG#1 + [20250519_010155.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG1_corrected_c.png + [20250519_010157.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG2_corrected_c.png + [20250519_010158.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG3_corrected_c.png + [20250519_010200.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG4_corrected_c.png + [20250519_010202.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG5_corrected_c.png + [20250519_010204.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG6_corrected_c.png + [20250519_010206.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG7_corrected_c.png + [20250519_010207.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG8_corrected_c.png + [20250519_010209.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG9_corrected_c.png + [20250519_010211.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_rowmeans_corrected_c.png + [20250519_010213.]: Entered 'solving_equations'-Function + [20250519_010213.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 79.8673456895745 - [20250519_005337.]: Samplename: Sample#1 + [20250519_010213.]: Samplename: Sample#1 Root: 79.867 --> Root in between the borders! Added to results. Hyperbolic solved: 29.7900184340805 - [20250519_005337.]: Samplename: Sample#10 + [20250519_010213.]: Samplename: Sample#10 Root: 29.79 --> Root in between the borders! Added to results. Hyperbolic solved: 41.6525415639691 - [20250519_005337.]: Samplename: Sample#2 + [20250519_010213.]: Samplename: Sample#2 Root: 41.653 --> Root in between the borders! Added to results. Hyperbolic solved: 57.4652090254513 - [20250519_005337.]: Samplename: Sample#3 + [20250519_010213.]: Samplename: Sample#3 Root: 57.465 --> Root in between the borders! Added to results. Hyperbolic solved: 9.2007130627765 - [20250519_005337.]: Samplename: Sample#4 + [20250519_010213.]: Samplename: Sample#4 Root: 9.201 --> Root in between the borders! Added to results. Hyperbolic solved: 21.8059600538131 - [20250519_005337.]: Samplename: Sample#5 + [20250519_010213.]: Samplename: Sample#5 Root: 21.806 --> Root in between the borders! Added to results. Hyperbolic solved: 23.083796735881 - [20250519_005337.]: Samplename: Sample#6 + [20250519_010213.]: Samplename: Sample#6 Root: 23.084 --> Root in between the borders! Added to results. Hyperbolic solved: 45.5034245569385 - [20250519_005337.]: Samplename: Sample#7 + [20250519_010213.]: Samplename: Sample#7 Root: 45.503 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6987904075704 - [20250519_005337.]: Samplename: Sample#8 + [20250519_010213.]: Samplename: Sample#8 Root: 85.699 --> Root in between the borders! Added to results. Hyperbolic solved: -3.66512807265101 - [20250519_005337.]: Samplename: Sample#9 + [20250519_010213.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -3.665 --> '-10 < root < 0' --> substitute 0 - [20250519_005337.]: Solving hyperbolic regression for CpG#2 + [20250519_010213.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 76.7227931065881 - [20250519_005337.]: Samplename: Sample#1 + [20250519_010213.]: Samplename: Sample#1 Root: 76.723 --> Root in between the borders! Added to results. Hyperbolic solved: 30.992427511473 - [20250519_005337.]: Samplename: Sample#10 + [20250519_010213.]: Samplename: Sample#10 Root: 30.992 --> Root in between the borders! Added to results. Hyperbolic solved: 42.6810882780827 - [20250519_005337.]: Samplename: Sample#2 + [20250519_010213.]: Samplename: Sample#2 Root: 42.681 --> Root in between the borders! Added to results. Hyperbolic solved: 58.9337908379889 - [20250519_005337.]: Samplename: Sample#3 + [20250519_010213.]: Samplename: Sample#3 Root: 58.934 --> Root in between the borders! Added to results. Hyperbolic solved: 4.98020457313721 - [20250519_005337.]: Samplename: Sample#4 + [20250519_010213.]: Samplename: Sample#4 Root: 4.98 --> Root in between the borders! Added to results. Hyperbolic solved: 20.2480263675669 - [20250519_005337.]: Samplename: Sample#5 + [20250519_010213.]: Samplename: Sample#5 Root: 20.248 --> Root in between the borders! Added to results. Hyperbolic solved: 17.8133085643845 - [20250519_005337.]: Samplename: Sample#6 + [20250519_010213.]: Samplename: Sample#6 Root: 17.813 --> Root in between the borders! Added to results. Hyperbolic solved: 40.9176745340012 - [20250519_005337.]: Samplename: Sample#7 + [20250519_010213.]: Samplename: Sample#7 Root: 40.918 --> Root in between the borders! Added to results. Hyperbolic solved: 86.0450715732916 - [20250519_005337.]: Samplename: Sample#8 + [20250519_010213.]: Samplename: Sample#8 Root: 86.045 --> Root in between the borders! Added to results. Hyperbolic solved: 3.11579770508969 - [20250519_005337.]: Samplename: Sample#9 + [20250519_010213.]: Samplename: Sample#9 Root: 3.116 --> Root in between the borders! Added to results. - [20250519_005337.]: Solving hyperbolic regression for CpG#3 + [20250519_010213.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 74.9349254100163 - [20250519_005337.]: Samplename: Sample#1 + [20250519_010213.]: Samplename: Sample#1 Root: 74.935 --> Root in between the borders! Added to results. Hyperbolic solved: 27.6844381581493 - [20250519_005337.]: Samplename: Sample#10 + [20250519_010213.]: Samplename: Sample#10 Root: 27.684 --> Root in between the borders! Added to results. Hyperbolic solved: 41.852019114379 - [20250519_005337.]: Samplename: Sample#2 + [20250519_010213.]: Samplename: Sample#2 Root: 41.852 --> Root in between the borders! Added to results. Hyperbolic solved: 55.8325180209418 - [20250519_005337.]: Samplename: Sample#3 + [20250519_010213.]: Samplename: Sample#3 Root: 55.833 --> Root in between the borders! Added to results. Hyperbolic solved: 8.03519251633153 - [20250519_005337.]: Samplename: Sample#4 + [20250519_010213.]: Samplename: Sample#4 Root: 8.035 --> Root in between the borders! Added to results. Hyperbolic solved: 24.1066315721853 - [20250519_005337.]: Samplename: Sample#5 + [20250519_010213.]: Samplename: Sample#5 Root: 24.107 --> Root in between the borders! Added to results. Hyperbolic solved: 26.2419820027673 - [20250519_005337.]: Samplename: Sample#6 + [20250519_010213.]: Samplename: Sample#6 Root: 26.242 --> Root in between the borders! Added to results. Hyperbolic solved: 44.0944922703422 - [20250519_005337.]: Samplename: Sample#7 + [20250519_010213.]: Samplename: Sample#7 Root: 44.094 --> Root in between the borders! Added to results. Hyperbolic solved: 85.8279382585787 - [20250519_005337.]: Samplename: Sample#8 + [20250519_010213.]: Samplename: Sample#8 Root: 85.828 --> Root in between the borders! Added to results. Hyperbolic solved: -0.666482392725758 - [20250519_005337.]: Samplename: Sample#9 + [20250519_010213.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.666 --> '-10 < root < 0' --> substitute 0 - [20250519_005337.]: Solving hyperbolic regression for CpG#4 + [20250519_010213.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 76.3495278640236 - [20250519_005337.]: Samplename: Sample#1 + [20250519_010213.]: Samplename: Sample#1 Root: 76.35 --> Root in between the borders! Added to results. Hyperbolic solved: 28.2568553570941 - [20250519_005337.]: Samplename: Sample#10 + [20250519_010213.]: Samplename: Sample#10 Root: 28.257 --> Root in between the borders! Added to results. Hyperbolic solved: 43.4089839390807 - [20250519_005337.]: Samplename: Sample#2 + [20250519_010213.]: Samplename: Sample#2 Root: 43.409 --> Root in between the borders! Added to results. Hyperbolic solved: 58.5435236860146 - [20250519_005337.]: Samplename: Sample#3 + [20250519_010213.]: Samplename: Sample#3 Root: 58.544 --> Root in between the borders! Added to results. Hyperbolic solved: 10.3087045690571 - [20250519_005337.]: Samplename: Sample#4 + [20250519_010213.]: Samplename: Sample#4 Root: 10.309 --> Root in between the borders! Added to results. Hyperbolic solved: 22.183045165659 - [20250519_005337.]: Samplename: Sample#5 + [20250519_010213.]: Samplename: Sample#5 Root: 22.183 --> Root in between the borders! Added to results. Hyperbolic solved: 27.1337769553499 - [20250519_005337.]: Samplename: Sample#6 + [20250519_010213.]: Samplename: Sample#6 Root: 27.134 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8321096080155 - [20250519_005337.]: Samplename: Sample#7 + [20250519_010213.]: Samplename: Sample#7 Root: 41.832 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6890189074743 - [20250519_005337.]: Samplename: Sample#8 + [20250519_010213.]: Samplename: Sample#8 Root: 85.689 --> Root in between the borders! Added to results. Hyperbolic solved: 2.42232098177269 - [20250519_005337.]: Samplename: Sample#9 + [20250519_010213.]: Samplename: Sample#9 Root: 2.422 --> Root in between the borders! Added to results. - [20250519_005337.]: Solving hyperbolic regression for CpG#5 + [20250519_010213.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 72.8126914355011 - [20250519_005337.]: Samplename: Sample#1 + [20250519_010213.]: Samplename: Sample#1 Root: 72.813 --> Root in between the borders! Added to results. Hyperbolic solved: 26.4206935815673 - [20250519_005337.]: Samplename: Sample#10 + [20250519_010213.]: Samplename: Sample#10 Root: 26.421 --> Root in between the borders! Added to results. Hyperbolic solved: 44.2738765057261 - [20250519_005337.]: Samplename: Sample#2 + [20250519_010213.]: Samplename: Sample#2 Root: 44.274 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5324386232341 - [20250519_005337.]: Samplename: Sample#3 + [20250519_010213.]: Samplename: Sample#3 Root: 53.532 --> Root in between the borders! Added to results. Hyperbolic solved: 10.0373717962154 - [20250519_005337.]: Samplename: Sample#4 + [20250519_010213.]: Samplename: Sample#4 Root: 10.037 --> Root in between the borders! Added to results. Hyperbolic solved: 22.8733115052607 - [20250519_005337.]: Samplename: Sample#5 + [20250519_010213.]: Samplename: Sample#5 Root: 22.873 --> Root in between the borders! Added to results. Hyperbolic solved: 25.9417915784748 - [20250519_005337.]: Samplename: Sample#6 + [20250519_010213.]: Samplename: Sample#6 Root: 25.942 --> Root in between the borders! Added to results. Hyperbolic solved: 42.7945074918604 - [20250519_005337.]: Samplename: Sample#7 + [20250519_010213.]: Samplename: Sample#7 Root: 42.795 --> Root in between the borders! Added to results. Hyperbolic solved: 88.8741214630346 - [20250519_005337.]: Samplename: Sample#8 + [20250519_010213.]: Samplename: Sample#8 Root: 88.874 --> Root in between the borders! Added to results. Hyperbolic solved: 2.34647529966966 - [20250519_005337.]: Samplename: Sample#9 + [20250519_010213.]: Samplename: Sample#9 Root: 2.346 --> Root in between the borders! Added to results. - [20250519_005337.]: Solving hyperbolic regression for CpG#6 + [20250519_010213.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 79.2780593622711 - [20250519_005337.]: Samplename: Sample#1 + [20250519_010213.]: Samplename: Sample#1 Root: 79.278 --> Root in between the borders! Added to results. Hyperbolic solved: 30.2012458984074 - [20250519_005337.]: Samplename: Sample#10 + [20250519_010213.]: Samplename: Sample#10 Root: 30.201 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8474393624107 - [20250519_005337.]: Samplename: Sample#2 + [20250519_010213.]: Samplename: Sample#2 Root: 41.847 --> Root in between the borders! Added to results. Hyperbolic solved: 56.8423517321508 - [20250519_005337.]: Samplename: Sample#3 + [20250519_010213.]: Samplename: Sample#3 Root: 56.842 --> Root in between the borders! Added to results. Hyperbolic solved: 8.87856046118588 - [20250519_005337.]: Samplename: Sample#4 + [20250519_010213.]: Samplename: Sample#4 Root: 8.879 --> Root in between the borders! Added to results. Hyperbolic solved: 18.69015950004 - [20250519_005337.]: Samplename: Sample#5 + [20250519_010213.]: Samplename: Sample#5 Root: 18.69 --> Root in between the borders! Added to results. Hyperbolic solved: 29.9309263534749 - [20250519_005337.]: Samplename: Sample#6 + [20250519_010213.]: Samplename: Sample#6 Root: 29.931 --> Root in between the borders! Added to results. Hyperbolic solved: 42.8148560027697 - [20250519_005337.]: Samplename: Sample#7 + [20250519_010213.]: Samplename: Sample#7 Root: 42.815 --> Root in between the borders! Added to results. Hyperbolic solved: 86.7501831416152 - [20250519_005337.]: Samplename: Sample#8 + [20250519_010213.]: Samplename: Sample#8 Root: 86.75 --> Root in between the borders! Added to results. Hyperbolic solved: 1.51516194985267 - [20250519_005337.]: Samplename: Sample#9 + [20250519_010213.]: Samplename: Sample#9 Root: 1.515 --> Root in between the borders! Added to results. - [20250519_005337.]: Solving hyperbolic regression for CpG#7 + [20250519_010213.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 78.2565592569279 - [20250519_005337.]: Samplename: Sample#1 + [20250519_010213.]: Samplename: Sample#1 Root: 78.257 --> Root in between the borders! Added to results. Hyperbolic solved: 25.488739349283 - [20250519_005337.]: Samplename: Sample#10 + [20250519_010213.]: Samplename: Sample#10 Root: 25.489 --> Root in between the borders! Added to results. Hyperbolic solved: 47.3712258915285 - [20250519_005337.]: Samplename: Sample#2 + [20250519_010213.]: Samplename: Sample#2 Root: 47.371 --> Root in between the borders! Added to results. Hyperbolic solved: 58.3142673189298 - [20250519_005337.]: Samplename: Sample#3 + [20250519_010213.]: Samplename: Sample#3 Root: 58.314 --> Root in between the borders! Added to results. Hyperbolic solved: 11.7212231360573 - [20250519_005337.]: Samplename: Sample#4 + [20250519_010213.]: Samplename: Sample#4 Root: 11.721 --> Root in between the borders! Added to results. Hyperbolic solved: 25.3797485992238 - [20250519_005337.]: Samplename: Sample#5 + [20250519_010213.]: Samplename: Sample#5 Root: 25.38 --> Root in between the borders! Added to results. Hyperbolic solved: 29.4095133062523 - [20250519_005337.]: Samplename: Sample#6 + [20250519_010213.]: Samplename: Sample#6 Root: 29.41 --> Root in between the borders! Added to results. Hyperbolic solved: 44.5755071469546 - [20250519_005337.]: Samplename: Sample#7 + [20250519_010213.]: Samplename: Sample#7 Root: 44.576 --> Root in between the borders! Added to results. Hyperbolic solved: 85.9628731021447 - [20250519_005337.]: Samplename: Sample#8 + [20250519_010213.]: Samplename: Sample#8 Root: 85.963 --> Root in between the borders! Added to results. Hyperbolic solved: -4.1645647175353 - [20250519_005337.]: Samplename: Sample#9 + [20250519_010213.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -4.165 --> '-10 < root < 0' --> substitute 0 - [20250519_005337.]: Solving hyperbolic regression for CpG#8 + [20250519_010213.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 72.9696605050898 - [20250519_005337.]: Samplename: Sample#1 + [20250519_010213.]: Samplename: Sample#1 Root: 72.97 --> Root in between the borders! Added to results. Hyperbolic solved: 27.8250956361743 - [20250519_005337.]: Samplename: Sample#10 + [20250519_010213.]: Samplename: Sample#10 Root: 27.825 --> Root in between the borders! Added to results. Hyperbolic solved: 34.9068388175484 - [20250519_005337.]: Samplename: Sample#2 + [20250519_010213.]: Samplename: Sample#2 Root: 34.907 --> Root in between the borders! Added to results. Hyperbolic solved: 59.163743824213 - [20250519_005337.]: Samplename: Sample#3 + [20250519_010213.]: Samplename: Sample#3 Root: 59.164 --> Root in between the borders! Added to results. Hyperbolic solved: 9.64644413255063 - [20250519_005337.]: Samplename: Sample#4 + [20250519_010213.]: Samplename: Sample#4 Root: 9.646 --> Root in between the borders! Added to results. Hyperbolic solved: 19.3522607837916 - [20250519_005337.]: Samplename: Sample#5 + [20250519_010213.]: Samplename: Sample#5 Root: 19.352 --> Root in between the borders! Added to results. Hyperbolic solved: 38.5571436812761 - [20250519_005337.]: Samplename: Sample#6 + [20250519_010213.]: Samplename: Sample#6 Root: 38.557 --> Root in between the borders! Added to results. Hyperbolic solved: 44.7759625743335 - [20250519_005337.]: Samplename: Sample#7 + [20250519_010213.]: Samplename: Sample#7 Root: 44.776 --> Root in between the borders! Added to results. Hyperbolic solved: 84.8599671624974 - [20250519_005337.]: Samplename: Sample#8 + [20250519_010213.]: Samplename: Sample#8 Root: 84.86 --> Root in between the borders! Added to results. Hyperbolic solved: -2.05044894070214 - [20250519_005337.]: Samplename: Sample#9 + [20250519_010213.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.05 --> '-10 < root < 0' --> substitute 0 - [20250519_005338.]: Solving hyperbolic regression for CpG#9 + [20250519_010213.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 81.041667802328 - [20250519_005338.]: Samplename: Sample#1 + [20250519_010213.]: Samplename: Sample#1 Root: 81.042 --> Root in between the borders! Added to results. Hyperbolic solved: 26.6441666289306 - [20250519_005338.]: Samplename: Sample#10 + [20250519_010213.]: Samplename: Sample#10 Root: 26.644 --> Root in between the borders! Added to results. Hyperbolic solved: 45.7046199658262 - [20250519_005338.]: Samplename: Sample#2 + [20250519_010213.]: Samplename: Sample#2 Root: 45.705 --> Root in between the borders! Added to results. Hyperbolic solved: 56.9767986854544 - [20250519_005338.]: Samplename: Sample#3 + [20250519_010213.]: Samplename: Sample#3 Root: 56.977 --> Root in between the borders! Added to results. Hyperbolic solved: 6.81231222322667 - [20250519_005338.]: Samplename: Sample#4 + [20250519_010213.]: Samplename: Sample#4 Root: 6.812 --> Root in between the borders! Added to results. Hyperbolic solved: 22.9284780361346 - [20250519_005338.]: Samplename: Sample#5 + [20250519_010213.]: Samplename: Sample#5 Root: 22.928 --> Root in between the borders! Added to results. Hyperbolic solved: 38.8653203361426 - [20250519_005338.]: Samplename: Sample#6 + [20250519_010213.]: Samplename: Sample#6 Root: 38.865 --> Root in between the borders! Added to results. Hyperbolic solved: 43.6855631975613 - [20250519_005338.]: Samplename: Sample#7 + [20250519_010213.]: Samplename: Sample#7 Root: 43.686 --> Root in between the borders! Added to results. Hyperbolic solved: 88.0155364495973 - [20250519_005338.]: Samplename: Sample#8 + [20250519_010213.]: Samplename: Sample#8 Root: 88.016 --> Root in between the borders! Added to results. Hyperbolic solved: -3.35610072407525 - [20250519_005338.]: Samplename: Sample#9 + [20250519_010213.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -3.356 --> '-10 < root < 0' --> substitute 0 - [20250519_005338.]: Solving hyperbolic regression for row_means + [20250519_010213.]: Solving hyperbolic regression for row_means Hyperbolic solved: 77.0692799954538 - [20250519_005338.]: Samplename: Sample#1 + [20250519_010213.]: Samplename: Sample#1 Root: 77.069 --> Root in between the borders! Added to results. Hyperbolic solved: 28.3620043244584 - [20250519_005338.]: Samplename: Sample#10 + [20250519_010213.]: Samplename: Sample#10 Root: 28.362 --> Root in between the borders! Added to results. Hyperbolic solved: 42.5026175468089 - [20250519_005338.]: Samplename: Sample#2 + [20250519_010213.]: Samplename: Sample#2 Root: 42.503 --> Root in between the borders! Added to results. Hyperbolic solved: 57.2972050444248 - [20250519_005338.]: Samplename: Sample#3 + [20250519_010213.]: Samplename: Sample#3 Root: 57.297 --> Root in between the borders! Added to results. Hyperbolic solved: 8.82704012618393 - [20250519_005338.]: Samplename: Sample#4 + [20250519_010213.]: Samplename: Sample#4 Root: 8.827 --> Root in between the borders! Added to results. Hyperbolic solved: 21.8102592524881 - [20250519_005338.]: Samplename: Sample#5 + [20250519_010213.]: Samplename: Sample#5 Root: 21.81 --> Root in between the borders! Added to results. Hyperbolic solved: 28.7228660045975 - [20250519_005338.]: Samplename: Sample#6 + [20250519_010213.]: Samplename: Sample#6 Root: 28.723 --> Root in between the borders! Added to results. Hyperbolic solved: 43.410510290696 - [20250519_005338.]: Samplename: Sample#7 + [20250519_010213.]: Samplename: Sample#7 Root: 43.411 --> Root in between the borders! Added to results. Hyperbolic solved: 86.4143551963301 - [20250519_005338.]: Samplename: Sample#8 + [20250519_010213.]: Samplename: Sample#8 Root: 86.414 --> Root in between the borders! Added to results. Hyperbolic solved: -0.237020571201611 - [20250519_005338.]: Samplename: Sample#9 + [20250519_010213.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.237 --> '-10 < root < 0' --> substitute 0 - [20250519_005338.]: on_start: using future::plan("sequential") - [20250519_005338.]: Entered 'clean_dt'-Function - [20250519_005338.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_005338.]: got experimental data - [20250519_005338.]: Entered 'clean_dt'-Function - [20250519_005338.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_005338.]: got calibration data - [20250519_005338.]: + [20250519_010213.]: on_start: using future::plan("sequential") + [20250519_010213.]: Entered 'clean_dt'-Function + [20250519_010213.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_010213.]: got experimental data + [20250519_010213.]: Entered 'clean_dt'-Function + [20250519_010213.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_010213.]: got calibration data + [20250519_010213.]: ### Starting with regression calculations ### - [20250519_005338.]: Entered 'regression_type1'-Function + [20250519_010213.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : @@ -15804,141 +15804,141 @@ step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient - [20250519_005338.]: # CpG-site: CpG#1 + [20250519_010213.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_005338.]: Logging df_agg: CpG#1 - [20250519_005338.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005338.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005338.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005338.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005338.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_005338.]: Entered 'hyperbolic_regression'-Function - [20250519_005338.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005338.]: Entered 'cubic_regression'-Function - [20250519_005338.]: 'cubic_regression': minmax = FALSE - [20250519_005338.]: # CpG-site: CpG#2 + [20250519_010213.]: Logging df_agg: CpG#1 + [20250519_010213.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010213.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_010213.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_010213.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_010213.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) + [20250519_010213.]: Entered 'hyperbolic_regression'-Function + [20250519_010213.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010213.]: Entered 'cubic_regression'-Function + [20250519_010213.]: 'cubic_regression': minmax = FALSE + [20250519_010213.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_005338.]: Logging df_agg: CpG#2 - [20250519_005338.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005338.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005338.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005338.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005338.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_005338.]: Entered 'hyperbolic_regression'-Function - [20250519_005338.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005338.]: Entered 'cubic_regression'-Function - [20250519_005338.]: 'cubic_regression': minmax = FALSE - [20250519_005338.]: # CpG-site: CpG#3 + [20250519_010213.]: Logging df_agg: CpG#2 + [20250519_010213.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010213.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_010213.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_010213.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_010213.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) + [20250519_010213.]: Entered 'hyperbolic_regression'-Function + [20250519_010213.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010214.]: Entered 'cubic_regression'-Function + [20250519_010214.]: 'cubic_regression': minmax = FALSE + [20250519_010214.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_005338.]: Logging df_agg: CpG#3 - [20250519_005338.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005338.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005338.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005338.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005338.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_005338.]: Entered 'hyperbolic_regression'-Function - [20250519_005338.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005339.]: Entered 'cubic_regression'-Function - [20250519_005339.]: 'cubic_regression': minmax = FALSE - [20250519_005339.]: # CpG-site: CpG#4 + [20250519_010214.]: Logging df_agg: CpG#3 + [20250519_010214.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010214.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_010214.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_010214.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_010214.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) + [20250519_010214.]: Entered 'hyperbolic_regression'-Function + [20250519_010214.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010214.]: Entered 'cubic_regression'-Function + [20250519_010214.]: 'cubic_regression': minmax = FALSE + [20250519_010214.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_005339.]: Logging df_agg: CpG#4 - [20250519_005339.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005339.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005339.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005339.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005339.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_005339.]: Entered 'hyperbolic_regression'-Function - [20250519_005339.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005339.]: Entered 'cubic_regression'-Function - [20250519_005339.]: 'cubic_regression': minmax = FALSE - [20250519_005339.]: # CpG-site: CpG#5 + [20250519_010214.]: Logging df_agg: CpG#4 + [20250519_010214.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010214.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_010214.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_010214.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_010214.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) + [20250519_010214.]: Entered 'hyperbolic_regression'-Function + [20250519_010214.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010214.]: Entered 'cubic_regression'-Function + [20250519_010214.]: 'cubic_regression': minmax = FALSE + [20250519_010214.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_005339.]: Logging df_agg: CpG#5 - [20250519_005339.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005339.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005339.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005339.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005339.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_005339.]: Entered 'hyperbolic_regression'-Function - [20250519_005339.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005339.]: Entered 'cubic_regression'-Function - [20250519_005339.]: 'cubic_regression': minmax = FALSE - [20250519_005339.]: # CpG-site: CpG#6 + [20250519_010214.]: Logging df_agg: CpG#5 + [20250519_010214.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010214.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_010214.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_010214.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_010214.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) + [20250519_010214.]: Entered 'hyperbolic_regression'-Function + [20250519_010214.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010214.]: Entered 'cubic_regression'-Function + [20250519_010214.]: 'cubic_regression': minmax = FALSE + [20250519_010214.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_005339.]: Logging df_agg: CpG#6 - [20250519_005339.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005339.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005339.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005339.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005339.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_005339.]: Entered 'hyperbolic_regression'-Function - [20250519_005339.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005339.]: Entered 'cubic_regression'-Function - [20250519_005339.]: 'cubic_regression': minmax = FALSE - [20250519_005339.]: # CpG-site: CpG#7 + [20250519_010214.]: Logging df_agg: CpG#6 + [20250519_010214.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010214.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_010214.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_010214.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_010214.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) + [20250519_010214.]: Entered 'hyperbolic_regression'-Function + [20250519_010214.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010214.]: Entered 'cubic_regression'-Function + [20250519_010214.]: 'cubic_regression': minmax = FALSE + [20250519_010214.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_005339.]: Logging df_agg: CpG#7 - [20250519_005339.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005339.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005339.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005339.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005339.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_005339.]: Entered 'hyperbolic_regression'-Function - [20250519_005339.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005339.]: Entered 'cubic_regression'-Function - [20250519_005339.]: 'cubic_regression': minmax = FALSE - [20250519_005339.]: # CpG-site: CpG#8 + [20250519_010214.]: Logging df_agg: CpG#7 + [20250519_010214.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010214.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_010214.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_010214.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_010214.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) + [20250519_010214.]: Entered 'hyperbolic_regression'-Function + [20250519_010214.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010215.]: Entered 'cubic_regression'-Function + [20250519_010215.]: 'cubic_regression': minmax = FALSE + [20250519_010215.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_005339.]: Logging df_agg: CpG#8 - [20250519_005339.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005339.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005339.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005339.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005339.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_005339.]: Entered 'hyperbolic_regression'-Function - [20250519_005339.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005340.]: Entered 'cubic_regression'-Function - [20250519_005340.]: 'cubic_regression': minmax = FALSE - [20250519_005340.]: # CpG-site: CpG#9 + [20250519_010215.]: Logging df_agg: CpG#8 + [20250519_010215.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010215.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_010215.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_010215.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_010215.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) + [20250519_010215.]: Entered 'hyperbolic_regression'-Function + [20250519_010215.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010215.]: Entered 'cubic_regression'-Function + [20250519_010215.]: 'cubic_regression': minmax = FALSE + [20250519_010215.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_005340.]: Logging df_agg: CpG#9 - [20250519_005340.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005340.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005340.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005340.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005340.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_005340.]: Entered 'hyperbolic_regression'-Function - [20250519_005340.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005340.]: Entered 'cubic_regression'-Function - [20250519_005340.]: 'cubic_regression': minmax = FALSE - [20250519_005340.]: # CpG-site: row_means + [20250519_010215.]: Logging df_agg: CpG#9 + [20250519_010215.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010215.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_010215.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_010215.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_010215.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) + [20250519_010215.]: Entered 'hyperbolic_regression'-Function + [20250519_010215.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010215.]: Entered 'cubic_regression'-Function + [20250519_010215.]: 'cubic_regression': minmax = FALSE + [20250519_010215.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723122, 2.47760998435345)c(5.94911111111111, 0.84962962962963, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703704, 19.6995555555555, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_005340.]: Logging df_agg: row_means - [20250519_005340.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005340.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005340.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723122, 2.47760998435345)[20250519_005340.]: c(5.94911111111111, 0.84962962962963, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005340.]: c(NA, 6.79703703703704, 19.6995555555555, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_005340.]: Entered 'hyperbolic_regression'-Function - [20250519_005340.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005340.]: Entered 'cubic_regression'-Function - [20250519_005340.]: 'cubic_regression': minmax = FALSE - [20250519_005341.]: ### Starting with plotting ### - [20250519_005341.]: Creating plot No. 1 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG1.png - [20250519_005341.]: # CpG-site: CpG#1 + [20250519_010215.]: Logging df_agg: row_means + [20250519_010215.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010215.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_010215.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723122, 2.47760998435345)[20250519_010215.]: c(5.94911111111111, 0.84962962962963, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_010215.]: c(NA, 6.79703703703704, 19.6995555555555, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) + [20250519_010215.]: Entered 'hyperbolic_regression'-Function + [20250519_010215.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010215.]: Entered 'cubic_regression'-Function + [20250519_010215.]: 'cubic_regression': minmax = FALSE + [20250519_010216.]: ### Starting with plotting ### + [20250519_010216.]: Creating plot No. 1 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG1.png + [20250519_010216.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -937.71833725671 , a = -108.568188547925 , b = -937.71833725671 , d = -232.05735859911 - [20250519_005341.]: # CpG-site: CpG#1 + [20250519_010216.]: # CpG-site: CpG#1 Cubic: Using a = 6.53413423120091e-05 , b = -0.0055806968734969 , c = 0.784061853455188 , d = 1.93182659932656 - [20250519_005343.]: Creating plot No. 2 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG2.png - [20250519_005343.]: # CpG-site: CpG#2 + [20250519_010218.]: Creating plot No. 2 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG2.png + [20250519_010218.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = -2393.14264996499 , a = -141.862267639951 , b = -2393.14264996499 , d = -268.331587255735 - [20250519_005343.]: # CpG-site: CpG#2 + [20250519_010218.]: # CpG-site: CpG#2 Cubic: Using a = 8.04237934904601e-06 , b = 0.00293158941798942 , c = 0.514821742825076 , d = 9.27667003367003 - [20250519_005345.]: Creating plot No. 3 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG3.png - [20250519_005345.]: # CpG-site: CpG#3 + [20250519_010220.]: Creating plot No. 3 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG3.png + [20250519_010220.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = -1477.46867711742 , a = -170.424279984248 , b = -1477.46867711742 , d = -327.189866698889 - [20250519_005345.]: # CpG-site: CpG#3 + [20250519_010220.]: # CpG-site: CpG#3 Cubic: Using a = 2.30555869809204e-05 , b = -0.000797009331409346 , c = 0.62783129228796 , d = 3.88705218855218 - [20250519_005347.]: Creating plot No. 4 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG4.png - [20250519_005347.]: # CpG-site: CpG#4 + [20250519_010223.]: Creating plot No. 4 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG4.png + [20250519_010223.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = -2115.87814484424 , a = -162.169929539519 , b = -2115.87814484424 , d = -299.662454768746 - [20250519_005347.]: # CpG-site: CpG#4 + [20250519_010223.]: # CpG-site: CpG#4 Cubic: Using a = 3.2166356902357e-05 , b = -0.0015913142857143 , c = 0.697398364598366 , d = 6.17055050505048 - [20250519_005350.]: Creating plot No. 5 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG5.png - [20250519_005350.]: # CpG-site: CpG#5 + [20250519_010225.]: Creating plot No. 5 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG5.png + [20250519_010225.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = -1383.32734884945 , a = -151.440088502132 , b = -1383.32734884945 , d = -304.200280997675 - [20250519_005350.]: # CpG-site: CpG#5 + [20250519_010225.]: # CpG-site: CpG#5 Cubic: Using a = -4.48459708193036e-06 , b = 0.00375371236171235 , c = 0.422446384479718 , d = 5.17203872053872 - [20250519_005352.]: Creating plot No. 6 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG6.png - [20250519_005352.]: # CpG-site: CpG#6 + [20250519_010227.]: Creating plot No. 6 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG6.png + [20250519_010227.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = -1550.59304894212 , a = -125.016150436792 , b = -1550.59304894212 , d = -247.279683783527 - [20250519_005352.]: # CpG-site: CpG#6 + [20250519_010227.]: # CpG-site: CpG#6 Cubic: Using a = 2.38852884399552e-05 , b = 0.000918637037037021 , c = 0.561022132435467 , d = 6.1479276094276 - [20250519_005354.]: Creating plot No. 7 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG7.png - [20250519_005354.]: # CpG-site: CpG#7 + [20250519_010229.]: Creating plot No. 7 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG7.png + [20250519_010229.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -772.843265948977 , a = -75.2726697123378 , b = -772.843265948977 , d = -226.67343046903 - [20250519_005354.]: # CpG-site: CpG#7 + [20250519_010229.]: # CpG-site: CpG#7 Cubic: Using a = 4.98010505050505e-05 , b = -0.00436152150072151 , c = 0.579588917748918 , d = 1.72104545454544 - [20250519_005356.]: Creating plot No. 8 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG8.png - [20250519_005356.]: # CpG-site: CpG#8 + [20250519_010231.]: Creating plot No. 8 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG8.png + [20250519_010231.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = -3872.73534838154 , a = -278.017155483789 , b = -3872.73534838154 , d = -438.894126934244 - [20250519_005356.]: # CpG-site: CpG#8 + [20250519_010231.]: # CpG-site: CpG#8 Cubic: Using a = -1.50060965207632e-05 , b = 0.00447978143338143 , c = 0.534897737694404 , d = 9.56981481481482 - [20250519_005358.]: Creating plot No. 9 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG9.png - [20250519_005358.]: # CpG-site: CpG#9 + [20250519_010233.]: Creating plot No. 9 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG9.png + [20250519_010233.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -843.925271611492 , a = -70.6796326247736 , b = -843.925271611492 , d = -184.482178799669 - [20250519_005358.]: # CpG-site: CpG#9 + [20250519_010233.]: # CpG-site: CpG#9 Cubic: Using a = 7.72300426487093e-05 , b = -0.00542281173641174 , c = 0.67899229597563 , d = 2.72413468013467 - [20250519_005400.]: Creating plot No. 10 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_rowmeans.png - [20250519_005400.]: # CpG-site: row_means + [20250519_010235.]: Creating plot No. 10 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_rowmeans.png + [20250519_010235.]: # CpG-site: row_means Hyperbolic: Using bias_weight = -1525.75751056682 , a = -127.675093193972 , b = -1525.75751056682 , d = -263.003121358972 - [20250519_005400.]: # CpG-site: row_means + [20250519_010235.]: # CpG-site: row_means Cubic: Using a = 2.88923726150392e-05 , b = -0.0006299592752926 , c = 0.600117857944524 , d = 5.17789562289563 - [20250519_005403.]: Entered 'solving_equations'-Function - [20250519_005403.]: Solving hyperbolic regression for CpG#1 + [20250519_010238.]: Entered 'solving_equations'-Function + [20250519_010238.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: -2.23220911684257 - [20250519_005403.]: Samplename: 0 + [20250519_010238.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. @@ -15946,126 +15946,126 @@ Root: -2.232 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.1698564372971 - [20250519_005403.]: Samplename: 12.5 + [20250519_010238.]: Samplename: 12.5 Root: 12.17 --> Root in between the borders! Added to results. Hyperbolic solved: 24.478190918656 - [20250519_005403.]: Samplename: 25 + [20250519_010238.]: Samplename: 25 Root: 24.478 --> Root in between the borders! Added to results. Hyperbolic solved: 38.1730371634976 - [20250519_005403.]: Samplename: 37.5 + [20250519_010238.]: Samplename: 37.5 Root: 38.173 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3349263290239 - [20250519_005403.]: Samplename: 50 + [20250519_010238.]: Samplename: 50 Root: 52.335 --> Root in between the borders! Added to results. Hyperbolic solved: 65.4582665283612 - [20250519_005403.]: Samplename: 62.5 + [20250519_010238.]: Samplename: 62.5 Root: 65.458 --> Root in between the borders! Added to results. Hyperbolic solved: 75.0090705794847 - [20250519_005403.]: Samplename: 75 + [20250519_010238.]: Samplename: 75 Root: 75.009 --> Root in between the borders! Added to results. Hyperbolic solved: 81.5271853400487 - [20250519_005403.]: Samplename: 87.5 + [20250519_010238.]: Samplename: 87.5 Root: 81.527 --> Root in between the borders! Added to results. Hyperbolic solved: 102.400898271831 - [20250519_005403.]: Samplename: 100 + [20250519_010238.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.401 --> '100 < root < 110' --> substitute 100 - [20250519_005403.]: Solving hyperbolic regression for CpG#2 + [20250519_010238.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 1.13660511681856 - [20250519_005403.]: Samplename: 0 + [20250519_010238.]: Samplename: 0 Root: 1.137 --> Root in between the borders! Added to results. Hyperbolic solved: 11.4129696918445 - [20250519_005403.]: Samplename: 12.5 + [20250519_010238.]: Samplename: 12.5 Root: 11.413 --> Root in between the borders! Added to results. Hyperbolic solved: 26.1740004612661 - [20250519_005403.]: Samplename: 25 + [20250519_010238.]: Samplename: 25 Root: 26.174 --> Root in between the borders! Added to results. Hyperbolic solved: 35.1050448132614 - [20250519_005403.]: Samplename: 37.5 + [20250519_010238.]: Samplename: 37.5 Root: 35.105 --> Root in between the borders! Added to results. Hyperbolic solved: 47.6855002074492 - [20250519_005403.]: Samplename: 50 + [20250519_010238.]: Samplename: 50 Root: 47.686 --> Root in between the borders! Added to results. Hyperbolic solved: 67.1440493313574 - [20250519_005403.]: Samplename: 62.5 + [20250519_010238.]: Samplename: 62.5 Root: 67.144 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7644668045486 - [20250519_005403.]: Samplename: 75 + [20250519_010238.]: Samplename: 75 Root: 75.764 --> Root in between the borders! Added to results. Hyperbolic solved: 84.4054158145455 - [20250519_005403.]: Samplename: 87.5 + [20250519_010238.]: Samplename: 87.5 Root: 84.405 --> Root in between the borders! Added to results. Hyperbolic solved: 100.948272543309 - [20250519_005403.]: Samplename: 100 + [20250519_010238.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.948 --> '100 < root < 110' --> substitute 100 - [20250519_005403.]: Solving hyperbolic regression for CpG#3 + [20250519_010238.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 0.512348561877098 - [20250519_005403.]: Samplename: 0 + [20250519_010238.]: Samplename: 0 Root: 0.512 --> Root in between the borders! Added to results. Hyperbolic solved: 10.7523845008661 - [20250519_005403.]: Samplename: 12.5 + [20250519_010238.]: Samplename: 12.5 Root: 10.752 --> Root in between the borders! Added to results. Hyperbolic solved: 25.5218912317424 - [20250519_005403.]: Samplename: 25 + [20250519_010238.]: Samplename: 25 Root: 25.522 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5270488279988 - [20250519_005403.]: Samplename: 37.5 + [20250519_010238.]: Samplename: 37.5 Root: 36.527 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7909283805111 - [20250519_005403.]: Samplename: 50 + [20250519_010238.]: Samplename: 50 Root: 50.791 --> Root in between the borders! Added to results. Hyperbolic solved: 64.8686353441458 - [20250519_005403.]: Samplename: 62.5 + [20250519_010238.]: Samplename: 62.5 Root: 64.869 --> Root in between the borders! Added to results. Hyperbolic solved: 77.5524208271734 - [20250519_005403.]: Samplename: 75 + [20250519_010238.]: Samplename: 75 Root: 77.552 --> Root in between the borders! Added to results. Hyperbolic solved: 80.4374631680835 - [20250519_005403.]: Samplename: 87.5 + [20250519_010238.]: Samplename: 87.5 Root: 80.437 --> Root in between the borders! Added to results. Hyperbolic solved: 102.704019894026 - [20250519_005403.]: Samplename: 100 + [20250519_010238.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.704 --> '100 < root < 110' --> substitute 100 - [20250519_005403.]: Solving hyperbolic regression for CpG#4 + [20250519_010238.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: -0.519496428117613 - [20250519_005403.]: Samplename: 0 + [20250519_010238.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. @@ -16073,126 +16073,126 @@ Root: -0.519 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 12.4934173132007 - [20250519_005403.]: Samplename: 12.5 + [20250519_010238.]: Samplename: 12.5 Root: 12.493 --> Root in between the borders! Added to results. Hyperbolic solved: 24.2685417185317 - [20250519_005403.]: Samplename: 25 + [20250519_010238.]: Samplename: 25 Root: 24.269 --> Root in between the borders! Added to results. Hyperbolic solved: 38.0817103888785 - [20250519_005403.]: Samplename: 37.5 + [20250519_010238.]: Samplename: 37.5 Root: 38.082 --> Root in between the borders! Added to results. Hyperbolic solved: 48.5843148203973 - [20250519_005403.]: Samplename: 50 + [20250519_010238.]: Samplename: 50 Root: 48.584 --> Root in between the borders! Added to results. Hyperbolic solved: 67.672236894341 - [20250519_005403.]: Samplename: 62.5 + [20250519_010238.]: Samplename: 62.5 Root: 67.672 --> Root in between the borders! Added to results. Hyperbolic solved: 74.1549253766253 - [20250519_005403.]: Samplename: 75 + [20250519_010238.]: Samplename: 75 Root: 74.155 --> Root in between the borders! Added to results. Hyperbolic solved: 82.8821786440368 - [20250519_005403.]: Samplename: 87.5 + [20250519_010238.]: Samplename: 87.5 Root: 82.882 --> Root in between the borders! Added to results. Hyperbolic solved: 102.079130232402 - [20250519_005403.]: Samplename: 100 + [20250519_010238.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.079 --> '100 < root < 110' --> substitute 100 - [20250519_005403.]: Solving hyperbolic regression for CpG#5 + [20250519_010238.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 2.41558718123367 - [20250519_005403.]: Samplename: 0 + [20250519_010238.]: Samplename: 0 Root: 2.416 --> Root in between the borders! Added to results. Hyperbolic solved: 10.1649680545674 - [20250519_005403.]: Samplename: 12.5 + [20250519_010238.]: Samplename: 12.5 Root: 10.165 --> Root in between the borders! Added to results. Hyperbolic solved: 23.9830821073418 - [20250519_005403.]: Samplename: 25 + [20250519_010238.]: Samplename: 25 Root: 23.983 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2773617400679 - [20250519_005403.]: Samplename: 37.5 + [20250519_010238.]: Samplename: 37.5 Root: 37.277 --> Root in between the borders! Added to results. Hyperbolic solved: 50.8659382464167 - [20250519_005403.]: Samplename: 50 + [20250519_010238.]: Samplename: 50 Root: 50.866 --> Root in between the borders! Added to results. Hyperbolic solved: 62.4342269461158 - [20250519_005403.]: Samplename: 62.5 + [20250519_010238.]: Samplename: 62.5 Root: 62.434 --> Root in between the borders! Added to results. Hyperbolic solved: 76.3915257997575 - [20250519_005403.]: Samplename: 75 + [20250519_010238.]: Samplename: 75 Root: 76.392 --> Root in between the borders! Added to results. Hyperbolic solved: 86.1597887396996 - [20250519_005403.]: Samplename: 87.5 + [20250519_010238.]: Samplename: 87.5 Root: 86.16 --> Root in between the borders! Added to results. Hyperbolic solved: 100.267760316884 - [20250519_005403.]: Samplename: 100 + [20250519_010238.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.268 --> '100 < root < 110' --> substitute 100 - [20250519_005403.]: Solving hyperbolic regression for CpG#6 + [20250519_010238.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 0.138164161050506 - [20250519_005403.]: Samplename: 0 + [20250519_010238.]: Samplename: 0 Root: 0.138 --> Root in between the borders! Added to results. Hyperbolic solved: 11.8635561171135 - [20250519_005403.]: Samplename: 12.5 + [20250519_010238.]: Samplename: 12.5 Root: 11.864 --> Root in between the borders! Added to results. Hyperbolic solved: 26.510745006666 - [20250519_005403.]: Samplename: 25 + [20250519_010238.]: Samplename: 25 Root: 26.511 --> Root in between the borders! Added to results. Hyperbolic solved: 35.3205072777727 - [20250519_005403.]: Samplename: 37.5 + [20250519_010238.]: Samplename: 37.5 Root: 35.321 --> Root in between the borders! Added to results. Hyperbolic solved: 50.0570766444694 - [20250519_005403.]: Samplename: 50 + [20250519_010238.]: Samplename: 50 Root: 50.057 --> Root in between the borders! Added to results. Hyperbolic solved: 64.9602942986125 - [20250519_005403.]: Samplename: 62.5 + [20250519_010238.]: Samplename: 62.5 Root: 64.96 --> Root in between the borders! Added to results. Hyperbolic solved: 73.6689055886064 - [20250519_005403.]: Samplename: 75 + [20250519_010238.]: Samplename: 75 Root: 73.669 --> Root in between the borders! Added to results. Hyperbolic solved: 87.1266085894673 - [20250519_005403.]: Samplename: 87.5 + [20250519_010238.]: Samplename: 87.5 Root: 87.127 --> Root in between the borders! Added to results. Hyperbolic solved: 100.261637049252 - [20250519_005403.]: Samplename: 100 + [20250519_010238.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.262 --> '100 < root < 110' --> substitute 100 - [20250519_005403.]: Solving hyperbolic regression for CpG#7 + [20250519_010238.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: -1.37235519395329 - [20250519_005403.]: Samplename: 0 + [20250519_010238.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. @@ -16200,85 +16200,85 @@ Root: -1.372 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.1993294013604 - [20250519_005403.]: Samplename: 12.5 + [20250519_010238.]: Samplename: 12.5 Root: 10.199 --> Root in between the borders! Added to results. Hyperbolic solved: 24.5951803850766 - [20250519_005403.]: Samplename: 25 + [20250519_010238.]: Samplename: 25 Root: 24.595 --> Root in between the borders! Added to results. Hyperbolic solved: 37.8310364564335 - [20250519_005403.]: Samplename: 37.5 + [20250519_010238.]: Samplename: 37.5 Root: 37.831 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5588645818264 - [20250519_005403.]: Samplename: 50 + [20250519_010238.]: Samplename: 50 Root: 53.559 --> Root in between the borders! Added to results. Hyperbolic solved: 65.9364860153449 - [20250519_005403.]: Samplename: 62.5 + [20250519_010238.]: Samplename: 62.5 Root: 65.936 --> Root in between the borders! Added to results. Hyperbolic solved: 75.7361033620359 - [20250519_005403.]: Samplename: 75 + [20250519_010238.]: Samplename: 75 Root: 75.736 --> Root in between the borders! Added to results. Hyperbolic solved: 79.4328192070151 - [20250519_005403.]: Samplename: 87.5 + [20250519_010238.]: Samplename: 87.5 Root: 79.433 --> Root in between the borders! Added to results. Hyperbolic solved: 103.004248773309 - [20250519_005403.]: Samplename: 100 + [20250519_010238.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 103.004 --> '100 < root < 110' --> substitute 100 - [20250519_005403.]: Solving hyperbolic regression for CpG#8 + [20250519_010238.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 2.80068093389159 - [20250519_005403.]: Samplename: 0 + [20250519_010238.]: Samplename: 0 Root: 2.801 --> Root in between the borders! Added to results. Hyperbolic solved: 9.27535066703723 - [20250519_005403.]: Samplename: 12.5 + [20250519_010238.]: Samplename: 12.5 Root: 9.275 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4762625870809 - [20250519_005403.]: Samplename: 25 + [20250519_010238.]: Samplename: 25 Root: 25.476 --> Root in between the borders! Added to results. Hyperbolic solved: 34.0122083314201 - [20250519_005403.]: Samplename: 37.5 + [20250519_010238.]: Samplename: 37.5 Root: 34.012 --> Root in between the borders! Added to results. Hyperbolic solved: 51.7842666342759 - [20250519_005403.]: Samplename: 50 + [20250519_010238.]: Samplename: 50 Root: 51.784 --> Root in between the borders! Added to results. Hyperbolic solved: 64.6732321058682 - [20250519_005403.]: Samplename: 62.5 + [20250519_010238.]: Samplename: 62.5 Root: 64.673 --> Root in between the borders! Added to results. Hyperbolic solved: 78.4326982875914 - [20250519_005403.]: Samplename: 75 + [20250519_010238.]: Samplename: 75 Root: 78.433 --> Root in between the borders! Added to results. Hyperbolic solved: 81.3427235319597 - [20250519_005403.]: Samplename: 87.5 + [20250519_010238.]: Samplename: 87.5 Root: 81.343 --> Root in between the borders! Added to results. Hyperbolic solved: 101.964405325155 - [20250519_005403.]: Samplename: 100 + [20250519_010238.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.964 --> '100 < root < 110' --> substitute 100 - [20250519_005403.]: Solving hyperbolic regression for CpG#9 + [20250519_010238.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: -2.1340328997008 - [20250519_005403.]: Samplename: 0 + [20250519_010238.]: Samplename: 0 ## WARNING ## No fitting root within the borders found. @@ -16286,85 +16286,85 @@ Root: -2.134 --> '-10 < root < 0' --> substitute 0 Hyperbolic solved: 10.5082212970134 - [20250519_005403.]: Samplename: 12.5 + [20250519_010238.]: Samplename: 12.5 Root: 10.508 --> Root in between the borders! Added to results. Hyperbolic solved: 26.9164566258777 - [20250519_005403.]: Samplename: 25 + [20250519_010238.]: Samplename: 25 Root: 26.916 --> Root in between the borders! Added to results. Hyperbolic solved: 36.8334769505904 - [20250519_005403.]: Samplename: 37.5 + [20250519_010238.]: Samplename: 37.5 Root: 36.833 --> Root in between the borders! Added to results. Hyperbolic solved: 52.0097879402751 - [20250519_005403.]: Samplename: 50 + [20250519_010238.]: Samplename: 50 Root: 52.01 --> Root in between the borders! Added to results. Hyperbolic solved: 64.893051148082 - [20250519_005403.]: Samplename: 62.5 + [20250519_010238.]: Samplename: 62.5 Root: 64.893 --> Root in between the borders! Added to results. Hyperbolic solved: 74.5671042786367 - [20250519_005403.]: Samplename: 75 + [20250519_010238.]: Samplename: 75 Root: 74.567 --> Root in between the borders! Added to results. Hyperbolic solved: 84.5294949210319 - [20250519_005403.]: Samplename: 87.5 + [20250519_010238.]: Samplename: 87.5 Root: 84.529 --> Root in between the borders! Added to results. Hyperbolic solved: 101.047147807246 - [20250519_005403.]: Samplename: 100 + [20250519_010238.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.047 --> '100 < root < 110' --> substitute 100 - [20250519_005403.]: Solving hyperbolic regression for row_means + [20250519_010238.]: Solving hyperbolic regression for row_means Hyperbolic solved: 0.290944901553815 - [20250519_005403.]: Samplename: 0 + [20250519_010238.]: Samplename: 0 Root: 0.291 --> Root in between the borders! Added to results. Hyperbolic solved: 11.0412427310408 - [20250519_005403.]: Samplename: 12.5 + [20250519_010238.]: Samplename: 12.5 Root: 11.041 --> Root in between the borders! Added to results. Hyperbolic solved: 25.4081501338029 - [20250519_005403.]: Samplename: 25 + [20250519_010238.]: Samplename: 25 Root: 25.408 --> Root in between the borders! Added to results. Hyperbolic solved: 36.5243709582085 - [20250519_005403.]: Samplename: 37.5 + [20250519_010238.]: Samplename: 37.5 Root: 36.524 --> Root in between the borders! Added to results. Hyperbolic solved: 50.7348808712519 - [20250519_005403.]: Samplename: 50 + [20250519_010238.]: Samplename: 50 Root: 50.735 --> Root in between the borders! Added to results. Hyperbolic solved: 65.3135195123017 - [20250519_005403.]: Samplename: 62.5 + [20250519_010238.]: Samplename: 62.5 Root: 65.314 --> Root in between the borders! Added to results. Hyperbolic solved: 75.5342699494589 - [20250519_005403.]: Samplename: 75 + [20250519_010238.]: Samplename: 75 Root: 75.534 --> Root in between the borders! Added to results. Hyperbolic solved: 83.2411225127701 - [20250519_005403.]: Samplename: 87.5 + [20250519_010238.]: Samplename: 87.5 Root: 83.241 --> Root in between the borders! Added to results. Hyperbolic solved: 101.666944784378 - [20250519_005403.]: Samplename: 100 + [20250519_010238.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.667 --> '100 < root < 110' --> substitute 100 - [20250519_005403.]: + [20250519_010238.]: ### Starting with regression calculations ### - [20250519_005403.]: Entered 'regression_type1'-Function + [20250519_010238.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : @@ -17093,561 +17093,561 @@ step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient - [20250519_005403.]: # CpG-site: CpG#1 + [20250519_010238.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.1698564372971, 24.478190918656, 38.1730371634976, 52.3349263290239, 65.4582665283612, 75.0090705794847, 81.5271853400487, 100)c(0, 0.330143562702901, 0.521809081343999, 0.673037163497597, 2.3349263290239, 2.95826652836119, 0.00907057948469969, 5.9728146599513, 0)c(NA, 2.64114850162321, 2.087236325376, 1.79476576932692, 4.6698526580478, 4.73322644537791, 0.0120941059795996, 6.8260738970872, 0) - [20250519_005403.]: Logging df_agg: CpG#1 - [20250519_005403.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005403.]: c(0, 12.1698564372971, 24.478190918656, 38.1730371634976, 52.3349263290239, 65.4582665283612, 75.0090705794847, 81.5271853400487, 100)[20250519_005403.]: c(0, 0.330143562702901, 0.521809081343999, 0.673037163497597, 2.3349263290239, 2.95826652836119, 0.00907057948469969, 5.9728146599513, 0)[20250519_005403.]: c(NA, 2.64114850162321, 2.087236325376, 1.79476576932692, 4.6698526580478, 4.73322644537791, 0.0120941059795996, 6.8260738970872, 0) - [20250519_005403.]: Entered 'hyperbolic_regression'-Function - [20250519_005403.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005403.]: Entered 'cubic_regression'-Function - [20250519_005403.]: 'cubic_regression': minmax = FALSE - [20250519_005403.]: # CpG-site: CpG#2 + [20250519_010238.]: Logging df_agg: CpG#1 + [20250519_010238.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010238.]: c(0, 12.1698564372971, 24.478190918656, 38.1730371634976, 52.3349263290239, 65.4582665283612, 75.0090705794847, 81.5271853400487, 100)[20250519_010238.]: c(0, 0.330143562702901, 0.521809081343999, 0.673037163497597, 2.3349263290239, 2.95826652836119, 0.00907057948469969, 5.9728146599513, 0)[20250519_010238.]: c(NA, 2.64114850162321, 2.087236325376, 1.79476576932692, 4.6698526580478, 4.73322644537791, 0.0120941059795996, 6.8260738970872, 0) + [20250519_010238.]: Entered 'hyperbolic_regression'-Function + [20250519_010238.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010238.]: Entered 'cubic_regression'-Function + [20250519_010238.]: 'cubic_regression': minmax = FALSE + [20250519_010238.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.13660511681856, 11.4129696918445, 26.1740004612661, 35.1050448132614, 47.6855002074492, 67.1440493313574, 75.7644668045486, 84.4054158145455, 100)c(1.13660511681856, 1.0870303081555, 1.1740004612661, 2.3949551867386, 2.3144997925508, 4.64404933135739, 0.764466804548604, 3.09458418545449, 0)c(NA, 8.696242465244, 4.6960018450644, 6.38654716463626, 4.6289995851016, 7.43047893017183, 1.01928907273147, 3.53666764051942, 0) - [20250519_005403.]: Logging df_agg: CpG#2 - [20250519_005403.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005403.]: c(1.13660511681856, 11.4129696918445, 26.1740004612661, 35.1050448132614, 47.6855002074492, 67.1440493313574, 75.7644668045486, 84.4054158145455, 100)[20250519_005403.]: c(1.13660511681856, 1.0870303081555, 1.1740004612661, 2.3949551867386, 2.3144997925508, 4.64404933135739, 0.764466804548604, 3.09458418545449, 0)[20250519_005403.]: c(NA, 8.696242465244, 4.6960018450644, 6.38654716463626, 4.6289995851016, 7.43047893017183, 1.01928907273147, 3.53666764051942, 0) - [20250519_005403.]: Entered 'hyperbolic_regression'-Function - [20250519_005403.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005404.]: Entered 'cubic_regression'-Function - [20250519_005404.]: 'cubic_regression': minmax = FALSE - [20250519_005404.]: # CpG-site: CpG#3 + [20250519_010238.]: Logging df_agg: CpG#2 + [20250519_010238.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010238.]: c(1.13660511681856, 11.4129696918445, 26.1740004612661, 35.1050448132614, 47.6855002074492, 67.1440493313574, 75.7644668045486, 84.4054158145455, 100)[20250519_010238.]: c(1.13660511681856, 1.0870303081555, 1.1740004612661, 2.3949551867386, 2.3144997925508, 4.64404933135739, 0.764466804548604, 3.09458418545449, 0)[20250519_010238.]: c(NA, 8.696242465244, 4.6960018450644, 6.38654716463626, 4.6289995851016, 7.43047893017183, 1.01928907273147, 3.53666764051942, 0) + [20250519_010238.]: Entered 'hyperbolic_regression'-Function + [20250519_010238.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010238.]: Entered 'cubic_regression'-Function + [20250519_010238.]: 'cubic_regression': minmax = FALSE + [20250519_010238.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.512348561877098, 10.7523845008661, 25.5218912317424, 36.5270488279988, 50.7909283805111, 64.8686353441458, 77.5524208271734, 80.4374631680835, 100)c(0.512348561877098, 1.7476154991339, 0.521891231742401, 0.972951172001203, 0.790928380511097, 2.3686353441458, 2.55242082717341, 7.0625368319165, 0)c(NA, 13.9809239930712, 2.08756492696961, 2.59453645866987, 1.58185676102219, 3.78981655063328, 3.40322776956454, 8.07147066504743, 0) - [20250519_005404.]: Logging df_agg: CpG#3 - [20250519_005404.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005404.]: c(0.512348561877098, 10.7523845008661, 25.5218912317424, 36.5270488279988, 50.7909283805111, 64.8686353441458, 77.5524208271734, 80.4374631680835, 100)[20250519_005404.]: c(0.512348561877098, 1.7476154991339, 0.521891231742401, 0.972951172001203, 0.790928380511097, 2.3686353441458, 2.55242082717341, 7.0625368319165, 0)[20250519_005404.]: c(NA, 13.9809239930712, 2.08756492696961, 2.59453645866987, 1.58185676102219, 3.78981655063328, 3.40322776956454, 8.07147066504743, 0) - [20250519_005404.]: Entered 'hyperbolic_regression'-Function - [20250519_005404.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005404.]: Entered 'cubic_regression'-Function - [20250519_005404.]: 'cubic_regression': minmax = FALSE - [20250519_005404.]: # CpG-site: CpG#4 + [20250519_010239.]: Logging df_agg: CpG#3 + [20250519_010239.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010239.]: c(0.512348561877098, 10.7523845008661, 25.5218912317424, 36.5270488279988, 50.7909283805111, 64.8686353441458, 77.5524208271734, 80.4374631680835, 100)[20250519_010239.]: c(0.512348561877098, 1.7476154991339, 0.521891231742401, 0.972951172001203, 0.790928380511097, 2.3686353441458, 2.55242082717341, 7.0625368319165, 0)[20250519_010239.]: c(NA, 13.9809239930712, 2.08756492696961, 2.59453645866987, 1.58185676102219, 3.78981655063328, 3.40322776956454, 8.07147066504743, 0) + [20250519_010239.]: Entered 'hyperbolic_regression'-Function + [20250519_010239.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010239.]: Entered 'cubic_regression'-Function + [20250519_010239.]: 'cubic_regression': minmax = FALSE + [20250519_010239.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 12.4934173132007, 24.2685417185317, 38.0817103888785, 48.5843148203973, 67.672236894341, 74.1549253766253, 82.8821786440368, 100)c(0, 0.00658268679929996, 0.731458281468299, 0.581710388878498, 1.4156851796027, 5.172236894341, 0.8450746233747, 4.6178213559632, 0)c(NA, 0.0526614943943997, 2.9258331258732, 1.551227703676, 2.83137035920539, 8.27557903094559, 1.1267661644996, 5.2775101211008, 0) - [20250519_005404.]: Logging df_agg: CpG#4 - [20250519_005404.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005404.]: c(0, 12.4934173132007, 24.2685417185317, 38.0817103888785, 48.5843148203973, 67.672236894341, 74.1549253766253, 82.8821786440368, 100)[20250519_005404.]: c(0, 0.00658268679929996, 0.731458281468299, 0.581710388878498, 1.4156851796027, 5.172236894341, 0.8450746233747, 4.6178213559632, 0)[20250519_005404.]: c(NA, 0.0526614943943997, 2.9258331258732, 1.551227703676, 2.83137035920539, 8.27557903094559, 1.1267661644996, 5.2775101211008, 0) - [20250519_005404.]: Entered 'hyperbolic_regression'-Function - [20250519_005404.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005404.]: Entered 'cubic_regression'-Function - [20250519_005404.]: 'cubic_regression': minmax = FALSE - [20250519_005404.]: # CpG-site: CpG#5 + [20250519_010239.]: Logging df_agg: CpG#4 + [20250519_010239.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010239.]: c(0, 12.4934173132007, 24.2685417185317, 38.0817103888785, 48.5843148203973, 67.672236894341, 74.1549253766253, 82.8821786440368, 100)[20250519_010239.]: c(0, 0.00658268679929996, 0.731458281468299, 0.581710388878498, 1.4156851796027, 5.172236894341, 0.8450746233747, 4.6178213559632, 0)[20250519_010239.]: c(NA, 0.0526614943943997, 2.9258331258732, 1.551227703676, 2.83137035920539, 8.27557903094559, 1.1267661644996, 5.2775101211008, 0) + [20250519_010239.]: Entered 'hyperbolic_regression'-Function + [20250519_010239.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010239.]: Entered 'cubic_regression'-Function + [20250519_010239.]: 'cubic_regression': minmax = FALSE + [20250519_010239.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.41558718123367, 10.1649680545674, 23.9830821073418, 37.2773617400679, 50.8659382464167, 62.4342269461158, 76.3915257997575, 86.1597887396996, 100)c(2.41558718123367, 2.3350319454326, 1.0169178926582, 0.222638259932097, 0.865938246416697, 0.0657730538842003, 1.3915257997575, 1.34021126030041, 0)c(NA, 18.6802555634608, 4.0676715706328, 0.593702026485592, 1.73187649283339, 0.10523688621472, 1.85536773301, 1.53167001177189, 0) - [20250519_005404.]: Logging df_agg: CpG#5 - [20250519_005404.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005404.]: c(2.41558718123367, 10.1649680545674, 23.9830821073418, 37.2773617400679, 50.8659382464167, 62.4342269461158, 76.3915257997575, 86.1597887396996, 100)[20250519_005404.]: c(2.41558718123367, 2.3350319454326, 1.0169178926582, 0.222638259932097, 0.865938246416697, 0.0657730538842003, 1.3915257997575, 1.34021126030041, 0)[20250519_005404.]: c(NA, 18.6802555634608, 4.0676715706328, 0.593702026485592, 1.73187649283339, 0.10523688621472, 1.85536773301, 1.53167001177189, 0) - [20250519_005404.]: Entered 'hyperbolic_regression'-Function - [20250519_005404.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005404.]: Entered 'cubic_regression'-Function - [20250519_005404.]: 'cubic_regression': minmax = FALSE - [20250519_005404.]: # CpG-site: CpG#6 + [20250519_010239.]: Logging df_agg: CpG#5 + [20250519_010239.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010239.]: c(2.41558718123367, 10.1649680545674, 23.9830821073418, 37.2773617400679, 50.8659382464167, 62.4342269461158, 76.3915257997575, 86.1597887396996, 100)[20250519_010239.]: c(2.41558718123367, 2.3350319454326, 1.0169178926582, 0.222638259932097, 0.865938246416697, 0.0657730538842003, 1.3915257997575, 1.34021126030041, 0)[20250519_010239.]: c(NA, 18.6802555634608, 4.0676715706328, 0.593702026485592, 1.73187649283339, 0.10523688621472, 1.85536773301, 1.53167001177189, 0) + [20250519_010239.]: Entered 'hyperbolic_regression'-Function + [20250519_010239.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010239.]: Entered 'cubic_regression'-Function + [20250519_010239.]: 'cubic_regression': minmax = FALSE + [20250519_010239.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.138164161050506, 11.8635561171135, 26.510745006666, 35.3205072777727, 50.0570766444694, 64.9602942986125, 73.6689055886064, 87.1266085894673, 100)c(0.138164161050506, 0.6364438828865, 1.510745006666, 2.1794927222273, 0.0570766444693973, 2.4602942986125, 1.33109441139359, 0.373391410532705, 0)c(NA, 5.091551063092, 6.042980026664, 5.81198059260614, 0.114153288938795, 3.93647087777999, 1.77479254852479, 0.426733040608805, 0) - [20250519_005404.]: Logging df_agg: CpG#6 - [20250519_005404.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005404.]: c(0.138164161050506, 11.8635561171135, 26.510745006666, 35.3205072777727, 50.0570766444694, 64.9602942986125, 73.6689055886064, 87.1266085894673, 100)[20250519_005404.]: c(0.138164161050506, 0.6364438828865, 1.510745006666, 2.1794927222273, 0.0570766444693973, 2.4602942986125, 1.33109441139359, 0.373391410532705, 0)[20250519_005404.]: c(NA, 5.091551063092, 6.042980026664, 5.81198059260614, 0.114153288938795, 3.93647087777999, 1.77479254852479, 0.426733040608805, 0) - [20250519_005404.]: Entered 'hyperbolic_regression'-Function - [20250519_005404.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005404.]: Entered 'cubic_regression'-Function - [20250519_005404.]: 'cubic_regression': minmax = FALSE - [20250519_005404.]: # CpG-site: CpG#7 + [20250519_010239.]: Logging df_agg: CpG#6 + [20250519_010239.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010239.]: c(0.138164161050506, 11.8635561171135, 26.510745006666, 35.3205072777727, 50.0570766444694, 64.9602942986125, 73.6689055886064, 87.1266085894673, 100)[20250519_010239.]: c(0.138164161050506, 0.6364438828865, 1.510745006666, 2.1794927222273, 0.0570766444693973, 2.4602942986125, 1.33109441139359, 0.373391410532705, 0)[20250519_010239.]: c(NA, 5.091551063092, 6.042980026664, 5.81198059260614, 0.114153288938795, 3.93647087777999, 1.77479254852479, 0.426733040608805, 0) + [20250519_010239.]: Entered 'hyperbolic_regression'-Function + [20250519_010239.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010239.]: Entered 'cubic_regression'-Function + [20250519_010239.]: 'cubic_regression': minmax = FALSE + [20250519_010239.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.1993294013604, 24.5951803850766, 37.8310364564335, 53.5588645818264, 65.9364860153449, 75.7361033620359, 79.4328192070151, 100)c(0, 2.3006705986396, 0.404819614923401, 0.331036456433502, 3.5588645818264, 3.4364860153449, 0.736103362035905, 8.0671807929849, 0)c(NA, 18.4053647891168, 1.6192784596936, 0.882763883822671, 7.1177291636528, 5.49837762455184, 0.981471149381207, 9.21963519198274, 0) - [20250519_005404.]: Logging df_agg: CpG#7 - [20250519_005404.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005404.]: c(0, 10.1993294013604, 24.5951803850766, 37.8310364564335, 53.5588645818264, 65.9364860153449, 75.7361033620359, 79.4328192070151, 100)[20250519_005404.]: c(0, 2.3006705986396, 0.404819614923401, 0.331036456433502, 3.5588645818264, 3.4364860153449, 0.736103362035905, 8.0671807929849, 0)[20250519_005404.]: c(NA, 18.4053647891168, 1.6192784596936, 0.882763883822671, 7.1177291636528, 5.49837762455184, 0.981471149381207, 9.21963519198274, 0) - [20250519_005404.]: Entered 'hyperbolic_regression'-Function - [20250519_005405.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005405.]: Entered 'cubic_regression'-Function - [20250519_005405.]: 'cubic_regression': minmax = FALSE - [20250519_005405.]: # CpG-site: CpG#8 + [20250519_010239.]: Logging df_agg: CpG#7 + [20250519_010239.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010239.]: c(0, 10.1993294013604, 24.5951803850766, 37.8310364564335, 53.5588645818264, 65.9364860153449, 75.7361033620359, 79.4328192070151, 100)[20250519_010239.]: c(0, 2.3006705986396, 0.404819614923401, 0.331036456433502, 3.5588645818264, 3.4364860153449, 0.736103362035905, 8.0671807929849, 0)[20250519_010239.]: c(NA, 18.4053647891168, 1.6192784596936, 0.882763883822671, 7.1177291636528, 5.49837762455184, 0.981471149381207, 9.21963519198274, 0) + [20250519_010239.]: Entered 'hyperbolic_regression'-Function + [20250519_010239.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010240.]: Entered 'cubic_regression'-Function + [20250519_010240.]: 'cubic_regression': minmax = FALSE + [20250519_010240.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.80068093389159, 9.27535066703723, 25.4762625870809, 34.0122083314201, 51.7842666342759, 64.6732321058682, 78.4326982875914, 81.3427235319597, 100)c(2.80068093389159, 3.22464933296277, 0.4762625870809, 3.4877916685799, 1.7842666342759, 2.17323210586819, 3.43269828759141, 6.15727646804029, 0)c(NA, 25.7971946637022, 1.9050503483236, 9.30077778287974, 3.5685332685518, 3.47717136938911, 4.57693105012188, 7.03688739204605, 0) - [20250519_005405.]: Logging df_agg: CpG#8 - [20250519_005405.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005405.]: c(2.80068093389159, 9.27535066703723, 25.4762625870809, 34.0122083314201, 51.7842666342759, 64.6732321058682, 78.4326982875914, 81.3427235319597, 100)[20250519_005405.]: c(2.80068093389159, 3.22464933296277, 0.4762625870809, 3.4877916685799, 1.7842666342759, 2.17323210586819, 3.43269828759141, 6.15727646804029, 0)[20250519_005405.]: c(NA, 25.7971946637022, 1.9050503483236, 9.30077778287974, 3.5685332685518, 3.47717136938911, 4.57693105012188, 7.03688739204605, 0) - [20250519_005405.]: Entered 'hyperbolic_regression'-Function - [20250519_005405.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005405.]: Entered 'cubic_regression'-Function - [20250519_005405.]: 'cubic_regression': minmax = FALSE - [20250519_005405.]: # CpG-site: CpG#9 + [20250519_010240.]: Logging df_agg: CpG#8 + [20250519_010240.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010240.]: c(2.80068093389159, 9.27535066703723, 25.4762625870809, 34.0122083314201, 51.7842666342759, 64.6732321058682, 78.4326982875914, 81.3427235319597, 100)[20250519_010240.]: c(2.80068093389159, 3.22464933296277, 0.4762625870809, 3.4877916685799, 1.7842666342759, 2.17323210586819, 3.43269828759141, 6.15727646804029, 0)[20250519_010240.]: c(NA, 25.7971946637022, 1.9050503483236, 9.30077778287974, 3.5685332685518, 3.47717136938911, 4.57693105012188, 7.03688739204605, 0) + [20250519_010240.]: Entered 'hyperbolic_regression'-Function + [20250519_010240.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010240.]: Entered 'cubic_regression'-Function + [20250519_010240.]: 'cubic_regression': minmax = FALSE + [20250519_010240.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0, 10.5082212970134, 26.9164566258777, 36.8334769505904, 52.0097879402751, 64.893051148082, 74.5671042786367, 84.5294949210319, 100)c(0, 1.9917787029866, 1.9164566258777, 0.666523049409598, 2.0097879402751, 2.39305114808199, 0.432895721363295, 2.9705050789681, 0)c(NA, 15.9342296238928, 7.6658265035108, 1.77739479842559, 4.01957588055021, 3.82888183693119, 0.57719429515106, 3.39486294739212, 0) - [20250519_005405.]: Logging df_agg: CpG#9 - [20250519_005405.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005405.]: c(0, 10.5082212970134, 26.9164566258777, 36.8334769505904, 52.0097879402751, 64.893051148082, 74.5671042786367, 84.5294949210319, 100)[20250519_005405.]: c(0, 1.9917787029866, 1.9164566258777, 0.666523049409598, 2.0097879402751, 2.39305114808199, 0.432895721363295, 2.9705050789681, 0)[20250519_005405.]: c(NA, 15.9342296238928, 7.6658265035108, 1.77739479842559, 4.01957588055021, 3.82888183693119, 0.57719429515106, 3.39486294739212, 0) - [20250519_005405.]: Entered 'hyperbolic_regression'-Function - [20250519_005405.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005405.]: Entered 'cubic_regression'-Function - [20250519_005405.]: 'cubic_regression': minmax = FALSE - [20250519_005405.]: # CpG-site: row_means + [20250519_010240.]: Logging df_agg: CpG#9 + [20250519_010240.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010240.]: c(0, 10.5082212970134, 26.9164566258777, 36.8334769505904, 52.0097879402751, 64.893051148082, 74.5671042786367, 84.5294949210319, 100)[20250519_010240.]: c(0, 1.9917787029866, 1.9164566258777, 0.666523049409598, 2.0097879402751, 2.39305114808199, 0.432895721363295, 2.9705050789681, 0)[20250519_010240.]: c(NA, 15.9342296238928, 7.6658265035108, 1.77739479842559, 4.01957588055021, 3.82888183693119, 0.57719429515106, 3.39486294739212, 0) + [20250519_010240.]: Entered 'hyperbolic_regression'-Function + [20250519_010240.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010240.]: Entered 'cubic_regression'-Function + [20250519_010240.]: 'cubic_regression': minmax = FALSE + [20250519_010240.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.290944901553815, 11.0412427310408, 25.4081501338029, 36.5243709582085, 50.7348808712519, 65.3135195123017, 75.5342699494589, 83.2411225127701, 100)c(0.290944901553815, 1.4587572689592, 0.408150133802899, 0.9756290417915, 0.734880871251903, 2.8135195123017, 0.534269949458903, 4.2588774872299, 0)c(NA, 11.6700581516736, 1.63260053521159, 2.60167744477733, 1.46976174250381, 4.50163121968271, 0.71235993261187, 4.86728855683417, 0) - [20250519_005405.]: Logging df_agg: row_means - [20250519_005405.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005405.]: c(0.290944901553815, 11.0412427310408, 25.4081501338029, 36.5243709582085, 50.7348808712519, 65.3135195123017, 75.5342699494589, 83.2411225127701, 100)[20250519_005405.]: c(0.290944901553815, 1.4587572689592, 0.408150133802899, 0.9756290417915, 0.734880871251903, 2.8135195123017, 0.534269949458903, 4.2588774872299, 0)[20250519_005405.]: c(NA, 11.6700581516736, 1.63260053521159, 2.60167744477733, 1.46976174250381, 4.50163121968271, 0.71235993261187, 4.86728855683417, 0) - [20250519_005405.]: Entered 'hyperbolic_regression'-Function - [20250519_005405.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005405.]: Entered 'cubic_regression'-Function - [20250519_005405.]: 'cubic_regression': minmax = FALSE - [20250519_005406.]: ### Starting with plotting ### - [20250519_005406.]: Creating BiasCorrected (hyperbolic) plot No. 1 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG1_corrected_h.png - [20250519_005406.]: # CpG-site: CpG#1 + [20250519_010240.]: Logging df_agg: row_means + [20250519_010240.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010240.]: c(0.290944901553815, 11.0412427310408, 25.4081501338029, 36.5243709582085, 50.7348808712519, 65.3135195123017, 75.5342699494589, 83.2411225127701, 100)[20250519_010240.]: c(0.290944901553815, 1.4587572689592, 0.408150133802899, 0.9756290417915, 0.734880871251903, 2.8135195123017, 0.534269949458903, 4.2588774872299, 0)[20250519_010240.]: c(NA, 11.6700581516736, 1.63260053521159, 2.60167744477733, 1.46976174250381, 4.50163121968271, 0.71235993261187, 4.86728855683417, 0) + [20250519_010240.]: Entered 'hyperbolic_regression'-Function + [20250519_010240.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010240.]: Entered 'cubic_regression'-Function + [20250519_010240.]: 'cubic_regression': minmax = FALSE + [20250519_010241.]: ### Starting with plotting ### + [20250519_010241.]: Creating BiasCorrected (hyperbolic) plot No. 1 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG1_corrected_h.png + [20250519_010241.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -657.85070009309 , a = 985.271206705952 , b = -657.85070009309 , d = 903.457967400565 - [20250519_005406.]: # CpG-site: CpG#1 + [20250519_010241.]: # CpG-site: CpG#1 Cubic: Using a = 5.1847317144537e-06 , b = -0.00180593132520811 , c = 1.11404026275692 , d = -0.858597345049636 - [20250519_005408.]: Creating BiasCorrected (hyperbolic) plot No. 2 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG2_corrected_h.png - [20250519_005408.]: # CpG-site: CpG#2 + [20250519_010243.]: Creating BiasCorrected (hyperbolic) plot No. 2 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG2_corrected_h.png + [20250519_010243.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = 3399.63077807263 , a = 30276.8974003894 , b = 3399.63077807263 , d = 30353.3579426379 - [20250519_005408.]: # CpG-site: CpG#2 + [20250519_010243.]: # CpG-site: CpG#2 Cubic: Using a = -2.58097552031906e-05 , b = 0.00383753288450718 , c = 0.851607751869747 , d = 0.95712734045721 - [20250519_005410.]: Creating BiasCorrected (hyperbolic) plot No. 3 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG3_corrected_h.png - [20250519_005410.]: # CpG-site: CpG#3 + [20250519_010244.]: Creating BiasCorrected (hyperbolic) plot No. 3 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG3_corrected_h.png + [20250519_010244.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = -945.763900328712 , a = 1333.61328542459 , b = -945.763900328712 , d = 1252.95190710086 - [20250519_005410.]: # CpG-site: CpG#3 + [20250519_010244.]: # CpG-site: CpG#3 Cubic: Using a = -1.13948418252831e-05 , b = 0.000937174314708123 , c = 0.998631105115713 , d = -0.383346041194064 - [20250519_005411.]: Creating BiasCorrected (hyperbolic) plot No. 4 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG4_corrected_h.png - [20250519_005411.]: # CpG-site: CpG#4 + [20250519_010246.]: Creating BiasCorrected (hyperbolic) plot No. 4 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG4_corrected_h.png + [20250519_010246.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = -754.050808709341 , a = 1637.26986382636 , b = -754.050808709341 , d = 1558.27566152171 - [20250519_005411.]: # CpG-site: CpG#4 + [20250519_010246.]: # CpG-site: CpG#4 Cubic: Using a = -3.25777472656452e-06 , b = -0.000129598485618069 , c = 1.03077245158202 , d = -0.370342672245111 - [20250519_005413.]: Creating BiasCorrected (hyperbolic) plot No. 5 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG5_corrected_h.png - [20250519_005413.]: # CpG-site: CpG#5 + [20250519_010248.]: Creating BiasCorrected (hyperbolic) plot No. 5 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG5_corrected_h.png + [20250519_010248.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = -1917.44470782279 , a = -5621.43051220384 , b = -1917.44470782279 , d = -5734.45004174505 - [20250519_005413.]: # CpG-site: CpG#5 + [20250519_010248.]: # CpG-site: CpG#5 Cubic: Using a = -3.16782847952728e-05 , b = 0.00493527243554485 , c = 0.800194551229801 , d = 1.38653114075915 - [20250519_005415.]: Creating BiasCorrected (hyperbolic) plot No. 6 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG6_corrected_h.png - [20250519_005415.]: # CpG-site: CpG#6 + [20250519_010250.]: Creating BiasCorrected (hyperbolic) plot No. 6 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG6_corrected_h.png + [20250519_010250.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = -1255.66539296107 , a = 13775.5132875201 , b = -1255.66539296107 , d = 13690.4025642689 - [20250519_005415.]: # CpG-site: CpG#6 + [20250519_010250.]: # CpG-site: CpG#6 Cubic: Using a = -3.70201699726522e-06 , b = 0.000482210843999232 , c = 0.98529311552838 , d = 0.0293572853154747 - [20250519_005417.]: Creating BiasCorrected (hyperbolic) plot No. 7 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG7_corrected_h.png - [20250519_005417.]: # CpG-site: CpG#7 + [20250519_010252.]: Creating BiasCorrected (hyperbolic) plot No. 7 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG7_corrected_h.png + [20250519_010252.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -1065.05219601462 , a = 722.085443879134 , b = -1065.05219601462 , d = 634.403611801521 - [20250519_005417.]: # CpG-site: CpG#7 + [20250519_010252.]: # CpG-site: CpG#7 Cubic: Using a = -1.04101209223167e-06 , b = -0.00130151331776721 , c = 1.1239953084199 , d = -1.59858561306358 - [20250519_005419.]: Creating BiasCorrected (hyperbolic) plot No. 8 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG8_corrected_h.png - [20250519_005419.]: # CpG-site: CpG#8 + [20250519_010254.]: Creating BiasCorrected (hyperbolic) plot No. 8 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG8_corrected_h.png + [20250519_010254.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = 68.5259802694693 , a = 4784.81913306904 , b = 68.5259802694693 , d = 4739.03101269703 - [20250519_005419.]: # CpG-site: CpG#8 + [20250519_010254.]: # CpG-site: CpG#8 Cubic: Using a = -4.65730461761547e-05 , b = 0.00676525443932464 , c = 0.747405842988874 , d = 1.5233701793389 - [20250519_005421.]: Creating BiasCorrected (hyperbolic) plot No. 9 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG9_corrected_h.png - [20250519_005421.]: # CpG-site: CpG#9 + [20250519_010255.]: Creating BiasCorrected (hyperbolic) plot No. 9 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG9_corrected_h.png + [20250519_010255.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -1022.70071430348 , a = 1273.73160369723 , b = -1022.70071430348 , d = 1181.86620562781 - [20250519_005421.]: # CpG-site: CpG#9 + [20250519_010255.]: # CpG-site: CpG#9 Cubic: Using a = 4.24791805783875e-06 , b = -0.00144169825546529 , c = 1.09837792836035 , d = -0.978987254622136 - [20250519_005422.]: Creating BiasCorrected (hyperbolic) plot No. 10 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_rowmeans_corrected_h.png - [20250519_005422.]: # CpG-site: row_means + [20250519_010257.]: Creating BiasCorrected (hyperbolic) plot No. 10 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_rowmeans_corrected_h.png + [20250519_010257.]: # CpG-site: row_means Hyperbolic: Using bias_weight = -1166.73385465972 , a = 1740.64188131784 , b = -1166.73385465972 , d = 1653.86486365297 - [20250519_005422.]: # CpG-site: row_means + [20250519_010257.]: # CpG-site: row_means Cubic: Using a = -8.71187897501959e-06 , b = 0.000715556896368103 , c = 1.00248226010187 , d = -0.420549765740683 - [20250519_005425.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG1_corrected_h.png - [20250519_005426.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG2_corrected_h.png - [20250519_005428.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG3_corrected_h.png - [20250519_005430.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG4_corrected_h.png - [20250519_005432.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG5_corrected_h.png - [20250519_005433.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG6_corrected_h.png - [20250519_005435.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG7_corrected_h.png - [20250519_005437.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG8_corrected_h.png - [20250519_005439.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG9_corrected_h.png - [20250519_005440.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_rowmeans_corrected_h.png - [20250519_005442.]: Entered 'solving_equations'-Function - [20250519_005442.]: Solving cubic regression for CpG#1 + [20250519_010259.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG1_corrected_h.png + [20250519_010301.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG2_corrected_h.png + [20250519_010303.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG3_corrected_h.png + [20250519_010304.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG4_corrected_h.png + [20250519_010306.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG5_corrected_h.png + [20250519_010308.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG6_corrected_h.png + [20250519_010310.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG7_corrected_h.png + [20250519_010311.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG8_corrected_h.png + [20250519_010313.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG9_corrected_h.png + [20250519_010315.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_rowmeans_corrected_h.png + [20250519_010317.]: Entered 'solving_equations'-Function + [20250519_010317.]: Solving cubic regression for CpG#1 Coefficients: -1.03617340067344Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_005442.]: Samplename: 0 + [20250519_010317.]: Samplename: 0 Root: 1.334 --> Root in between the borders! Added to results. Coefficients: -8.34150673400678Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_005442.]: Samplename: 12.5 + [20250519_010317.]: Samplename: 12.5 Root: 11.446 --> Root in between the borders! Added to results. Coefficients: -15.3881734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_005442.]: Samplename: 25 + [20250519_010317.]: Samplename: 25 Root: 22.228 --> Root in between the borders! Added to results. Coefficients: -24.2801734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_005442.]: Samplename: 37.5 + [20250519_010317.]: Samplename: 37.5 Root: 36.374 --> Root in between the borders! Added to results. Coefficients: -34.9006734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_005442.]: Samplename: 50 + [20250519_010317.]: Samplename: 50 Root: 52.044 --> Root in between the borders! Added to results. Coefficients: -46.3541734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_005442.]: Samplename: 62.5 + [20250519_010317.]: Samplename: 62.5 Root: 66.144 --> Root in between the borders! Added to results. Coefficients: -55.8931734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_005442.]: Samplename: 75 + [20250519_010317.]: Samplename: 75 Root: 75.864 --> Root in between the borders! Added to results. Coefficients: -63.0981734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_005442.]: Samplename: 87.5 + [20250519_010317.]: Samplename: 87.5 Root: 82.254 --> Root in between the borders! Added to results. Coefficients: -91.0461734006735Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_005442.]: Samplename: 100 + [20250519_010317.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.877 --> '100 < root < 110' --> substitute 100 - [20250519_005442.]: Solving cubic regression for CpG#2 + [20250519_010317.]: Solving cubic regression for CpG#2 Coefficients: -0.283329966329966Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_005442.]: Samplename: 0 + [20250519_010317.]: Samplename: 0 Root: 0.549 --> Root in between the borders! Added to results. Coefficients: -6.33999663299663Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_005442.]: Samplename: 12.5 + [20250519_010317.]: Samplename: 12.5 Root: 11.533 --> Root in between the borders! Added to results. Coefficients: -15.93932996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_005442.]: Samplename: 25 + [20250519_010317.]: Samplename: 25 Root: 26.628 --> Root in between the borders! Added to results. Coefficients: -22.33732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_005442.]: Samplename: 37.5 + [20250519_010317.]: Samplename: 37.5 Root: 35.509 --> Root in between the borders! Added to results. Coefficients: -32.22832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_005442.]: Samplename: 50 + [20250519_010317.]: Samplename: 50 Root: 47.851 --> Root in between the borders! Added to results. Coefficients: -49.96332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_005442.]: Samplename: 62.5 + [20250519_010317.]: Samplename: 62.5 Root: 66.893 --> Root in between the borders! Added to results. Coefficients: -58.96582996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_005442.]: Samplename: 75 + [20250519_010317.]: Samplename: 75 Root: 75.431 --> Root in between the borders! Added to results. Coefficients: -68.8366632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_005442.]: Samplename: 87.5 + [20250519_010317.]: Samplename: 87.5 Root: 84.118 --> Root in between the borders! Added to results. Coefficients: -90.57732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_005442.]: Samplename: 100 + [20250519_010317.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.287 --> '100 < root < 110' --> substitute 100 - [20250519_005442.]: Solving cubic regression for CpG#3 + [20250519_010317.]: Solving cubic regression for CpG#3 Coefficients: -0.90294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_005442.]: Samplename: 0 + [20250519_010317.]: Samplename: 0 Root: 1.441 --> Root in between the borders! Added to results. Coefficients: -6.57294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_005442.]: Samplename: 12.5 + [20250519_010317.]: Samplename: 12.5 Root: 10.568 --> Root in between the borders! Added to results. Coefficients: -15.4289478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_005442.]: Samplename: 25 + [20250519_010317.]: Samplename: 25 Root: 24.796 --> Root in between the borders! Added to results. Coefficients: -22.6129478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_005442.]: Samplename: 37.5 + [20250519_010317.]: Samplename: 37.5 Root: 35.952 --> Root in between the borders! Added to results. Coefficients: -32.7754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_005442.]: Samplename: 50 + [20250519_010317.]: Samplename: 50 Root: 50.684 --> Root in between the borders! Added to results. Coefficients: -43.8889478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_005442.]: Samplename: 62.5 + [20250519_010317.]: Samplename: 62.5 Root: 65.142 --> Root in between the borders! Added to results. Coefficients: -54.9754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_005442.]: Samplename: 75 + [20250519_010317.]: Samplename: 75 Root: 77.905 --> Root in between the borders! Added to results. Coefficients: -57.6562811447812Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_005442.]: Samplename: 87.5 + [20250519_010317.]: Samplename: 87.5 Root: 80.767 --> Root in between the borders! Added to results. Coefficients: -80.6649478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_005442.]: Samplename: 100 + [20250519_010317.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.38 --> '100 < root < 110' --> substitute 100 - [20250519_005442.]: Solving cubic regression for CpG#4 + [20250519_010317.]: Solving cubic regression for CpG#4 Coefficients: -0.597449494949524Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_005442.]: Samplename: 0 + [20250519_010317.]: Samplename: 0 Root: 0.858 --> Root in between the borders! Added to results. Coefficients: -8.25278282828286Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_005442.]: Samplename: 12.5 + [20250519_010317.]: Samplename: 12.5 Root: 12.086 --> Root in between the borders! Added to results. Coefficients: -15.8034494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_005442.]: Samplename: 25 + [20250519_010317.]: Samplename: 25 Root: 23.316 --> Root in between the borders! Added to results. Coefficients: -25.5274494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_005442.]: Samplename: 37.5 + [20250519_010317.]: Samplename: 37.5 Root: 37.383 --> Root in between the borders! Added to results. Coefficients: -33.6369494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_005442.]: Samplename: 50 + [20250519_010317.]: Samplename: 50 Root: 48.353 --> Root in between the borders! Added to results. Coefficients: -50.2554494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_005442.]: Samplename: 62.5 + [20250519_010317.]: Samplename: 62.5 Root: 68.082 --> Root in between the borders! Added to results. Coefficients: -56.5394494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_005442.]: Samplename: 75 + [20250519_010317.]: Samplename: 75 Root: 74.615 --> Root in between the borders! Added to results. Coefficients: -65.5927828282829Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_005442.]: Samplename: 87.5 + [20250519_010317.]: Samplename: 87.5 Root: 83.254 --> Root in between the borders! Added to results. Coefficients: -88.3214494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_005442.]: Samplename: 100 + [20250519_010317.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.715 --> '100 < root < 110' --> substitute 100 - [20250519_005442.]: Solving cubic regression for CpG#5 + [20250519_010317.]: Solving cubic regression for CpG#5 Coefficients: -0.623961279461278Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_005442.]: Samplename: 0 + [20250519_010317.]: Samplename: 0 Root: 1.458 --> Root in between the borders! Added to results. Coefficients: -4.76796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_005442.]: Samplename: 12.5 + [20250519_010317.]: Samplename: 12.5 Root: 10.347 --> Root in between the borders! Added to results. Coefficients: -12.7259612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_005442.]: Samplename: 25 + [20250519_010317.]: Samplename: 25 Root: 24.815 --> Root in between the borders! Added to results. Coefficients: -21.1599612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_005442.]: Samplename: 37.5 + [20250519_010317.]: Samplename: 37.5 Root: 37.902 --> Root in between the borders! Added to results. Coefficients: -30.6954612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_005442.]: Samplename: 50 + [20250519_010317.]: Samplename: 50 Root: 50.977 --> Root in between the borders! Added to results. Coefficients: -39.6579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_005442.]: Samplename: 62.5 + [20250519_010317.]: Samplename: 62.5 Root: 62.126 --> Root in between the borders! Added to results. Coefficients: -51.6829612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_005442.]: Samplename: 75 + [20250519_010317.]: Samplename: 75 Root: 75.852 --> Root in between the borders! Added to results. Coefficients: -61.0146279461279Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_005442.]: Samplename: 87.5 + [20250519_010317.]: Samplename: 87.5 Root: 85.767 --> Root in between the borders! Added to results. Coefficients: -76.0699612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_005442.]: Samplename: 100 + [20250519_010317.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.743 --> '100 < root < 110' --> substitute 100 - [20250519_005442.]: Solving cubic regression for CpG#6 + [20250519_010317.]: Solving cubic regression for CpG#6 Coefficients: -0.196072390572403Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_005442.]: Samplename: 0 + [20250519_010317.]: Samplename: 0 Root: 0.349 --> Root in between the borders! Added to results. Coefficients: -6.73873905723907Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_005442.]: Samplename: 12.5 + [20250519_010317.]: Samplename: 12.5 Root: 11.718 --> Root in between the borders! Added to results. Coefficients: -15.8880723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_005442.]: Samplename: 25 + [20250519_010317.]: Samplename: 25 Root: 26.396 --> Root in between the borders! Added to results. Coefficients: -22.0000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_005442.]: Samplename: 37.5 + [20250519_010317.]: Samplename: 37.5 Root: 35.301 --> Root in between the borders! Added to results. Coefficients: -33.4445723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_005442.]: Samplename: 50 + [20250519_010317.]: Samplename: 50 Root: 50.134 --> Root in between the borders! Added to results. Coefficients: -46.9000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_005442.]: Samplename: 62.5 + [20250519_010317.]: Samplename: 62.5 Root: 64.993 --> Root in between the borders! Added to results. Coefficients: -55.8320723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_005442.]: Samplename: 75 + [20250519_010317.]: Samplename: 75 Root: 73.639 --> Root in between the borders! Added to results. Coefficients: -71.5454057239057Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_005442.]: Samplename: 87.5 + [20250519_010317.]: Samplename: 87.5 Root: 87.043 --> Root in between the borders! Added to results. Coefficients: -89.6560723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_005442.]: Samplename: 100 + [20250519_010317.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.329 --> '100 < root < 110' --> substitute 100 - [20250519_005442.]: Solving cubic regression for CpG#7 + [20250519_010317.]: Solving cubic regression for CpG#7 Coefficients: -1.21495454545456Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_005442.]: Samplename: 0 + [20250519_010317.]: Samplename: 0 Root: 2.13 --> Root in between the borders! Added to results. Coefficients: -5.39562121212123Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_005442.]: Samplename: 12.5 + [20250519_010317.]: Samplename: 12.5 Root: 9.973 --> Root in between the borders! Added to results. Coefficients: -11.2649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_005442.]: Samplename: 25 + [20250519_010317.]: Samplename: 25 Root: 22.206 --> Root in between the borders! Added to results. Coefficients: -17.4509545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_005442.]: Samplename: 37.5 + [20250519_010317.]: Samplename: 37.5 Root: 35.814 --> Root in between the borders! Added to results. Coefficients: -26.0314545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_005442.]: Samplename: 50 + [20250519_010317.]: Samplename: 50 Root: 53.28 --> Root in between the borders! Added to results. Coefficients: -33.9649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_005442.]: Samplename: 62.5 + [20250519_010317.]: Samplename: 62.5 Root: 66.598 --> Root in between the borders! Added to results. Coefficients: -41.1689545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_005442.]: Samplename: 75 + [20250519_010317.]: Samplename: 75 Root: 76.575 --> Root in between the borders! Added to results. Coefficients: -44.1356212121212Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_005442.]: Samplename: 87.5 + [20250519_010317.]: Samplename: 87.5 Root: 80.219 --> Root in between the borders! Added to results. Coefficients: -67.2229545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_005442.]: Samplename: 100 + [20250519_010317.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.506 --> '100 < root < 110' --> substitute 100 - [20250519_005442.]: Solving cubic regression for CpG#8 + [20250519_010317.]: Solving cubic regression for CpG#8 Coefficients: -1.09618518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_005442.]: Samplename: 0 + [20250519_010317.]: Samplename: 0 Root: 2.016 --> Root in between the borders! Added to results. Coefficients: -5.44685185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_005442.]: Samplename: 12.5 + [20250519_010317.]: Samplename: 12.5 Root: 9.458 --> Root in between the borders! Added to results. Coefficients: -16.9301851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_005442.]: Samplename: 25 + [20250519_010317.]: Samplename: 25 Root: 26.35 --> Root in between the borders! Added to results. Coefficients: -23.3501851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_005442.]: Samplename: 37.5 + [20250519_010317.]: Samplename: 37.5 Root: 34.728 --> Root in between the borders! Added to results. Coefficients: -37.6251851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_005442.]: Samplename: 50 + [20250519_010317.]: Samplename: 50 Root: 51.781 --> Root in between the borders! Added to results. Coefficients: -48.8261851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_005442.]: Samplename: 62.5 + [20250519_010317.]: Samplename: 62.5 Root: 64.192 --> Root in between the borders! Added to results. Coefficients: -61.6676851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_005442.]: Samplename: 75 + [20250519_010317.]: Samplename: 75 Root: 77.804 --> Root in between the borders! Added to results. Coefficients: -64.5101851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_005442.]: Samplename: 87.5 + [20250519_010317.]: Samplename: 87.5 Root: 80.758 --> Root in between the borders! Added to results. Coefficients: -86.0601851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_005442.]: Samplename: 100 + [20250519_010317.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.834 --> '100 < root < 110' --> substitute 100 - [20250519_005442.]: Solving cubic regression for CpG#9 + [20250519_010317.]: Solving cubic regression for CpG#9 Coefficients: -0.989865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_005442.]: Samplename: 0 + [20250519_010317.]: Samplename: 0 Root: 1.475 --> Root in between the borders! Added to results. Coefficients: -6.39586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_005442.]: Samplename: 12.5 + [20250519_010317.]: Samplename: 12.5 Root: 10.12 --> Root in between the borders! Added to results. Coefficients: -14.7058653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_005442.]: Samplename: 25 + [20250519_010317.]: Samplename: 25 Root: 24.844 --> Root in between the borders! Added to results. Coefficients: -20.6238653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_005442.]: Samplename: 37.5 + [20250519_010317.]: Samplename: 37.5 Root: 35.327 --> Root in between the borders! Added to results. Coefficients: -31.3958653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_005442.]: Samplename: 50 + [20250519_010317.]: Samplename: 50 Root: 51.855 --> Root in between the borders! Added to results. Coefficients: -42.6858653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_005442.]: Samplename: 62.5 + [20250519_010317.]: Samplename: 62.5 Root: 65.265 --> Root in between the borders! Added to results. Coefficients: -52.9033653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_005442.]: Samplename: 75 + [20250519_010317.]: Samplename: 75 Root: 74.915 --> Root in between the borders! Added to results. Coefficients: -65.492531986532Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_005442.]: Samplename: 87.5 + [20250519_010317.]: Samplename: 87.5 Root: 84.67 --> Root in between the borders! Added to results. Coefficients: -92.9898653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_005442.]: Samplename: 100 + [20250519_010317.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.082 --> '100 < root < 110' --> substitute 100 - [20250519_005442.]: Solving cubic regression for row_means + [20250519_010317.]: Solving cubic regression for row_means Coefficients: -0.771215488215478Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_005442.]: Samplename: 0 + [20250519_010317.]: Samplename: 0 Root: 1.287 --> Root in between the borders! Added to results. Coefficients: -6.47247474747474Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_005442.]: Samplename: 12.5 + [20250519_010317.]: Samplename: 12.5 Root: 10.847 --> Root in between the borders! Added to results. Coefficients: -14.8972154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_005442.]: Samplename: 25 + [20250519_010317.]: Samplename: 25 Root: 24.737 --> Root in between the borders! Added to results. Coefficients: -22.1492154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_005442.]: Samplename: 37.5 + [20250519_010317.]: Samplename: 37.5 Root: 36.02 --> Root in between the borders! Added to results. Coefficients: -32.5259932659933Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_005442.]: Samplename: 50 + [20250519_010317.]: Samplename: 50 Root: 50.639 --> Root in between the borders! Added to results. Coefficients: -44.7218821548821Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_005442.]: Samplename: 62.5 + [20250519_010317.]: Samplename: 62.5 Root: 65.497 --> Root in between the borders! Added to results. Coefficients: -54.4032154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_005442.]: Samplename: 75 + [20250519_010317.]: Samplename: 75 Root: 75.751 --> Root in between the borders! Added to results. Coefficients: -62.4313636363636Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_005442.]: Samplename: 87.5 + [20250519_010317.]: Samplename: 87.5 Root: 83.403 --> Root in between the borders! Added to results. Coefficients: -84.7343265993266Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_005442.]: Samplename: 100 + [20250519_010317.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.573 --> '100 < root < 110' --> substitute 100 - [20250519_005442.]: + [20250519_010317.]: ### Starting with regression calculations ### - [20250519_005442.]: Entered 'regression_type1'-Function + [20250519_010317.]: Entered 'regression_type1'-Function Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient Error in (function (formula, data = parent.frame(), start, control = nls.control(), : @@ -18382,3028 +18382,3028 @@ step factor 0.000488281 reduced below 'minFactor' of 0.000976562 Error in (function (formula, data = parent.frame(), start, control = nls.control(), : singular gradient - [20250519_005443.]: # CpG-site: CpG#1 + [20250519_010317.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) - [20250519_005443.]: Logging df_agg: CpG#1 - [20250519_005443.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005443.]: c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)[20250519_005443.]: c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)[20250519_005443.]: c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) - [20250519_005443.]: Entered 'hyperbolic_regression'-Function - [20250519_005443.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005443.]: Entered 'cubic_regression'-Function - [20250519_005443.]: 'cubic_regression': minmax = FALSE - [20250519_005443.]: # CpG-site: CpG#2 + [20250519_010317.]: Logging df_agg: CpG#1 + [20250519_010317.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010317.]: c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)[20250519_010317.]: c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)[20250519_010317.]: c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) + [20250519_010317.]: Entered 'hyperbolic_regression'-Function + [20250519_010317.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010317.]: Entered 'cubic_regression'-Function + [20250519_010317.]: 'cubic_regression': minmax = FALSE + [20250519_010317.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) - [20250519_005443.]: Logging df_agg: CpG#2 - [20250519_005443.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005443.]: c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)[20250519_005443.]: c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)[20250519_005443.]: c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) - [20250519_005443.]: Entered 'hyperbolic_regression'-Function - [20250519_005443.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005443.]: Entered 'cubic_regression'-Function - [20250519_005443.]: 'cubic_regression': minmax = FALSE - [20250519_005443.]: # CpG-site: CpG#3 + [20250519_010317.]: Logging df_agg: CpG#2 + [20250519_010317.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010317.]: c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)[20250519_010317.]: c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)[20250519_010317.]: c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) + [20250519_010317.]: Entered 'hyperbolic_regression'-Function + [20250519_010317.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010318.]: Entered 'cubic_regression'-Function + [20250519_010318.]: 'cubic_regression': minmax = FALSE + [20250519_010318.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) - [20250519_005443.]: Logging df_agg: CpG#3 - [20250519_005443.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005443.]: c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)[20250519_005443.]: c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)[20250519_005443.]: c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) - [20250519_005443.]: Entered 'hyperbolic_regression'-Function - [20250519_005443.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005443.]: Entered 'cubic_regression'-Function - [20250519_005443.]: 'cubic_regression': minmax = FALSE - [20250519_005443.]: # CpG-site: CpG#4 + [20250519_010318.]: Logging df_agg: CpG#3 + [20250519_010318.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010318.]: c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)[20250519_010318.]: c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)[20250519_010318.]: c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) + [20250519_010318.]: Entered 'hyperbolic_regression'-Function + [20250519_010318.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010318.]: Entered 'cubic_regression'-Function + [20250519_010318.]: 'cubic_regression': minmax = FALSE + [20250519_010318.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) - [20250519_005443.]: Logging df_agg: CpG#4 - [20250519_005443.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005443.]: c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)[20250519_005443.]: c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)[20250519_005443.]: c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) - [20250519_005443.]: Entered 'hyperbolic_regression'-Function - [20250519_005443.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005444.]: Entered 'cubic_regression'-Function - [20250519_005444.]: 'cubic_regression': minmax = FALSE - [20250519_005444.]: # CpG-site: CpG#5 + [20250519_010318.]: Logging df_agg: CpG#4 + [20250519_010318.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010318.]: c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)[20250519_010318.]: c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)[20250519_010318.]: c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) + [20250519_010318.]: Entered 'hyperbolic_regression'-Function + [20250519_010318.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010318.]: Entered 'cubic_regression'-Function + [20250519_010318.]: 'cubic_regression': minmax = FALSE + [20250519_010318.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) - [20250519_005444.]: Logging df_agg: CpG#5 - [20250519_005444.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005444.]: c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)[20250519_005444.]: c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)[20250519_005444.]: c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) - [20250519_005444.]: Entered 'hyperbolic_regression'-Function - [20250519_005444.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005444.]: Entered 'cubic_regression'-Function - [20250519_005444.]: 'cubic_regression': minmax = FALSE - [20250519_005444.]: # CpG-site: CpG#6 + [20250519_010318.]: Logging df_agg: CpG#5 + [20250519_010318.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010318.]: c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)[20250519_010318.]: c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)[20250519_010318.]: c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) + [20250519_010318.]: Entered 'hyperbolic_regression'-Function + [20250519_010318.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010318.]: Entered 'cubic_regression'-Function + [20250519_010318.]: 'cubic_regression': minmax = FALSE + [20250519_010318.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) - [20250519_005444.]: Logging df_agg: CpG#6 - [20250519_005444.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005444.]: c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)[20250519_005444.]: c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)[20250519_005444.]: c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) - [20250519_005444.]: Entered 'hyperbolic_regression'-Function - [20250519_005444.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005444.]: Entered 'cubic_regression'-Function - [20250519_005444.]: 'cubic_regression': minmax = FALSE - [20250519_005444.]: # CpG-site: CpG#7 + [20250519_010318.]: Logging df_agg: CpG#6 + [20250519_010318.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010318.]: c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)[20250519_010318.]: c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)[20250519_010318.]: c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) + [20250519_010318.]: Entered 'hyperbolic_regression'-Function + [20250519_010318.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010318.]: Entered 'cubic_regression'-Function + [20250519_010318.]: 'cubic_regression': minmax = FALSE + [20250519_010318.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) - [20250519_005444.]: Logging df_agg: CpG#7 - [20250519_005444.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005444.]: c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)[20250519_005444.]: c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)[20250519_005444.]: c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) - [20250519_005444.]: Entered 'hyperbolic_regression'-Function - [20250519_005444.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005444.]: Entered 'cubic_regression'-Function - [20250519_005444.]: 'cubic_regression': minmax = FALSE - [20250519_005444.]: # CpG-site: CpG#8 + [20250519_010318.]: Logging df_agg: CpG#7 + [20250519_010318.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010318.]: c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)[20250519_010318.]: c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)[20250519_010318.]: c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) + [20250519_010318.]: Entered 'hyperbolic_regression'-Function + [20250519_010318.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010319.]: Entered 'cubic_regression'-Function + [20250519_010319.]: 'cubic_regression': minmax = FALSE + [20250519_010319.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) - [20250519_005444.]: Logging df_agg: CpG#8 - [20250519_005444.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005444.]: c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)[20250519_005444.]: c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)[20250519_005444.]: c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) - [20250519_005444.]: Entered 'hyperbolic_regression'-Function - [20250519_005444.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005445.]: Entered 'cubic_regression'-Function - [20250519_005445.]: 'cubic_regression': minmax = FALSE - [20250519_005445.]: # CpG-site: CpG#9 + [20250519_010319.]: Logging df_agg: CpG#8 + [20250519_010319.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010319.]: c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)[20250519_010319.]: c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)[20250519_010319.]: c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) + [20250519_010319.]: Entered 'hyperbolic_regression'-Function + [20250519_010319.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010319.]: Entered 'cubic_regression'-Function + [20250519_010319.]: 'cubic_regression': minmax = FALSE + [20250519_010319.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) - [20250519_005445.]: Logging df_agg: CpG#9 - [20250519_005445.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005445.]: c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)[20250519_005445.]: c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)[20250519_005445.]: c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) - [20250519_005445.]: Entered 'hyperbolic_regression'-Function - [20250519_005445.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005445.]: Entered 'cubic_regression'-Function - [20250519_005445.]: 'cubic_regression': minmax = FALSE - [20250519_005445.]: # CpG-site: row_means + [20250519_010319.]: Logging df_agg: CpG#9 + [20250519_010319.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010319.]: c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)[20250519_010319.]: c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)[20250519_010319.]: c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) + [20250519_010319.]: Entered 'hyperbolic_regression'-Function + [20250519_010319.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010319.]: Entered 'cubic_regression'-Function + [20250519_010319.]: 'cubic_regression': minmax = FALSE + [20250519_010319.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) - [20250519_005445.]: Logging df_agg: row_means - [20250519_005445.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005445.]: c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)[20250519_005445.]: c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)[20250519_005445.]: c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) - [20250519_005445.]: Entered 'hyperbolic_regression'-Function - [20250519_005445.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005445.]: Entered 'cubic_regression'-Function - [20250519_005445.]: 'cubic_regression': minmax = FALSE - [20250519_005445.]: ### Starting with plotting ### - [20250519_005446.]: Creating BiasCorrected (cubic) plot No. 1 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG1_corrected_c.png - [20250519_005446.]: # CpG-site: CpG#1 + [20250519_010319.]: Logging df_agg: row_means + [20250519_010319.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010319.]: c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)[20250519_010319.]: c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)[20250519_010319.]: c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) + [20250519_010319.]: Entered 'hyperbolic_regression'-Function + [20250519_010319.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010319.]: Entered 'cubic_regression'-Function + [20250519_010319.]: 'cubic_regression': minmax = FALSE + [20250519_010320.]: ### Starting with plotting ### + [20250519_010320.]: Creating BiasCorrected (cubic) plot No. 1 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG1_corrected_c.png + [20250519_010320.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -1164.78037112998 , a = 2039.49073397227 , b = -1164.78037112998 , d = 1955.827713817 - [20250519_005446.]: # CpG-site: CpG#1 + [20250519_010320.]: # CpG-site: CpG#1 Cubic: Using a = -3.42821196240366e-05 , b = 0.00462173155013798 , c = 0.850326258865826 , d = 0.500073874211125 - [20250519_005447.]: Creating BiasCorrected (cubic) plot No. 2 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG2_corrected_c.png - [20250519_005447.]: # CpG-site: CpG#2 + [20250519_010322.]: Creating BiasCorrected (cubic) plot No. 2 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG2_corrected_c.png + [20250519_010322.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = -464.14479041434 , a = 4988.85430873436 , b = -464.14479041434 , d = 4925.32709283236 - [20250519_005447.]: # CpG-site: CpG#2 + [20250519_010322.]: # CpG-site: CpG#2 Cubic: Using a = -1.40811796906314e-05 , b = 0.00190849431754485 , c = 0.933455870941473 , d = 0.363096210099674 - [20250519_005449.]: Creating BiasCorrected (cubic) plot No. 3 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG3_corrected_c.png - [20250519_005449.]: # CpG-site: CpG#3 + [20250519_010324.]: Creating BiasCorrected (cubic) plot No. 3 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG3_corrected_c.png + [20250519_010324.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = -745.616753604673 , a = 2026.5285182947 , b = -745.616753604673 , d = 1953.27811759735 - [20250519_005449.]: # CpG-site: CpG#3 + [20250519_010324.]: # CpG-site: CpG#3 Cubic: Using a = -2.78835051857186e-05 , b = 0.00366863707889089 , c = 0.880665171220893 , d = 0.510779308833653 - [20250519_005451.]: Creating BiasCorrected (cubic) plot No. 4 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG4_corrected_c.png - [20250519_005451.]: # CpG-site: CpG#4 + [20250519_010325.]: Creating BiasCorrected (cubic) plot No. 4 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG4_corrected_c.png + [20250519_010325.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = -833.077868829112 , a = 2871.13688289992 , b = -833.077868829112 , d = 2796.8115417971 - [20250519_005451.]: # CpG-site: CpG#4 + [20250519_010325.]: # CpG-site: CpG#4 Cubic: Using a = -2.30028865643e-05 , b = 0.00309017851940692 , c = 0.897073107777645 , d = 0.443455761841866 - [20250519_005453.]: Creating BiasCorrected (cubic) plot No. 5 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG5_corrected_c.png - [20250519_005453.]: # CpG-site: CpG#5 + [20250519_010327.]: Creating BiasCorrected (cubic) plot No. 5 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG5_corrected_c.png + [20250519_010327.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = 222.093251203934 , a = 12477.4109611091 , b = 222.093251203934 , d = 12432.0779293841 - [20250519_005453.]: # CpG-site: CpG#5 + [20250519_010327.]: # CpG-site: CpG#5 Cubic: Using a = -1.28632696989134e-05 , b = 0.00184822285299014 , c = 0.931001046631176 , d = 0.437963095185599 - [20250519_005455.]: Creating BiasCorrected (cubic) plot No. 6 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG6_corrected_c.png - [20250519_005455.]: # CpG-site: CpG#6 + [20250519_010329.]: Creating BiasCorrected (cubic) plot No. 6 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG6_corrected_c.png + [20250519_010329.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = -700.460688613084 , a = 21299.059407053 , b = -700.460688613084 , d = 21234.4652853188 - [20250519_005455.]: # CpG-site: CpG#6 + [20250519_010329.]: # CpG-site: CpG#6 Cubic: Using a = -5.838335151375e-06 , b = 0.000828447163218526 , c = 0.970047764475166 , d = 0.158074701412251 - [20250519_005457.]: Creating BiasCorrected (cubic) plot No. 7 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG7_corrected_c.png - [20250519_005457.]: # CpG-site: CpG#7 + [20250519_010331.]: Creating BiasCorrected (cubic) plot No. 7 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG7_corrected_c.png + [20250519_010331.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -1144.91191228206 , a = 1477.91098401837 , b = -1144.91191228206 , d = 1394.69940178972 - [20250519_005457.]: # CpG-site: CpG#7 + [20250519_010331.]: # CpG-site: CpG#7 Cubic: Using a = -4.54220535614047e-05 , b = 0.00607993130565131 , c = 0.805411493452389 , d = 0.615125382790254 - [20250519_005458.]: Creating BiasCorrected (cubic) plot No. 8 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG8_corrected_c.png - [20250519_005458.]: # CpG-site: CpG#8 + [20250519_010333.]: Creating BiasCorrected (cubic) plot No. 8 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG8_corrected_c.png + [20250519_010333.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = -349.37367288786 , a = 2377.38756952122 , b = -349.37367288786 , d = 2315.21494778767 - [20250519_005458.]: # CpG-site: CpG#8 + [20250519_010333.]: # CpG-site: CpG#8 Cubic: Using a = -2.64622306669639e-05 , b = 0.00353621593528015 , c = 0.877916844709074 , d = 0.698684606493051 - [20250519_005500.]: Creating BiasCorrected (cubic) plot No. 9 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG9_corrected_c.png - [20250519_005500.]: # CpG-site: CpG#9 + [20250519_010335.]: Creating BiasCorrected (cubic) plot No. 9 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG9_corrected_c.png + [20250519_010335.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -1536.91699417279 , a = 3745.17619112231 , b = -1536.91699417279 , d = 3656.26801968932 - [20250519_005500.]: # CpG-site: CpG#9 + [20250519_010335.]: # CpG-site: CpG#9 Cubic: Using a = -2.70976130611725e-05 , b = 0.00378478247182342 , c = 0.871825263051793 , d = 0.455475740974113 - [20250519_005502.]: Creating BiasCorrected (cubic) plot No. 10 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_rowmeans_corrected_c.png - [20250519_005502.]: # CpG-site: row_means + [20250519_010336.]: Creating BiasCorrected (cubic) plot No. 10 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_rowmeans_corrected_c.png + [20250519_010336.]: # CpG-site: row_means Hyperbolic: Using bias_weight = -820.643805549713 , a = 3198.46619445775 , b = -820.643805549713 , d = 3124.21083989374 - [20250519_005502.]: # CpG-site: row_means + [20250519_010336.]: # CpG-site: row_means Cubic: Using a = -2.34328362100009e-05 , b = 0.00319139073176266 , c = 0.891842168032129 , d = 0.493517689787135 - [20250519_005504.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG1_corrected_c.png - [20250519_005506.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG2_corrected_c.png - [20250519_005508.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG3_corrected_c.png - [20250519_005510.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG4_corrected_c.png - [20250519_005511.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG5_corrected_c.png - [20250519_005513.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG6_corrected_c.png - [20250519_005515.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG7_corrected_c.png - [20250519_005517.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG8_corrected_c.png - [20250519_005518.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG9_corrected_c.png - [20250519_005520.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_rowmeans_corrected_c.png - [20250519_005522.]: Entered 'solving_equations'-Function - [20250519_005522.]: Solving hyperbolic regression for CpG#1 + [20250519_010338.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG1_corrected_c.png + [20250519_010340.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG2_corrected_c.png + [20250519_010342.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG3_corrected_c.png + [20250519_010344.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG4_corrected_c.png + [20250519_010345.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG5_corrected_c.png + [20250519_010347.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG6_corrected_c.png + [20250519_010349.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG7_corrected_c.png + [20250519_010351.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG8_corrected_c.png + [20250519_010353.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG9_corrected_c.png + [20250519_010354.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_rowmeans_corrected_c.png + [20250519_010356.]: Entered 'solving_equations'-Function + [20250519_010356.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 79.867338305743 - [20250519_005522.]: Samplename: Sample#1 + [20250519_010356.]: Samplename: Sample#1 Root: 79.867 --> Root in between the borders! Added to results. Hyperbolic solved: 29.7900144307253 - [20250519_005522.]: Samplename: Sample#10 + [20250519_010356.]: Samplename: Sample#10 Root: 29.79 --> Root in between the borders! Added to results. Hyperbolic solved: 41.6525328583179 - [20250519_005522.]: Samplename: Sample#2 + [20250519_010356.]: Samplename: Sample#2 Root: 41.653 --> Root in between the borders! Added to results. Hyperbolic solved: 57.4651978180159 - [20250519_005522.]: Samplename: Sample#3 + [20250519_010356.]: Samplename: Sample#3 Root: 57.465 --> Root in between the borders! Added to results. Hyperbolic solved: 9.20072297150156 - [20250519_005522.]: Samplename: Sample#4 + [20250519_010356.]: Samplename: Sample#4 Root: 9.201 --> Root in between the borders! Added to results. Hyperbolic solved: 21.8059605790798 - [20250519_005522.]: Samplename: Sample#5 + [20250519_010356.]: Samplename: Sample#5 Root: 21.806 --> Root in between the borders! Added to results. Hyperbolic solved: 23.0837964625959 - [20250519_005522.]: Samplename: Sample#6 + [20250519_010356.]: Samplename: Sample#6 Root: 23.084 --> Root in between the borders! Added to results. Hyperbolic solved: 45.5034148455809 - [20250519_005522.]: Samplename: Sample#7 + [20250519_010356.]: Samplename: Sample#7 Root: 45.503 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6987854360424 - [20250519_005522.]: Samplename: Sample#8 + [20250519_010356.]: Samplename: Sample#8 Root: 85.699 --> Root in between the borders! Added to results. Hyperbolic solved: -3.66510576608508 - [20250519_005522.]: Samplename: Sample#9 + [20250519_010356.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -3.665 --> '-10 < root < 0' --> substitute 0 - [20250519_005522.]: Solving hyperbolic regression for CpG#2 + [20250519_010356.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 76.7227930253137 - [20250519_005522.]: Samplename: Sample#1 + [20250519_010356.]: Samplename: Sample#1 Root: 76.723 --> Root in between the borders! Added to results. Hyperbolic solved: 30.9924274267338 - [20250519_005522.]: Samplename: Sample#10 + [20250519_010356.]: Samplename: Sample#10 Root: 30.992 --> Root in between the borders! Added to results. Hyperbolic solved: 42.6810881616502 - [20250519_005522.]: Samplename: Sample#2 + [20250519_010356.]: Samplename: Sample#2 Root: 42.681 --> Root in between the borders! Added to results. Hyperbolic solved: 58.93379071467 - [20250519_005522.]: Samplename: Sample#3 + [20250519_010356.]: Samplename: Sample#3 Root: 58.934 --> Root in between the borders! Added to results. Hyperbolic solved: 4.98020463922415 - [20250519_005522.]: Samplename: Sample#4 + [20250519_010356.]: Samplename: Sample#4 Root: 4.98 --> Root in between the borders! Added to results. Hyperbolic solved: 20.2480263316954 - [20250519_005522.]: Samplename: Sample#5 + [20250519_010356.]: Samplename: Sample#5 Root: 20.248 --> Root in between the borders! Added to results. Hyperbolic solved: 17.8133085422138 - [20250519_005522.]: Samplename: Sample#6 + [20250519_010356.]: Samplename: Sample#6 Root: 17.813 --> Root in between the borders! Added to results. Hyperbolic solved: 40.917674420917 - [20250519_005522.]: Samplename: Sample#7 + [20250519_010356.]: Samplename: Sample#7 Root: 40.918 --> Root in between the borders! Added to results. Hyperbolic solved: 86.045071534744 - [20250519_005522.]: Samplename: Sample#8 + [20250519_010356.]: Samplename: Sample#8 Root: 86.045 --> Root in between the borders! Added to results. Hyperbolic solved: 3.1157977862424 - [20250519_005522.]: Samplename: Sample#9 + [20250519_010356.]: Samplename: Sample#9 Root: 3.116 --> Root in between the borders! Added to results. - [20250519_005522.]: Solving hyperbolic regression for CpG#3 + [20250519_010356.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 74.9349278251272 - [20250519_005522.]: Samplename: Sample#1 + [20250519_010356.]: Samplename: Sample#1 Root: 74.935 --> Root in between the borders! Added to results. Hyperbolic solved: 27.6844390885298 - [20250519_005522.]: Samplename: Sample#10 + [20250519_010356.]: Samplename: Sample#10 Root: 27.684 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8520223561161 - [20250519_005522.]: Samplename: Sample#2 + [20250519_010356.]: Samplename: Sample#2 Root: 41.852 --> Root in between the borders! Added to results. Hyperbolic solved: 55.8325219766746 - [20250519_005522.]: Samplename: Sample#3 + [20250519_010356.]: Samplename: Sample#3 Root: 55.833 --> Root in between the borders! Added to results. Hyperbolic solved: 8.03518759540723 - [20250519_005522.]: Samplename: Sample#4 + [20250519_010356.]: Samplename: Sample#4 Root: 8.035 --> Root in between the borders! Added to results. Hyperbolic solved: 24.1066316660805 - [20250519_005522.]: Samplename: Sample#5 + [20250519_010356.]: Samplename: Sample#5 Root: 24.107 --> Root in between the borders! Added to results. Hyperbolic solved: 26.2419826081677 - [20250519_005522.]: Samplename: Sample#6 + [20250519_010356.]: Samplename: Sample#6 Root: 26.242 --> Root in between the borders! Added to results. Hyperbolic solved: 44.0944957314077 - [20250519_005522.]: Samplename: Sample#7 + [20250519_010356.]: Samplename: Sample#7 Root: 44.094 --> Root in between the borders! Added to results. Hyperbolic solved: 85.8279384942248 - [20250519_005522.]: Samplename: Sample#8 + [20250519_010356.]: Samplename: Sample#8 Root: 85.828 --> Root in between the borders! Added to results. Hyperbolic solved: -0.66649088715512 - [20250519_005522.]: Samplename: Sample#9 + [20250519_010356.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.666 --> '-10 < root < 0' --> substitute 0 - [20250519_005522.]: Solving hyperbolic regression for CpG#4 + [20250519_010356.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 76.349525731503 - [20250519_005522.]: Samplename: Sample#1 + [20250519_010356.]: Samplename: Sample#1 Root: 76.35 --> Root in between the borders! Added to results. Hyperbolic solved: 28.2568543207918 - [20250519_005522.]: Samplename: Sample#10 + [20250519_010356.]: Samplename: Sample#10 Root: 28.257 --> Root in between the borders! Added to results. Hyperbolic solved: 43.408980967851 - [20250519_005522.]: Samplename: Sample#2 + [20250519_010356.]: Samplename: Sample#2 Root: 43.409 --> Root in between the borders! Added to results. Hyperbolic solved: 58.5435202418955 - [20250519_005522.]: Samplename: Sample#3 + [20250519_010356.]: Samplename: Sample#3 Root: 58.544 --> Root in between the borders! Added to results. Hyperbolic solved: 10.308707716736 - [20250519_005522.]: Samplename: Sample#4 + [20250519_010356.]: Samplename: Sample#4 Root: 10.309 --> Root in between the borders! Added to results. Hyperbolic solved: 22.1830453155629 - [20250519_005522.]: Samplename: Sample#5 + [20250519_010356.]: Samplename: Sample#5 Root: 22.183 --> Root in between the borders! Added to results. Hyperbolic solved: 27.133776120676 - [20250519_005522.]: Samplename: Sample#6 + [20250519_010356.]: Samplename: Sample#6 Root: 27.134 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8321067699793 - [20250519_005522.]: Samplename: Sample#7 + [20250519_010356.]: Samplename: Sample#7 Root: 41.832 --> Root in between the borders! Added to results. Hyperbolic solved: 85.6890182703099 - [20250519_005522.]: Samplename: Sample#8 + [20250519_010356.]: Samplename: Sample#8 Root: 85.689 --> Root in between the borders! Added to results. Hyperbolic solved: 2.42232661674155 - [20250519_005522.]: Samplename: Sample#9 + [20250519_010356.]: Samplename: Sample#9 Root: 2.422 --> Root in between the borders! Added to results. - [20250519_005522.]: Solving hyperbolic regression for CpG#5 + [20250519_010356.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 72.8126911247073 - [20250519_005522.]: Samplename: Sample#1 + [20250519_010356.]: Samplename: Sample#1 Root: 72.813 --> Root in between the borders! Added to results. Hyperbolic solved: 26.4206935777278 - [20250519_005522.]: Samplename: Sample#10 + [20250519_010356.]: Samplename: Sample#10 Root: 26.421 --> Root in between the borders! Added to results. Hyperbolic solved: 44.2738761535648 - [20250519_005522.]: Samplename: Sample#2 + [20250519_010356.]: Samplename: Sample#2 Root: 44.274 --> Root in between the borders! Added to results. Hyperbolic solved: 53.5324382038438 - [20250519_005522.]: Samplename: Sample#3 + [20250519_010356.]: Samplename: Sample#3 Root: 53.532 --> Root in between the borders! Added to results. Hyperbolic solved: 10.0373723654377 - [20250519_005522.]: Samplename: Sample#4 + [20250519_010356.]: Samplename: Sample#4 Root: 10.037 --> Root in between the borders! Added to results. Hyperbolic solved: 22.8733116049306 - [20250519_005522.]: Samplename: Sample#5 + [20250519_010356.]: Samplename: Sample#5 Root: 22.873 --> Root in between the borders! Added to results. Hyperbolic solved: 25.9417915879453 - [20250519_005522.]: Samplename: Sample#6 + [20250519_010356.]: Samplename: Sample#6 Root: 25.942 --> Root in between the borders! Added to results. Hyperbolic solved: 42.7945071576182 - [20250519_005522.]: Samplename: Sample#7 + [20250519_010356.]: Samplename: Sample#7 Root: 42.795 --> Root in between the borders! Added to results. Hyperbolic solved: 88.8741214991654 - [20250519_005522.]: Samplename: Sample#8 + [20250519_010356.]: Samplename: Sample#8 Root: 88.874 --> Root in between the borders! Added to results. Hyperbolic solved: 2.34647622155857 - [20250519_005522.]: Samplename: Sample#9 + [20250519_010356.]: Samplename: Sample#9 Root: 2.346 --> Root in between the borders! Added to results. - [20250519_005522.]: Solving hyperbolic regression for CpG#6 + [20250519_010356.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 79.278059253184 - [20250519_005522.]: Samplename: Sample#1 + [20250519_010356.]: Samplename: Sample#1 Root: 79.278 --> Root in between the borders! Added to results. Hyperbolic solved: 30.2012459144106 - [20250519_005522.]: Samplename: Sample#10 + [20250519_010356.]: Samplename: Sample#10 Root: 30.201 --> Root in between the borders! Added to results. Hyperbolic solved: 41.8474392901329 - [20250519_005522.]: Samplename: Sample#2 + [20250519_010356.]: Samplename: Sample#2 Root: 41.847 --> Root in between the borders! Added to results. Hyperbolic solved: 56.8423515999057 - [20250519_005522.]: Samplename: Sample#3 + [20250519_010356.]: Samplename: Sample#3 Root: 56.842 --> Root in between the borders! Added to results. Hyperbolic solved: 8.87856073331453 - [20250519_005522.]: Samplename: Sample#4 + [20250519_010356.]: Samplename: Sample#4 Root: 8.879 --> Root in between the borders! Added to results. Hyperbolic solved: 18.6901596391315 - [20250519_005522.]: Samplename: Sample#5 + [20250519_010356.]: Samplename: Sample#5 Root: 18.69 --> Root in between the borders! Added to results. Hyperbolic solved: 29.9309263719602 - [20250519_005522.]: Samplename: Sample#6 + [20250519_010356.]: Samplename: Sample#6 Root: 29.931 --> Root in between the borders! Added to results. Hyperbolic solved: 42.814855924799 - [20250519_005522.]: Samplename: Sample#7 + [20250519_010356.]: Samplename: Sample#7 Root: 42.815 --> Root in between the borders! Added to results. Hyperbolic solved: 86.75018307028 - [20250519_005522.]: Samplename: Sample#8 + [20250519_010356.]: Samplename: Sample#8 Root: 86.75 --> Root in between the borders! Added to results. Hyperbolic solved: 1.51516233882239 - [20250519_005522.]: Samplename: Sample#9 + [20250519_010356.]: Samplename: Sample#9 Root: 1.515 --> Root in between the borders! Added to results. - [20250519_005522.]: Solving hyperbolic regression for CpG#7 + [20250519_010356.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 78.2565541874121 - [20250519_005522.]: Samplename: Sample#1 + [20250519_010356.]: Samplename: Sample#1 Root: 78.257 --> Root in between the borders! Added to results. Hyperbolic solved: 25.488740177574 - [20250519_005522.]: Samplename: Sample#10 + [20250519_010356.]: Samplename: Sample#10 Root: 25.489 --> Root in between the borders! Added to results. Hyperbolic solved: 47.3712173704701 - [20250519_005522.]: Samplename: Sample#2 + [20250519_010356.]: Samplename: Sample#2 Root: 47.371 --> Root in between the borders! Added to results. Hyperbolic solved: 58.3142577916519 - [20250519_005522.]: Samplename: Sample#3 + [20250519_010356.]: Samplename: Sample#3 Root: 58.314 --> Root in between the borders! Added to results. Hyperbolic solved: 11.7212348600366 - [20250519_005522.]: Samplename: Sample#4 + [20250519_010356.]: Samplename: Sample#4 Root: 11.721 --> Root in between the borders! Added to results. Hyperbolic solved: 25.3797494985646 - [20250519_005522.]: Samplename: Sample#5 + [20250519_010356.]: Samplename: Sample#5 Root: 25.38 --> Root in between the borders! Added to results. Hyperbolic solved: 29.4095117400265 - [20250519_005522.]: Samplename: Sample#6 + [20250519_010356.]: Samplename: Sample#6 Root: 29.41 --> Root in between the borders! Added to results. Hyperbolic solved: 44.5754992753069 - [20250519_005522.]: Samplename: Sample#7 + [20250519_010356.]: Samplename: Sample#7 Root: 44.575 --> Root in between the borders! Added to results. Hyperbolic solved: 85.9628719316752 - [20250519_005522.]: Samplename: Sample#8 + [20250519_010356.]: Samplename: Sample#8 Root: 85.963 --> Root in between the borders! Added to results. Hyperbolic solved: -4.16453560967523 - [20250519_005522.]: Samplename: Sample#9 + [20250519_010356.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -4.165 --> '-10 < root < 0' --> substitute 0 - [20250519_005522.]: Solving hyperbolic regression for CpG#8 + [20250519_010356.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 72.9696611539955 - [20250519_005522.]: Samplename: Sample#1 + [20250519_010356.]: Samplename: Sample#1 Root: 72.97 --> Root in between the borders! Added to results. Hyperbolic solved: 27.8250961443825 - [20250519_005522.]: Samplename: Sample#10 + [20250519_010356.]: Samplename: Sample#10 Root: 27.825 --> Root in between the borders! Added to results. Hyperbolic solved: 34.9068396054724 - [20250519_005522.]: Samplename: Sample#2 + [20250519_010356.]: Samplename: Sample#2 Root: 34.907 --> Root in between the borders! Added to results. Hyperbolic solved: 59.163744843639 - [20250519_005522.]: Samplename: Sample#3 + [20250519_010356.]: Samplename: Sample#3 Root: 59.164 --> Root in between the borders! Added to results. Hyperbolic solved: 9.64644348381889 - [20250519_005522.]: Samplename: Sample#4 + [20250519_010356.]: Samplename: Sample#4 Root: 9.646 --> Root in between the borders! Added to results. Hyperbolic solved: 19.3522608313684 - [20250519_005522.]: Samplename: Sample#5 + [20250519_010356.]: Samplename: Sample#5 Root: 19.352 --> Root in between the borders! Added to results. Hyperbolic solved: 38.5571445759356 - [20250519_005522.]: Samplename: Sample#6 + [20250519_010356.]: Samplename: Sample#6 Root: 38.557 --> Root in between the borders! Added to results. Hyperbolic solved: 44.7759635921689 - [20250519_005522.]: Samplename: Sample#7 + [20250519_010356.]: Samplename: Sample#7 Root: 44.776 --> Root in between the borders! Added to results. Hyperbolic solved: 84.8599672002532 - [20250519_005522.]: Samplename: Sample#8 + [20250519_010356.]: Samplename: Sample#8 Root: 84.86 --> Root in between the borders! Added to results. Hyperbolic solved: -2.05045066787228 - [20250519_005522.]: Samplename: Sample#9 + [20250519_010356.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -2.05 --> '-10 < root < 0' --> substitute 0 - [20250519_005522.]: Solving hyperbolic regression for CpG#9 + [20250519_010356.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 81.0416669543132 - [20250519_005522.]: Samplename: Sample#1 + [20250519_010356.]: Samplename: Sample#1 Root: 81.042 --> Root in between the borders! Added to results. Hyperbolic solved: 26.6441665578561 - [20250519_005522.]: Samplename: Sample#10 + [20250519_010356.]: Samplename: Sample#10 Root: 26.644 --> Root in between the borders! Added to results. Hyperbolic solved: 45.7046185029368 - [20250519_005522.]: Samplename: Sample#2 + [20250519_010356.]: Samplename: Sample#2 Root: 45.705 --> Root in between the borders! Added to results. Hyperbolic solved: 56.9767969678006 - [20250519_005522.]: Samplename: Sample#3 + [20250519_010356.]: Samplename: Sample#3 Root: 56.977 --> Root in between the borders! Added to results. Hyperbolic solved: 6.81231488238752 - [20250519_005522.]: Samplename: Sample#4 + [20250519_010356.]: Samplename: Sample#4 Root: 6.812 --> Root in between the borders! Added to results. Hyperbolic solved: 22.928478377057 - [20250519_005522.]: Samplename: Sample#5 + [20250519_010356.]: Samplename: Sample#5 Root: 22.928 --> Root in between the borders! Added to results. Hyperbolic solved: 38.8653192337298 - [20250519_005522.]: Samplename: Sample#6 + [20250519_010356.]: Samplename: Sample#6 Root: 38.865 --> Root in between the borders! Added to results. Hyperbolic solved: 43.6855618249121 - [20250519_005522.]: Samplename: Sample#7 + [20250519_010356.]: Samplename: Sample#7 Root: 43.686 --> Root in between the borders! Added to results. Hyperbolic solved: 88.0155362134065 - [20250519_005522.]: Samplename: Sample#8 + [20250519_010356.]: Samplename: Sample#8 Root: 88.016 --> Root in between the borders! Added to results. Hyperbolic solved: -3.35609615796437 - [20250519_005522.]: Samplename: Sample#9 + [20250519_010356.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -3.356 --> '-10 < root < 0' --> substitute 0 - [20250519_005522.]: Solving hyperbolic regression for row_means + [20250519_010356.]: Solving hyperbolic regression for row_means Hyperbolic solved: 77.0692788889392 - [20250519_005522.]: Samplename: Sample#1 + [20250519_010356.]: Samplename: Sample#1 Root: 77.069 --> Root in between the borders! Added to results. Hyperbolic solved: 28.3620037732087 - [20250519_005522.]: Samplename: Sample#10 + [20250519_010356.]: Samplename: Sample#10 Root: 28.362 --> Root in between the borders! Added to results. Hyperbolic solved: 42.5026157763218 - [20250519_005522.]: Samplename: Sample#2 + [20250519_010356.]: Samplename: Sample#2 Root: 42.503 --> Root in between the borders! Added to results. Hyperbolic solved: 57.2972029249363 - [20250519_005522.]: Samplename: Sample#3 + [20250519_010356.]: Samplename: Sample#3 Root: 57.297 --> Root in between the borders! Added to results. Hyperbolic solved: 8.82704268325745 - [20250519_005522.]: Samplename: Sample#4 + [20250519_010356.]: Samplename: Sample#4 Root: 8.827 --> Root in between the borders! Added to results. Hyperbolic solved: 21.8102595595999 - [20250519_005522.]: Samplename: Sample#5 + [20250519_010356.]: Samplename: Sample#5 Root: 21.81 --> Root in between the borders! Added to results. Hyperbolic solved: 28.7228654114699 - [20250519_005522.]: Samplename: Sample#6 + [20250519_010356.]: Samplename: Sample#6 Root: 28.723 --> Root in between the borders! Added to results. Hyperbolic solved: 43.4105084715017 - [20250519_005522.]: Samplename: Sample#7 + [20250519_010356.]: Samplename: Sample#7 Root: 43.411 --> Root in between the borders! Added to results. Hyperbolic solved: 86.4143551575745 - [20250519_005522.]: Samplename: Sample#8 + [20250519_010356.]: Samplename: Sample#8 Root: 86.414 --> Root in between the borders! Added to results. Hyperbolic solved: -0.237016010782264 - [20250519_005522.]: Samplename: Sample#9 + [20250519_010356.]: Samplename: Sample#9 ## WARNING ## No fitting root within the borders found. Negative numeric root found: Root: -0.237 --> '-10 < root < 0' --> substitute 0 - [20250519_005522.]: on_start: using future::plan("sequential") - [20250519_005522.]: Entered 'clean_dt'-Function - [20250519_005522.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_005522.]: got experimental data - [20250519_005522.]: Entered 'clean_dt'-Function - [20250519_005522.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_005522.]: got calibration data - [20250519_005522.]: + [20250519_010356.]: on_start: using future::plan("sequential") + [20250519_010356.]: Entered 'clean_dt'-Function + [20250519_010356.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_010356.]: got experimental data + [20250519_010356.]: Entered 'clean_dt'-Function + [20250519_010356.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_010356.]: got calibration data + [20250519_010356.]: ### Starting with regression calculations ### - [20250519_005522.]: Entered 'regression_type1'-Function - [20250519_005522.]: # CpG-site: CpG#1 + [20250519_010356.]: Entered 'regression_type1'-Function + [20250519_010356.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_005522.]: Logging df_agg: CpG#1 - [20250519_005522.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005522.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005522.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005522.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005522.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_005522.]: Entered 'hyperbolic_regression'-Function - [20250519_005522.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005522.]: Entered 'cubic_regression'-Function - [20250519_005522.]: 'cubic_regression': minmax = FALSE - [20250519_005523.]: # CpG-site: CpG#2 + [20250519_010357.]: Logging df_agg: CpG#1 + [20250519_010357.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010357.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_010357.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_010357.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_010357.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) + [20250519_010357.]: Entered 'hyperbolic_regression'-Function + [20250519_010357.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010357.]: Entered 'cubic_regression'-Function + [20250519_010357.]: 'cubic_regression': minmax = FALSE + [20250519_010357.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_005523.]: Logging df_agg: CpG#2 - [20250519_005523.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005523.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005523.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005523.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005523.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_005523.]: Entered 'hyperbolic_regression'-Function - [20250519_005523.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005523.]: Entered 'cubic_regression'-Function - [20250519_005523.]: 'cubic_regression': minmax = FALSE - [20250519_005523.]: # CpG-site: CpG#3 + [20250519_010357.]: Logging df_agg: CpG#2 + [20250519_010357.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010357.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_010357.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_010357.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_010357.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) + [20250519_010357.]: Entered 'hyperbolic_regression'-Function + [20250519_010357.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010357.]: Entered 'cubic_regression'-Function + [20250519_010357.]: 'cubic_regression': minmax = FALSE + [20250519_010357.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_005523.]: Logging df_agg: CpG#3 - [20250519_005523.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005523.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005523.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005523.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005523.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_005523.]: Entered 'hyperbolic_regression'-Function - [20250519_005523.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005523.]: Entered 'cubic_regression'-Function - [20250519_005523.]: 'cubic_regression': minmax = FALSE - [20250519_005523.]: # CpG-site: CpG#4 + [20250519_010357.]: Logging df_agg: CpG#3 + [20250519_010357.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010357.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_010357.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_010357.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_010357.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) + [20250519_010357.]: Entered 'hyperbolic_regression'-Function + [20250519_010357.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010357.]: Entered 'cubic_regression'-Function + [20250519_010357.]: 'cubic_regression': minmax = FALSE + [20250519_010357.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_005523.]: Logging df_agg: CpG#4 - [20250519_005523.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005523.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005523.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005523.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005523.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_005523.]: Entered 'hyperbolic_regression'-Function - [20250519_005523.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005523.]: Entered 'cubic_regression'-Function - [20250519_005523.]: 'cubic_regression': minmax = FALSE - [20250519_005523.]: # CpG-site: CpG#5 + [20250519_010357.]: Logging df_agg: CpG#4 + [20250519_010357.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010357.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_010357.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_010357.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_010357.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) + [20250519_010357.]: Entered 'hyperbolic_regression'-Function + [20250519_010357.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010357.]: Entered 'cubic_regression'-Function + [20250519_010357.]: 'cubic_regression': minmax = FALSE + [20250519_010357.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_005523.]: Logging df_agg: CpG#5 - [20250519_005523.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005523.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005523.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005523.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005523.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_005523.]: Entered 'hyperbolic_regression'-Function - [20250519_005523.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005523.]: Entered 'cubic_regression'-Function - [20250519_005523.]: 'cubic_regression': minmax = FALSE - [20250519_005523.]: # CpG-site: CpG#6 + [20250519_010357.]: Logging df_agg: CpG#5 + [20250519_010357.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010357.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_010357.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_010357.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_010357.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) + [20250519_010357.]: Entered 'hyperbolic_regression'-Function + [20250519_010357.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010357.]: Entered 'cubic_regression'-Function + [20250519_010357.]: 'cubic_regression': minmax = FALSE + [20250519_010357.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_005523.]: Logging df_agg: CpG#6 - [20250519_005523.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005523.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005523.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005523.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005523.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_005523.]: Entered 'hyperbolic_regression'-Function - [20250519_005523.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005523.]: Entered 'cubic_regression'-Function - [20250519_005523.]: 'cubic_regression': minmax = FALSE - [20250519_005523.]: # CpG-site: CpG#7 + [20250519_010357.]: Logging df_agg: CpG#6 + [20250519_010357.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010357.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_010357.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_010357.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_010357.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) + [20250519_010357.]: Entered 'hyperbolic_regression'-Function + [20250519_010357.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010357.]: Entered 'cubic_regression'-Function + [20250519_010357.]: 'cubic_regression': minmax = FALSE + [20250519_010357.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_005523.]: Logging df_agg: CpG#7 - [20250519_005523.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005523.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005523.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005523.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005523.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_005523.]: Entered 'hyperbolic_regression'-Function - [20250519_005523.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005523.]: Entered 'cubic_regression'-Function - [20250519_005523.]: 'cubic_regression': minmax = FALSE - [20250519_005523.]: # CpG-site: CpG#8 + [20250519_010357.]: Logging df_agg: CpG#7 + [20250519_010357.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010357.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_010357.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_010357.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_010357.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) + [20250519_010357.]: Entered 'hyperbolic_regression'-Function + [20250519_010357.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010357.]: Entered 'cubic_regression'-Function + [20250519_010357.]: 'cubic_regression': minmax = FALSE + [20250519_010357.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_005523.]: Logging df_agg: CpG#8 - [20250519_005523.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005523.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005523.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005523.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005523.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_005523.]: Entered 'hyperbolic_regression'-Function - [20250519_005523.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005523.]: Entered 'cubic_regression'-Function - [20250519_005523.]: 'cubic_regression': minmax = FALSE - [20250519_005523.]: # CpG-site: CpG#9 + [20250519_010357.]: Logging df_agg: CpG#8 + [20250519_010357.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010357.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_010357.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_010357.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_010357.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) + [20250519_010357.]: Entered 'hyperbolic_regression'-Function + [20250519_010357.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010357.]: Entered 'cubic_regression'-Function + [20250519_010357.]: 'cubic_regression': minmax = FALSE + [20250519_010357.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_005523.]: Logging df_agg: CpG#9 - [20250519_005523.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005523.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005523.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005523.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005523.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_005523.]: Entered 'hyperbolic_regression'-Function - [20250519_005523.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005523.]: Entered 'cubic_regression'-Function - [20250519_005523.]: 'cubic_regression': minmax = FALSE - [20250519_005523.]: # CpG-site: row_means + [20250519_010357.]: Logging df_agg: CpG#9 + [20250519_010357.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010357.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_010357.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_010357.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_010357.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) + [20250519_010357.]: Entered 'hyperbolic_regression'-Function + [20250519_010357.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010357.]: Entered 'cubic_regression'-Function + [20250519_010357.]: 'cubic_regression': minmax = FALSE + [20250519_010357.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723122, 2.47760998435345)c(5.94911111111111, 0.84962962962963, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703704, 19.6995555555555, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_005523.]: Logging df_agg: row_means - [20250519_005523.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005523.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005523.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723122, 2.47760998435345)[20250519_005523.]: c(5.94911111111111, 0.84962962962963, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005523.]: c(NA, 6.79703703703704, 19.6995555555555, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_005523.]: Entered 'hyperbolic_regression'-Function - [20250519_005523.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005523.]: Entered 'cubic_regression'-Function - [20250519_005523.]: 'cubic_regression': minmax = FALSE - [20250519_005523.]: ### Starting with plotting ### - [20250519_005524.]: Creating plot No. 1 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG1.png - [20250519_005524.]: # CpG-site: CpG#1 + [20250519_010357.]: Logging df_agg: row_means + [20250519_010357.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010357.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_010357.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723122, 2.47760998435345)[20250519_010357.]: c(5.94911111111111, 0.84962962962963, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_010357.]: c(NA, 6.79703703703704, 19.6995555555555, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) + [20250519_010357.]: Entered 'hyperbolic_regression'-Function + [20250519_010357.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010357.]: Entered 'cubic_regression'-Function + [20250519_010357.]: 'cubic_regression': minmax = FALSE + [20250519_010358.]: ### Starting with plotting ### + [20250519_010358.]: Creating plot No. 1 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG1.png + [20250519_010358.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -123249135.40483 , a = 49589952.0989949 , b = -123249135.40483 , d = 59280789.0121927 - [20250519_005524.]: # CpG-site: CpG#1 + [20250519_010358.]: # CpG-site: CpG#1 Cubic: Using a = 6.53413423120091e-05 , b = -0.0055806968734969 , c = 0.784061853455188 , d = 1.93182659932656 - [20250519_005526.]: Creating plot No. 2 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG2.png - [20250519_005526.]: # CpG-site: CpG#2 + [20250519_010400.]: Creating plot No. 2 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG2.png + [20250519_010400.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = 167929575.171327 , a = 42299444.0962795 , b = 167929575.171327 , d = 47897274.2220611 - [20250519_005526.]: # CpG-site: CpG#2 + [20250519_010400.]: # CpG-site: CpG#2 Cubic: Using a = 8.04237934904601e-06 , b = 0.00293158941798942 , c = 0.514821742825076 , d = 9.27667003367003 - [20250519_005528.]: Creating plot No. 3 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG3.png - [20250519_005528.]: # CpG-site: CpG#3 + [20250519_010402.]: Creating plot No. 3 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG3.png + [20250519_010402.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = 31004745.3756238 , a = 31050253.3779659 , b = 31004745.3756238 , d = 40665231.758814 - [20250519_005528.]: # CpG-site: CpG#3 + [20250519_010402.]: # CpG-site: CpG#3 Cubic: Using a = 2.30555869809204e-05 , b = -0.000797009331409346 , c = 0.62783129228796 , d = 3.88705218855218 - [20250519_005530.]: Creating plot No. 4 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG4.png - [20250519_005530.]: # CpG-site: CpG#4 + [20250519_010404.]: Creating plot No. 4 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG4.png + [20250519_010404.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = 128022258.276155 , a = 42779497.5538086 , b = 128022258.276155 , d = 50999439.9227065 - [20250519_005530.]: # CpG-site: CpG#4 + [20250519_010404.]: # CpG-site: CpG#4 Cubic: Using a = 3.2166356902357e-05 , b = -0.0015913142857143 , c = 0.697398364598366 , d = 6.17055050505048 - [20250519_005532.]: Creating plot No. 5 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG5.png - [20250519_005532.]: # CpG-site: CpG#5 + [20250519_010406.]: Creating plot No. 5 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG5.png + [20250519_010406.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = 29447513.6184781 , a = 41864399.8673718 , b = 29447513.6184781 , d = 55382346.8470806 - [20250519_005532.]: # CpG-site: CpG#5 + [20250519_010406.]: # CpG-site: CpG#5 Cubic: Using a = -4.48459708193036e-06 , b = 0.00375371236171235 , c = 0.422446384479718 , d = 5.17203872053872 - [20250519_005534.]: Creating plot No. 6 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG6.png - [20250519_005534.]: # CpG-site: CpG#6 + [20250519_010408.]: Creating plot No. 6 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG6.png + [20250519_010408.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = 17150080.5960938 , a = 40932834.1078748 , b = 17150080.5960938 , d = 46723471.6686748 - [20250519_005534.]: # CpG-site: CpG#6 + [20250519_010408.]: # CpG-site: CpG#6 Cubic: Using a = 2.38852884399552e-05 , b = 0.000918637037037021 , c = 0.561022132435467 , d = 6.1479276094276 - [20250519_005537.]: Creating plot No. 7 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG7.png - [20250519_005537.]: # CpG-site: CpG#7 + [20250519_010410.]: Creating plot No. 7 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG7.png + [20250519_010410.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -57204506.2862076 , a = 29554652.6579632 , b = -57204506.2862076 , d = 48548425.6483363 - [20250519_005537.]: # CpG-site: CpG#7 + [20250519_010410.]: # CpG-site: CpG#7 Cubic: Using a = 4.98010505050505e-05 , b = -0.00436152150072151 , c = 0.579588917748918 , d = 1.72104545454544 - [20250519_005539.]: Creating plot No. 8 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG8.png - [20250519_005539.]: # CpG-site: CpG#8 + [20250519_010412.]: Creating plot No. 8 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG8.png + [20250519_010412.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = 263871870.982016 , a = 38159683.1948542 , b = 263871870.982016 , d = 45284547.6704654 - [20250519_005539.]: # CpG-site: CpG#8 + [20250519_010412.]: # CpG-site: CpG#8 Cubic: Using a = -1.50060965207632e-05 , b = 0.00447978143338143 , c = 0.534897737694404 , d = 9.56981481481482 - [20250519_005541.]: Creating plot No. 9 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG9.png - [20250519_005541.]: # CpG-site: CpG#9 + [20250519_010415.]: Creating plot No. 9 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG9.png + [20250519_010415.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -224404013.660658 , a = 51672526.9851193 , b = -224404013.660658 , d = 60201205.3472543 - [20250519_005541.]: # CpG-site: CpG#9 + [20250519_010415.]: # CpG-site: CpG#9 Cubic: Using a = 7.72300426487093e-05 , b = -0.00542281173641174 , c = 0.67899229597563 , d = 2.72413468013467 - [20250519_005543.]: Creating plot No. 10 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_rowmeans.png - [20250519_005543.]: # CpG-site: row_means + [20250519_010417.]: Creating plot No. 10 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_rowmeans.png + [20250519_010417.]: # CpG-site: row_means Hyperbolic: Using bias_weight = 40333655.1288316 , a = 44937623.5310126 , b = 40333655.1288316 , d = 55678831.3245349 - [20250519_005543.]: # CpG-site: row_means + [20250519_010417.]: # CpG-site: row_means Cubic: Using a = 2.88923726150392e-05 , b = -0.0006299592752926 , c = 0.600117857944524 , d = 5.17789562289563 - [20250519_005545.]: Entered 'solving_equations'-Function - [20250519_005545.]: Solving hyperbolic regression for CpG#1 + [20250519_010419.]: Entered 'solving_equations'-Function + [20250519_010419.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 6.03337011705089 - [20250519_005545.]: Samplename: 0 + [20250519_010419.]: Samplename: 0 Root: 6.033 --> Root in between the borders! Added to results. Hyperbolic solved: 14.7663097441095 - [20250519_005545.]: Samplename: 12.5 + [20250519_010419.]: Samplename: 12.5 Root: 14.766 --> Root in between the borders! Added to results. Hyperbolic solved: 23.1900365713551 - [20250519_005545.]: Samplename: 25 + [20250519_010419.]: Samplename: 25 Root: 23.19 --> Root in between the borders! Added to results. Hyperbolic solved: 33.8197153352119 - [20250519_005545.]: Samplename: 37.5 + [20250519_010419.]: Samplename: 37.5 Root: 33.82 --> Root in between the borders! Added to results. Hyperbolic solved: 46.5156833662696 - [20250519_005545.]: Samplename: 50 + [20250519_010419.]: Samplename: 50 Root: 46.516 --> Root in between the borders! Added to results. Hyperbolic solved: 60.2074431300908 - [20250519_005545.]: Samplename: 62.5 + [20250519_010419.]: Samplename: 62.5 Root: 60.207 --> Root in between the borders! Added to results. Hyperbolic solved: 71.6105733239506 - [20250519_005545.]: Samplename: 75 + [20250519_010419.]: Samplename: 75 Root: 71.611 --> Root in between the borders! Added to results. Hyperbolic solved: 80.2235915426204 - [20250519_005545.]: Samplename: 87.5 + [20250519_010419.]: Samplename: 87.5 Root: 80.224 --> Root in between the borders! Added to results. Hyperbolic solved: 113.633279788745 - [20250519_005545.]: Samplename: 100 + [20250519_010419.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA - [20250519_005545.]: Solving hyperbolic regression for CpG#2 + [20250519_010419.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 6.85513576175294 - [20250519_005545.]: Samplename: 0 + [20250519_010419.]: Samplename: 0 Root: 6.855 --> Root in between the borders! Added to results. Hyperbolic solved: 13.7133339963707 - [20250519_005545.]: Samplename: 12.5 + [20250519_010419.]: Samplename: 12.5 Root: 13.713 --> Root in between the borders! Added to results. Hyperbolic solved: 24.5830349243156 - [20250519_005545.]: Samplename: 25 + [20250519_010419.]: Samplename: 25 Root: 24.583 --> Root in between the borders! Added to results. Hyperbolic solved: 31.8277435330554 - [20250519_005545.]: Samplename: 37.5 + [20250519_010419.]: Samplename: 37.5 Root: 31.828 --> Root in between the borders! Added to results. Hyperbolic solved: 43.0277185945539 - [20250519_005545.]: Samplename: 50 + [20250519_010419.]: Samplename: 50 Root: 43.028 --> Root in between the borders! Added to results. Hyperbolic solved: 63.1097818281222 - [20250519_005545.]: Samplename: 62.5 + [20250519_010419.]: Samplename: 62.5 Root: 63.11 --> Root in between the borders! Added to results. Hyperbolic solved: 73.3036858899805 - [20250519_005545.]: Samplename: 75 + [20250519_010419.]: Samplename: 75 Root: 73.304 --> Root in between the borders! Added to results. Hyperbolic solved: 84.4808447799272 - [20250519_005545.]: Samplename: 87.5 + [20250519_010419.]: Samplename: 87.5 Root: 84.481 --> Root in between the borders! Added to results. Hyperbolic solved: 109.0987325611 - [20250519_005545.]: Samplename: 100 + [20250519_010419.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 109.099 --> '100 < root < 110' --> substitute 100 - [20250519_005545.]: Solving hyperbolic regression for CpG#3 + [20250519_010419.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 5.27473119209009 - [20250519_005545.]: Samplename: 0 + [20250519_010419.]: Samplename: 0 Root: 5.275 --> Root in between the borders! Added to results. Hyperbolic solved: 12.7004989900866 - [20250519_005545.]: Samplename: 12.5 + [20250519_010419.]: Samplename: 12.5 Root: 12.7 --> Root in between the borders! Added to results. Hyperbolic solved: 24.2988465005425 - [20250519_005545.]: Samplename: 25 + [20250519_010419.]: Samplename: 25 Root: 24.299 --> Root in between the borders! Added to results. Hyperbolic solved: 33.7074476249853 - [20250519_005545.]: Samplename: 37.5 + [20250519_010419.]: Samplename: 37.5 Root: 33.707 --> Root in between the borders! Added to results. Hyperbolic solved: 47.0168800224423 - [20250519_005545.]: Samplename: 50 + [20250519_010419.]: Samplename: 50 Root: 47.017 --> Root in between the borders! Added to results. Hyperbolic solved: 61.5718102366584 - [20250519_005545.]: Samplename: 62.5 + [20250519_010419.]: Samplename: 62.5 Root: 61.572 --> Root in between the borders! Added to results. Hyperbolic solved: 76.091389954119 - [20250519_005545.]: Samplename: 75 + [20250519_010419.]: Samplename: 75 Root: 76.091 --> Root in between the borders! Added to results. Hyperbolic solved: 79.6023799465703 - [20250519_005545.]: Samplename: 87.5 + [20250519_010419.]: Samplename: 87.5 Root: 79.602 --> Root in between the borders! Added to results. Hyperbolic solved: 109.736019452694 - [20250519_005545.]: Samplename: 100 + [20250519_010419.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 109.736 --> '100 < root < 110' --> substitute 100 - [20250519_005545.]: Solving hyperbolic regression for CpG#4 + [20250519_010419.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 5.07584235183922 - [20250519_005545.]: Samplename: 0 + [20250519_010419.]: Samplename: 0 Root: 5.076 --> Root in between the borders! Added to results. Hyperbolic solved: 14.2021272601476 - [20250519_005545.]: Samplename: 12.5 + [20250519_010419.]: Samplename: 12.5 Root: 14.202 --> Root in between the borders! Added to results. Hyperbolic solved: 23.203637284743 - [20250519_005545.]: Samplename: 25 + [20250519_010419.]: Samplename: 25 Root: 23.204 --> Root in between the borders! Added to results. Hyperbolic solved: 34.7960863609446 - [20250519_005545.]: Samplename: 37.5 + [20250519_010419.]: Samplename: 37.5 Root: 34.796 --> Root in between the borders! Added to results. Hyperbolic solved: 44.4638162018573 - [20250519_005545.]: Samplename: 50 + [20250519_010419.]: Samplename: 50 Root: 44.464 --> Root in between the borders! Added to results. Hyperbolic solved: 64.2755507391393 - [20250519_005545.]: Samplename: 62.5 + [20250519_010419.]: Samplename: 62.5 Root: 64.276 --> Root in between the borders! Added to results. Hyperbolic solved: 71.7670214785718 - [20250519_005545.]: Samplename: 75 + [20250519_010419.]: Samplename: 75 Root: 71.767 --> Root in between the borders! Added to results. Hyperbolic solved: 82.5599568822802 - [20250519_005545.]: Samplename: 87.5 + [20250519_010419.]: Samplename: 87.5 Root: 82.56 --> Root in between the borders! Added to results. Hyperbolic solved: 109.655967198106 - [20250519_005545.]: Samplename: 100 + [20250519_010419.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 109.656 --> '100 < root < 110' --> substitute 100 - [20250519_005546.]: Solving hyperbolic regression for CpG#5 + [20250519_010419.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 6.96411772281142 - [20250519_005546.]: Samplename: 0 + [20250519_010419.]: Samplename: 0 Root: 6.964 --> Root in between the borders! Added to results. Hyperbolic solved: 12.4462106087166 - [20250519_005546.]: Samplename: 12.5 + [20250519_010419.]: Samplename: 12.5 Root: 12.446 --> Root in between the borders! Added to results. Hyperbolic solved: 22.9738427992134 - [20250519_005546.]: Samplename: 25 + [20250519_010419.]: Samplename: 25 Root: 22.974 --> Root in between the borders! Added to results. Hyperbolic solved: 34.1311793989123 - [20250519_005546.]: Samplename: 37.5 + [20250519_010419.]: Samplename: 37.5 Root: 34.131 --> Root in between the borders! Added to results. Hyperbolic solved: 46.7456955019114 - [20250519_005546.]: Samplename: 50 + [20250519_010419.]: Samplename: 50 Root: 46.746 --> Root in between the borders! Added to results. Hyperbolic solved: 58.6021949543013 - [20250519_005546.]: Samplename: 62.5 + [20250519_010419.]: Samplename: 62.5 Root: 58.602 --> Root in between the borders! Added to results. Hyperbolic solved: 74.5100864344263 - [20250519_005546.]: Samplename: 75 + [20250519_010419.]: Samplename: 75 Root: 74.51 --> Root in between the borders! Added to results. Hyperbolic solved: 86.8549692943716 - [20250519_005546.]: Samplename: 87.5 + [20250519_010419.]: Samplename: 87.5 Root: 86.855 --> Root in between the borders! Added to results. Hyperbolic solved: 106.771715286513 - [20250519_005546.]: Samplename: 100 + [20250519_010419.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 106.772 --> '100 < root < 110' --> substitute 100 - [20250519_005546.]: Solving hyperbolic regression for CpG#6 + [20250519_010419.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 6.82248549455073 - [20250519_005546.]: Samplename: 0 + [20250519_010419.]: Samplename: 0 Root: 6.822 --> Root in between the borders! Added to results. Hyperbolic solved: 14.2907258003629 - [20250519_005546.]: Samplename: 12.5 + [20250519_010419.]: Samplename: 12.5 Root: 14.291 --> Root in between the borders! Added to results. Hyperbolic solved: 24.7343949718638 - [20250519_005546.]: Samplename: 25 + [20250519_010419.]: Samplename: 25 Root: 24.734 --> Root in between the borders! Added to results. Hyperbolic solved: 31.7110486196861 - [20250519_005546.]: Samplename: 37.5 + [20250519_010419.]: Samplename: 37.5 Root: 31.711 --> Root in between the borders! Added to results. Hyperbolic solved: 44.7745870553137 - [20250519_005546.]: Samplename: 50 + [20250519_010419.]: Samplename: 50 Root: 44.775 --> Root in between the borders! Added to results. Hyperbolic solved: 60.1336283719223 - [20250519_005546.]: Samplename: 62.5 + [20250519_010419.]: Samplename: 62.5 Root: 60.134 --> Root in between the borders! Added to results. Hyperbolic solved: 70.3292384995337 - [20250519_005546.]: Samplename: 75 + [20250519_010419.]: Samplename: 75 Root: 70.329 --> Root in between the borders! Added to results. Hyperbolic solved: 88.2655480529845 - [20250519_005546.]: Samplename: 87.5 + [20250519_010419.]: Samplename: 87.5 Root: 88.266 --> Root in between the borders! Added to results. Hyperbolic solved: 108.938360440145 - [20250519_005546.]: Samplename: 100 + [20250519_010419.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 108.938 --> '100 < root < 110' --> substitute 100 - [20250519_005546.]: Solving hyperbolic regression for CpG#7 + [20250519_010419.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 6.75841824784972 - [20250519_005546.]: Samplename: 0 + [20250519_010419.]: Samplename: 0 Root: 6.758 --> Root in between the borders! Added to results. Hyperbolic solved: 13.6258602138932 - [20250519_005546.]: Samplename: 12.5 + [20250519_010419.]: Samplename: 12.5 Root: 13.626 --> Root in between the borders! Added to results. Hyperbolic solved: 23.2672219789518 - [20250519_005546.]: Samplename: 25 + [20250519_010419.]: Samplename: 25 Root: 23.267 --> Root in between the borders! Added to results. Hyperbolic solved: 33.4287658578254 - [20250519_005546.]: Samplename: 37.5 + [20250519_010419.]: Samplename: 37.5 Root: 33.429 --> Root in between the borders! Added to results. Hyperbolic solved: 47.5236851621158 - [20250519_005546.]: Samplename: 50 + [20250519_010419.]: Samplename: 50 Root: 47.524 --> Root in between the borders! Added to results. Hyperbolic solved: 60.5558050598499 - [20250519_005546.]: Samplename: 62.5 + [20250519_010419.]: Samplename: 62.5 Root: 60.556 --> Root in between the borders! Added to results. Hyperbolic solved: 72.3896034879297 - [20250519_005546.]: Samplename: 75 + [20250519_010419.]: Samplename: 75 Root: 72.39 --> Root in between the borders! Added to results. Hyperbolic solved: 77.2628610417933 - [20250519_005546.]: Samplename: 87.5 + [20250519_010419.]: Samplename: 87.5 Root: 77.263 --> Root in between the borders! Added to results. Hyperbolic solved: 115.187789384315 - [20250519_005546.]: Samplename: 100 + [20250519_010419.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA - [20250519_005546.]: Solving hyperbolic regression for CpG#8 + [20250519_010419.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 5.74253131510683 - [20250519_005546.]: Samplename: 0 + [20250519_010419.]: Samplename: 0 Root: 5.743 --> Root in between the borders! Added to results. Hyperbolic solved: 10.9055216254216 - [20250519_005546.]: Samplename: 12.5 + [20250519_010419.]: Samplename: 12.5 Root: 10.906 --> Root in between the borders! Added to results. Hyperbolic solved: 24.5329419436721 - [20250519_005546.]: Samplename: 25 + [20250519_010419.]: Samplename: 25 Root: 24.533 --> Root in between the borders! Added to results. Hyperbolic solved: 32.1516426249373 - [20250519_005546.]: Samplename: 37.5 + [20250519_010419.]: Samplename: 37.5 Root: 32.152 --> Root in between the borders! Added to results. Hyperbolic solved: 49.0919870496865 - [20250519_005546.]: Samplename: 50 + [20250519_010419.]: Samplename: 50 Root: 49.092 --> Root in between the borders! Added to results. Hyperbolic solved: 62.3843810658735 - [20250519_005546.]: Samplename: 62.5 + [20250519_010419.]: Samplename: 62.5 Root: 62.384 --> Root in between the borders! Added to results. Hyperbolic solved: 77.6235905386846 - [20250519_005546.]: Samplename: 75 + [20250519_010419.]: Samplename: 75 Root: 77.624 --> Root in between the borders! Added to results. Hyperbolic solved: 80.9968312605936 - [20250519_005546.]: Samplename: 87.5 + [20250519_010419.]: Samplename: 87.5 Root: 80.997 --> Root in between the borders! Added to results. Hyperbolic solved: 106.570581137258 - [20250519_005546.]: Samplename: 100 + [20250519_010419.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 106.571 --> '100 < root < 110' --> substitute 100 - [20250519_005546.]: Solving hyperbolic regression for CpG#9 + [20250519_010419.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 8.66981641228949 - [20250519_005546.]: Samplename: 0 + [20250519_010419.]: Samplename: 0 Root: 8.67 --> Root in between the borders! Added to results. Hyperbolic solved: 14.9680920996829 - [20250519_005546.]: Samplename: 12.5 + [20250519_010419.]: Samplename: 12.5 Root: 14.968 --> Root in between the borders! Added to results. Hyperbolic solved: 24.6496838227806 - [20250519_005546.]: Samplename: 25 + [20250519_010419.]: Samplename: 25 Root: 24.65 --> Root in between the borders! Added to results. Hyperbolic solved: 31.5444702961411 - [20250519_005546.]: Samplename: 37.5 + [20250519_010419.]: Samplename: 37.5 Root: 31.544 --> Root in between the borders! Added to results. Hyperbolic solved: 44.0944303975514 - [20250519_005546.]: Samplename: 50 + [20250519_010419.]: Samplename: 50 Root: 44.094 --> Root in between the borders! Added to results. Hyperbolic solved: 57.2478940106457 - [20250519_005546.]: Samplename: 62.5 + [20250519_010419.]: Samplename: 62.5 Root: 57.248 --> Root in between the borders! Added to results. Hyperbolic solved: 69.1518417879612 - [20250519_005546.]: Samplename: 75 + [20250519_010419.]: Samplename: 75 Root: 69.152 --> Root in between the borders! Added to results. Hyperbolic solved: 83.8189177560508 - [20250519_005546.]: Samplename: 87.5 + [20250519_010419.]: Samplename: 87.5 Root: 83.819 --> Root in between the borders! Added to results. Hyperbolic solved: 115.854857900091 - [20250519_005546.]: Samplename: 100 + [20250519_010419.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA - [20250519_005546.]: Solving hyperbolic regression for row_means + [20250519_010419.]: Solving hyperbolic regression for row_means Hyperbolic solved: 6.47354965864506 - [20250519_005546.]: Samplename: 0 + [20250519_010419.]: Samplename: 0 Root: 6.474 --> Root in between the borders! Added to results. Hyperbolic solved: 13.5375540918194 - [20250519_005546.]: Samplename: 12.5 + [20250519_010419.]: Samplename: 12.5 Root: 13.538 --> Root in between the borders! Added to results. Hyperbolic solved: 23.976024251167 - [20250519_005546.]: Samplename: 25 + [20250519_010419.]: Samplename: 25 Root: 23.976 --> Root in between the borders! Added to results. Hyperbolic solved: 32.9614416466039 - [20250519_005546.]: Samplename: 37.5 + [20250519_010419.]: Samplename: 37.5 Root: 32.961 --> Root in between the borders! Added to results. Hyperbolic solved: 45.8185453601259 - [20250519_005546.]: Samplename: 50 + [20250519_010419.]: Samplename: 50 Root: 45.819 --> Root in between the borders! Added to results. Hyperbolic solved: 60.9295837234236 - [20250519_005546.]: Samplename: 62.5 + [20250519_010419.]: Samplename: 62.5 Root: 60.93 --> Root in between the borders! Added to results. Hyperbolic solved: 72.9250251350637 - [20250519_005546.]: Samplename: 75 + [20250519_010419.]: Samplename: 75 Root: 72.925 --> Root in between the borders! Added to results. Hyperbolic solved: 82.8721280175692 - [20250519_005546.]: Samplename: 87.5 + [20250519_010419.]: Samplename: 87.5 Root: 82.872 --> Root in between the borders! Added to results. Hyperbolic solved: 110.506149316569 - [20250519_005546.]: Samplename: 100 + [20250519_010419.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. No fitting numeric roots within the borders found: substitute NA - [20250519_005546.]: + [20250519_010419.]: ### Starting with regression calculations ### - [20250519_005546.]: Entered 'regression_type1'-Function - [20250519_005546.]: # CpG-site: CpG#1 + [20250519_010419.]: Entered 'regression_type1'-Function + [20250519_010419.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)c(6.03337011705089, 14.7663097441095, 23.1900365713551, 33.8197153352119, 46.5156833662696, 60.2074431300908, 71.6105733239506, 80.2235915426204)c(6.03337011705089, 2.2663097441095, 1.8099634286449, 3.6802846647881, 3.4843166337304, 2.2925568699092, 3.3894266760494, 7.2764084573796)c(NA, 18.130477952876, 7.2398537145796, 9.81409243943492, 6.9686332674608, 3.66809099185472, 4.51923556806586, 8.3158953798624) - [20250519_005546.]: Logging df_agg: CpG#1 - [20250519_005546.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)[20250519_005546.]: c(6.03337011705089, 14.7663097441095, 23.1900365713551, 33.8197153352119, 46.5156833662696, 60.2074431300908, 71.6105733239506, 80.2235915426204)[20250519_005546.]: c(6.03337011705089, 2.2663097441095, 1.8099634286449, 3.6802846647881, 3.4843166337304, 2.2925568699092, 3.3894266760494, 7.2764084573796)[20250519_005546.]: c(NA, 18.130477952876, 7.2398537145796, 9.81409243943492, 6.9686332674608, 3.66809099185472, 4.51923556806586, 8.3158953798624) - [20250519_005546.]: Entered 'hyperbolic_regression'-Function - [20250519_005546.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005546.]: Entered 'cubic_regression'-Function - [20250519_005546.]: 'cubic_regression': minmax = FALSE - [20250519_005546.]: # CpG-site: CpG#2 + [20250519_010419.]: Logging df_agg: CpG#1 + [20250519_010419.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)[20250519_010419.]: c(6.03337011705089, 14.7663097441095, 23.1900365713551, 33.8197153352119, 46.5156833662696, 60.2074431300908, 71.6105733239506, 80.2235915426204)[20250519_010419.]: c(6.03337011705089, 2.2663097441095, 1.8099634286449, 3.6802846647881, 3.4843166337304, 2.2925568699092, 3.3894266760494, 7.2764084573796)[20250519_010419.]: c(NA, 18.130477952876, 7.2398537145796, 9.81409243943492, 6.9686332674608, 3.66809099185472, 4.51923556806586, 8.3158953798624) + [20250519_010419.]: Entered 'hyperbolic_regression'-Function + [20250519_010419.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010419.]: Entered 'cubic_regression'-Function + [20250519_010419.]: 'cubic_regression': minmax = FALSE + [20250519_010419.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.85513576175294, 13.7133339963707, 24.5830349243156, 31.8277435330554, 43.0277185945539, 63.1097818281222, 73.3036858899805, 84.4808447799272, 100)c(6.85513576175294, 1.2133339963707, 0.416965075684399, 5.6722564669446, 6.9722814054461, 0.609781828122202, 1.6963141100195, 3.0191552200728, 0)c(NA, 9.7066719709656, 1.66786030273759, 15.1260172451856, 13.9445628108922, 0.975650924995523, 2.26175214669267, 3.45046310865463, 0) - [20250519_005546.]: Logging df_agg: CpG#2 - [20250519_005546.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005546.]: c(6.85513576175294, 13.7133339963707, 24.5830349243156, 31.8277435330554, 43.0277185945539, 63.1097818281222, 73.3036858899805, 84.4808447799272, 100)[20250519_005546.]: c(6.85513576175294, 1.2133339963707, 0.416965075684399, 5.6722564669446, 6.9722814054461, 0.609781828122202, 1.6963141100195, 3.0191552200728, 0)[20250519_005546.]: c(NA, 9.7066719709656, 1.66786030273759, 15.1260172451856, 13.9445628108922, 0.975650924995523, 2.26175214669267, 3.45046310865463, 0) - [20250519_005546.]: Entered 'hyperbolic_regression'-Function - [20250519_005546.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005546.]: Entered 'cubic_regression'-Function - [20250519_005546.]: 'cubic_regression': minmax = FALSE - [20250519_005546.]: # CpG-site: CpG#3 + [20250519_010419.]: Logging df_agg: CpG#2 + [20250519_010419.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010419.]: c(6.85513576175294, 13.7133339963707, 24.5830349243156, 31.8277435330554, 43.0277185945539, 63.1097818281222, 73.3036858899805, 84.4808447799272, 100)[20250519_010419.]: c(6.85513576175294, 1.2133339963707, 0.416965075684399, 5.6722564669446, 6.9722814054461, 0.609781828122202, 1.6963141100195, 3.0191552200728, 0)[20250519_010419.]: c(NA, 9.7066719709656, 1.66786030273759, 15.1260172451856, 13.9445628108922, 0.975650924995523, 2.26175214669267, 3.45046310865463, 0) + [20250519_010419.]: Entered 'hyperbolic_regression'-Function + [20250519_010419.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010419.]: Entered 'cubic_regression'-Function + [20250519_010419.]: 'cubic_regression': minmax = FALSE + [20250519_010419.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.27473119209009, 12.7004989900866, 24.2988465005425, 33.7074476249853, 47.0168800224423, 61.5718102366584, 76.091389954119, 79.6023799465703, 100)c(5.27473119209009, 0.200498990086601, 0.701153499457501, 3.7925523750147, 2.9831199775577, 0.928189763341599, 1.09138995411899, 7.8976200534297, 0)c(NA, 1.60399192069281, 2.80461399783, 10.1134730000392, 5.9662399551154, 1.48510362134656, 1.45518660549199, 9.02585148963395, 0) - [20250519_005546.]: Logging df_agg: CpG#3 - [20250519_005546.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005546.]: c(5.27473119209009, 12.7004989900866, 24.2988465005425, 33.7074476249853, 47.0168800224423, 61.5718102366584, 76.091389954119, 79.6023799465703, 100)[20250519_005546.]: c(5.27473119209009, 0.200498990086601, 0.701153499457501, 3.7925523750147, 2.9831199775577, 0.928189763341599, 1.09138995411899, 7.8976200534297, 0)[20250519_005546.]: c(NA, 1.60399192069281, 2.80461399783, 10.1134730000392, 5.9662399551154, 1.48510362134656, 1.45518660549199, 9.02585148963395, 0) - [20250519_005546.]: Entered 'hyperbolic_regression'-Function - [20250519_005546.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005546.]: Entered 'cubic_regression'-Function - [20250519_005546.]: 'cubic_regression': minmax = FALSE - [20250519_005546.]: # CpG-site: CpG#4 + [20250519_010419.]: Logging df_agg: CpG#3 + [20250519_010419.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010419.]: c(5.27473119209009, 12.7004989900866, 24.2988465005425, 33.7074476249853, 47.0168800224423, 61.5718102366584, 76.091389954119, 79.6023799465703, 100)[20250519_010419.]: c(5.27473119209009, 0.200498990086601, 0.701153499457501, 3.7925523750147, 2.9831199775577, 0.928189763341599, 1.09138995411899, 7.8976200534297, 0)[20250519_010419.]: c(NA, 1.60399192069281, 2.80461399783, 10.1134730000392, 5.9662399551154, 1.48510362134656, 1.45518660549199, 9.02585148963395, 0) + [20250519_010419.]: Entered 'hyperbolic_regression'-Function + [20250519_010419.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010419.]: Entered 'cubic_regression'-Function + [20250519_010419.]: 'cubic_regression': minmax = FALSE + [20250519_010419.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.07584235183922, 14.2021272601476, 23.203637284743, 34.7960863609446, 44.4638162018573, 64.2755507391393, 71.7670214785718, 82.5599568822802, 100)c(5.07584235183922, 1.7021272601476, 1.796362715257, 2.7039136390554, 5.5361837981427, 1.77555073913931, 3.23297852142819, 4.9400431177198, 0)c(NA, 13.6170180811808, 7.185450861028, 7.2104363708144, 11.0723675962854, 2.84088118262289, 4.31063802857093, 5.64576356310834, 0) - [20250519_005546.]: Logging df_agg: CpG#4 - [20250519_005546.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005546.]: c(5.07584235183922, 14.2021272601476, 23.203637284743, 34.7960863609446, 44.4638162018573, 64.2755507391393, 71.7670214785718, 82.5599568822802, 100)[20250519_005546.]: c(5.07584235183922, 1.7021272601476, 1.796362715257, 2.7039136390554, 5.5361837981427, 1.77555073913931, 3.23297852142819, 4.9400431177198, 0)[20250519_005546.]: c(NA, 13.6170180811808, 7.185450861028, 7.2104363708144, 11.0723675962854, 2.84088118262289, 4.31063802857093, 5.64576356310834, 0) - [20250519_005546.]: Entered 'hyperbolic_regression'-Function - [20250519_005546.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005546.]: Entered 'cubic_regression'-Function - [20250519_005546.]: 'cubic_regression': minmax = FALSE - [20250519_005546.]: # CpG-site: CpG#5 + [20250519_010419.]: Logging df_agg: CpG#4 + [20250519_010419.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010419.]: c(5.07584235183922, 14.2021272601476, 23.203637284743, 34.7960863609446, 44.4638162018573, 64.2755507391393, 71.7670214785718, 82.5599568822802, 100)[20250519_010419.]: c(5.07584235183922, 1.7021272601476, 1.796362715257, 2.7039136390554, 5.5361837981427, 1.77555073913931, 3.23297852142819, 4.9400431177198, 0)[20250519_010419.]: c(NA, 13.6170180811808, 7.185450861028, 7.2104363708144, 11.0723675962854, 2.84088118262289, 4.31063802857093, 5.64576356310834, 0) + [20250519_010419.]: Entered 'hyperbolic_regression'-Function + [20250519_010419.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010419.]: Entered 'cubic_regression'-Function + [20250519_010419.]: 'cubic_regression': minmax = FALSE + [20250519_010420.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.96411772281142, 12.4462106087166, 22.9738427992134, 34.1311793989123, 46.7456955019114, 58.6021949543013, 74.5100864344263, 86.8549692943716, 100)c(6.96411772281142, 0.0537893912834004, 2.0261572007866, 3.3688206010877, 3.2543044980886, 3.8978050456987, 0.489913565573701, 0.6450307056284, 0)c(NA, 0.430315130267203, 8.1046288031464, 8.98352160290053, 6.5086089961772, 6.23648807311793, 0.653218087431602, 0.7371779492896, 0) - [20250519_005546.]: Logging df_agg: CpG#5 - [20250519_005546.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005546.]: c(6.96411772281142, 12.4462106087166, 22.9738427992134, 34.1311793989123, 46.7456955019114, 58.6021949543013, 74.5100864344263, 86.8549692943716, 100)[20250519_005546.]: c(6.96411772281142, 0.0537893912834004, 2.0261572007866, 3.3688206010877, 3.2543044980886, 3.8978050456987, 0.489913565573701, 0.6450307056284, 0)[20250519_005546.]: c(NA, 0.430315130267203, 8.1046288031464, 8.98352160290053, 6.5086089961772, 6.23648807311793, 0.653218087431602, 0.7371779492896, 0) - [20250519_005546.]: Entered 'hyperbolic_regression'-Function - [20250519_005546.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005546.]: Entered 'cubic_regression'-Function - [20250519_005546.]: 'cubic_regression': minmax = FALSE - [20250519_005546.]: # CpG-site: CpG#6 + [20250519_010420.]: Logging df_agg: CpG#5 + [20250519_010420.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010420.]: c(6.96411772281142, 12.4462106087166, 22.9738427992134, 34.1311793989123, 46.7456955019114, 58.6021949543013, 74.5100864344263, 86.8549692943716, 100)[20250519_010420.]: c(6.96411772281142, 0.0537893912834004, 2.0261572007866, 3.3688206010877, 3.2543044980886, 3.8978050456987, 0.489913565573701, 0.6450307056284, 0)[20250519_010420.]: c(NA, 0.430315130267203, 8.1046288031464, 8.98352160290053, 6.5086089961772, 6.23648807311793, 0.653218087431602, 0.7371779492896, 0) + [20250519_010420.]: Entered 'hyperbolic_regression'-Function + [20250519_010420.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010420.]: Entered 'cubic_regression'-Function + [20250519_010420.]: 'cubic_regression': minmax = FALSE + [20250519_010420.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.82248549455073, 14.2907258003629, 24.7343949718638, 31.7110486196861, 44.7745870553137, 60.1336283719223, 70.3292384995337, 88.2655480529845, 100)c(6.82248549455073, 1.7907258003629, 0.2656050281362, 5.7889513803139, 5.2254129446863, 2.3663716280777, 4.6707615004663, 0.765548052984499, 0)c(NA, 14.3258064029032, 1.0624201125448, 15.4372036808371, 10.4508258893726, 3.78619460492432, 6.22768200062173, 0.874912060553714, 0) - [20250519_005546.]: Logging df_agg: CpG#6 - [20250519_005546.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005546.]: c(6.82248549455073, 14.2907258003629, 24.7343949718638, 31.7110486196861, 44.7745870553137, 60.1336283719223, 70.3292384995337, 88.2655480529845, 100)[20250519_005546.]: c(6.82248549455073, 1.7907258003629, 0.2656050281362, 5.7889513803139, 5.2254129446863, 2.3663716280777, 4.6707615004663, 0.765548052984499, 0)[20250519_005546.]: c(NA, 14.3258064029032, 1.0624201125448, 15.4372036808371, 10.4508258893726, 3.78619460492432, 6.22768200062173, 0.874912060553714, 0) - [20250519_005546.]: Entered 'hyperbolic_regression'-Function - [20250519_005546.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005546.]: Entered 'cubic_regression'-Function - [20250519_005546.]: 'cubic_regression': minmax = FALSE - [20250519_005546.]: # CpG-site: CpG#7 + [20250519_010420.]: Logging df_agg: CpG#6 + [20250519_010420.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010420.]: c(6.82248549455073, 14.2907258003629, 24.7343949718638, 31.7110486196861, 44.7745870553137, 60.1336283719223, 70.3292384995337, 88.2655480529845, 100)[20250519_010420.]: c(6.82248549455073, 1.7907258003629, 0.2656050281362, 5.7889513803139, 5.2254129446863, 2.3663716280777, 4.6707615004663, 0.765548052984499, 0)[20250519_010420.]: c(NA, 14.3258064029032, 1.0624201125448, 15.4372036808371, 10.4508258893726, 3.78619460492432, 6.22768200062173, 0.874912060553714, 0) + [20250519_010420.]: Entered 'hyperbolic_regression'-Function + [20250519_010420.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010420.]: Entered 'cubic_regression'-Function + [20250519_010420.]: 'cubic_regression': minmax = FALSE + [20250519_010420.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)c(6.75841824784972, 13.6258602138932, 23.2672219789518, 33.4287658578254, 47.5236851621158, 60.5558050598499, 72.3896034879297, 77.2628610417933)c(6.75841824784972, 1.1258602138932, 1.7327780210482, 4.0712341421746, 2.4763148378842, 1.9441949401501, 2.6103965120703, 10.2371389582067)c(NA, 9.0068817111456, 6.9311120841928, 10.8566243791323, 4.95262967576841, 3.11071190424016, 3.48052868276039, 11.6995873808077) - [20250519_005546.]: Logging df_agg: CpG#7 - [20250519_005546.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)[20250519_005546.]: c(6.75841824784972, 13.6258602138932, 23.2672219789518, 33.4287658578254, 47.5236851621158, 60.5558050598499, 72.3896034879297, 77.2628610417933)[20250519_005546.]: c(6.75841824784972, 1.1258602138932, 1.7327780210482, 4.0712341421746, 2.4763148378842, 1.9441949401501, 2.6103965120703, 10.2371389582067)[20250519_005546.]: c(NA, 9.0068817111456, 6.9311120841928, 10.8566243791323, 4.95262967576841, 3.11071190424016, 3.48052868276039, 11.6995873808077) - [20250519_005546.]: Entered 'hyperbolic_regression'-Function - [20250519_005546.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005546.]: Entered 'cubic_regression'-Function - [20250519_005546.]: 'cubic_regression': minmax = FALSE - [20250519_005546.]: # CpG-site: CpG#8 + [20250519_010420.]: Logging df_agg: CpG#7 + [20250519_010420.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)[20250519_010420.]: c(6.75841824784972, 13.6258602138932, 23.2672219789518, 33.4287658578254, 47.5236851621158, 60.5558050598499, 72.3896034879297, 77.2628610417933)[20250519_010420.]: c(6.75841824784972, 1.1258602138932, 1.7327780210482, 4.0712341421746, 2.4763148378842, 1.9441949401501, 2.6103965120703, 10.2371389582067)[20250519_010420.]: c(NA, 9.0068817111456, 6.9311120841928, 10.8566243791323, 4.95262967576841, 3.11071190424016, 3.48052868276039, 11.6995873808077) + [20250519_010420.]: Entered 'hyperbolic_regression'-Function + [20250519_010420.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010420.]: Entered 'cubic_regression'-Function + [20250519_010420.]: 'cubic_regression': minmax = FALSE + [20250519_010420.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.74253131510683, 10.9055216254216, 24.5329419436721, 32.1516426249373, 49.0919870496865, 62.3843810658735, 77.6235905386846, 80.9968312605936, 100)c(5.74253131510683, 1.5944783745784, 0.4670580563279, 5.3483573750627, 0.908012950313498, 0.115618934126502, 2.6235905386846, 6.50316873940641, 0)c(NA, 12.7558269966272, 1.8682322253116, 14.2622863335005, 1.816025900627, 0.184990294602403, 3.49812071824613, 7.43219284503589, 0) - [20250519_005546.]: Logging df_agg: CpG#8 - [20250519_005546.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005546.]: c(5.74253131510683, 10.9055216254216, 24.5329419436721, 32.1516426249373, 49.0919870496865, 62.3843810658735, 77.6235905386846, 80.9968312605936, 100)[20250519_005546.]: c(5.74253131510683, 1.5944783745784, 0.4670580563279, 5.3483573750627, 0.908012950313498, 0.115618934126502, 2.6235905386846, 6.50316873940641, 0)[20250519_005546.]: c(NA, 12.7558269966272, 1.8682322253116, 14.2622863335005, 1.816025900627, 0.184990294602403, 3.49812071824613, 7.43219284503589, 0) - [20250519_005546.]: Entered 'hyperbolic_regression'-Function - [20250519_005546.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005546.]: Entered 'cubic_regression'-Function - [20250519_005546.]: 'cubic_regression': minmax = FALSE - [20250519_005546.]: # CpG-site: CpG#9 + [20250519_010420.]: Logging df_agg: CpG#8 + [20250519_010420.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010420.]: c(5.74253131510683, 10.9055216254216, 24.5329419436721, 32.1516426249373, 49.0919870496865, 62.3843810658735, 77.6235905386846, 80.9968312605936, 100)[20250519_010420.]: c(5.74253131510683, 1.5944783745784, 0.4670580563279, 5.3483573750627, 0.908012950313498, 0.115618934126502, 2.6235905386846, 6.50316873940641, 0)[20250519_010420.]: c(NA, 12.7558269966272, 1.8682322253116, 14.2622863335005, 1.816025900627, 0.184990294602403, 3.49812071824613, 7.43219284503589, 0) + [20250519_010420.]: Entered 'hyperbolic_regression'-Function + [20250519_010420.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010420.]: Entered 'cubic_regression'-Function + [20250519_010420.]: 'cubic_regression': minmax = FALSE + [20250519_010420.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)c(8.66981641228949, 14.9680920996829, 24.6496838227806, 31.5444702961411, 44.0944303975514, 57.2478940106457, 69.1518417879612, 83.8189177560508)c(8.66981641228949, 2.4680920996829, 0.350316177219401, 5.9555297038589, 5.9055696024486, 5.2521059893543, 5.8481582120388, 3.68108224394921)c(NA, 19.7447367974632, 1.4012647088776, 15.8814125436237, 11.8111392048972, 8.40336958296688, 7.7975442827184, 4.20695113594195) - [20250519_005546.]: Logging df_agg: CpG#9 - [20250519_005546.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)[20250519_005546.]: c(8.66981641228949, 14.9680920996829, 24.6496838227806, 31.5444702961411, 44.0944303975514, 57.2478940106457, 69.1518417879612, 83.8189177560508)[20250519_005546.]: c(8.66981641228949, 2.4680920996829, 0.350316177219401, 5.9555297038589, 5.9055696024486, 5.2521059893543, 5.8481582120388, 3.68108224394921)[20250519_005546.]: c(NA, 19.7447367974632, 1.4012647088776, 15.8814125436237, 11.8111392048972, 8.40336958296688, 7.7975442827184, 4.20695113594195) - [20250519_005546.]: Entered 'hyperbolic_regression'-Function - [20250519_005546.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005546.]: Entered 'cubic_regression'-Function - [20250519_005546.]: 'cubic_regression': minmax = FALSE - [20250519_005546.]: # CpG-site: row_means + [20250519_010420.]: Logging df_agg: CpG#9 + [20250519_010420.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)[20250519_010420.]: c(8.66981641228949, 14.9680920996829, 24.6496838227806, 31.5444702961411, 44.0944303975514, 57.2478940106457, 69.1518417879612, 83.8189177560508)[20250519_010420.]: c(8.66981641228949, 2.4680920996829, 0.350316177219401, 5.9555297038589, 5.9055696024486, 5.2521059893543, 5.8481582120388, 3.68108224394921)[20250519_010420.]: c(NA, 19.7447367974632, 1.4012647088776, 15.8814125436237, 11.8111392048972, 8.40336958296688, 7.7975442827184, 4.20695113594195) + [20250519_010420.]: Entered 'hyperbolic_regression'-Function + [20250519_010420.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010420.]: Entered 'cubic_regression'-Function + [20250519_010420.]: 'cubic_regression': minmax = FALSE + [20250519_010420.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)c(6.47354965864506, 13.5375540918194, 23.976024251167, 32.9614416466039, 45.8185453601259, 60.9295837234236, 72.9250251350637, 82.8721280175692)c(6.47354965864506, 1.0375540918194, 1.023975748833, 4.5385583533961, 4.1814546398741, 1.5704162765764, 2.07497486493629, 4.6278719824308)c(NA, 8.3004327345552, 4.095902995332, 12.1028222757229, 8.36290927974819, 2.51266604252224, 2.76663315324839, 5.28899655134949) - [20250519_005546.]: Logging df_agg: row_means - [20250519_005546.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)[20250519_005546.]: c(6.47354965864506, 13.5375540918194, 23.976024251167, 32.9614416466039, 45.8185453601259, 60.9295837234236, 72.9250251350637, 82.8721280175692)[20250519_005546.]: c(6.47354965864506, 1.0375540918194, 1.023975748833, 4.5385583533961, 4.1814546398741, 1.5704162765764, 2.07497486493629, 4.6278719824308)[20250519_005546.]: c(NA, 8.3004327345552, 4.095902995332, 12.1028222757229, 8.36290927974819, 2.51266604252224, 2.76663315324839, 5.28899655134949) - [20250519_005546.]: Entered 'hyperbolic_regression'-Function - [20250519_005546.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005546.]: Entered 'cubic_regression'-Function - [20250519_005546.]: 'cubic_regression': minmax = FALSE - [20250519_005547.]: ### Starting with plotting ### - [20250519_005547.]: Creating BiasCorrected (hyperbolic) plot No. 1 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG1_corrected_h.png - [20250519_005547.]: # CpG-site: CpG#1 + [20250519_010420.]: Logging df_agg: row_means + [20250519_010420.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5)[20250519_010420.]: c(6.47354965864506, 13.5375540918194, 23.976024251167, 32.9614416466039, 45.8185453601259, 60.9295837234236, 72.9250251350637, 82.8721280175692)[20250519_010420.]: c(6.47354965864506, 1.0375540918194, 1.023975748833, 4.5385583533961, 4.1814546398741, 1.5704162765764, 2.07497486493629, 4.6278719824308)[20250519_010420.]: c(NA, 8.3004327345552, 4.095902995332, 12.1028222757229, 8.36290927974819, 2.51266604252224, 2.76663315324839, 5.28899655134949) + [20250519_010420.]: Entered 'hyperbolic_regression'-Function + [20250519_010420.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010420.]: Entered 'cubic_regression'-Function + [20250519_010420.]: 'cubic_regression': minmax = FALSE + [20250519_010420.]: ### Starting with plotting ### + [20250519_010420.]: Creating BiasCorrected (hyperbolic) plot No. 1 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG1_corrected_h.png + [20250519_010420.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = 170413234.729972 , a = 44157086.707734 , b = 170413234.729972 , d = 49999790.9017601 - [20250519_005547.]: # CpG-site: CpG#1 + [20250519_010420.]: # CpG-site: CpG#1 Cubic: Using a = -8.02902858014277e-05 , b = 0.0119973831380665 , c = 0.410459267079284 , d = 6.65096545514267 - [20250519_005549.]: Creating BiasCorrected (hyperbolic) plot No. 2 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG2_corrected_h.png - [20250519_005549.]: # CpG-site: CpG#2 + [20250519_010422.]: Creating BiasCorrected (hyperbolic) plot No. 2 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG2_corrected_h.png + [20250519_010422.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = 63519198.3583519 , a = 42702123.5062977 , b = 63519198.3583519 , d = 44879882.4102916 - [20250519_005549.]: # CpG-site: CpG#2 + [20250519_010422.]: # CpG-site: CpG#2 Cubic: Using a = -4.57917988885898e-05 , b = 0.00978975326683099 , c = 0.400365566888902 , d = 7.17765330717532 - [20250519_005550.]: Creating BiasCorrected (hyperbolic) plot No. 3 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG3_corrected_h.png - [20250519_005550.]: # CpG-site: CpG#3 + [20250519_010424.]: Creating BiasCorrected (hyperbolic) plot No. 3 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG3_corrected_h.png + [20250519_010424.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = 60249784.2121432 , a = 37717764.1245422 , b = 60249784.2121432 , d = 39783429.2823954 - [20250519_005550.]: # CpG-site: CpG#3 + [20250519_010424.]: # CpG-site: CpG#3 Cubic: Using a = -2.8548625146914e-05 , b = 0.00587956303749884 , c = 0.626869802626483 , d = 4.78057876315863 - [20250519_005552.]: Creating BiasCorrected (hyperbolic) plot No. 4 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG4_corrected_h.png - [20250519_005552.]: # CpG-site: CpG#4 + [20250519_010426.]: Creating BiasCorrected (hyperbolic) plot No. 4 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG4_corrected_h.png + [20250519_010426.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = 66787089.0359508 , a = 42175722.8129271 , b = 66787089.0359508 , d = 44465523.7860812 - [20250519_005552.]: # CpG-site: CpG#4 + [20250519_010426.]: # CpG-site: CpG#4 Cubic: Using a = -1.99135996570308e-05 , b = 0.00496936895290719 , c = 0.637632604471187 , d = 5.04633670060351 - [20250519_005554.]: Creating BiasCorrected (hyperbolic) plot No. 5 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG5_corrected_h.png - [20250519_005554.]: # CpG-site: CpG#5 + [20250519_010428.]: Creating BiasCorrected (hyperbolic) plot No. 5 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG5_corrected_h.png + [20250519_010428.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = 57501729.1236441 , a = 52618093.853152 , b = 57501729.1236441 , d = 54589535.3828564 - [20250519_005554.]: # CpG-site: CpG#5 + [20250519_010428.]: # CpG-site: CpG#5 Cubic: Using a = -4.67907948499236e-05 , b = 0.00978122250124343 , c = 0.42296364991001 , d = 6.61748738798833 - [20250519_005556.]: Creating BiasCorrected (hyperbolic) plot No. 6 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG6_corrected_h.png - [20250519_005556.]: # CpG-site: CpG#6 + [20250519_010429.]: Creating BiasCorrected (hyperbolic) plot No. 6 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG6_corrected_h.png + [20250519_010429.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = 62067256.5784193 , a = 42512767.1502548 , b = 62067256.5784193 , d = 44640742.1604705 - [20250519_005556.]: # CpG-site: CpG#6 + [20250519_010429.]: # CpG-site: CpG#6 Cubic: Using a = -2.66665530276184e-05 , b = 0.00740474965950072 , c = 0.461019694351388 , d = 7.23067904029629 - [20250519_005558.]: Creating BiasCorrected (hyperbolic) plot No. 7 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG7_corrected_h.png - [20250519_005558.]: # CpG-site: CpG#7 + [20250519_010431.]: Creating BiasCorrected (hyperbolic) plot No. 7 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG7_corrected_h.png + [20250519_010431.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = 162158036.04427 , a = 37148305.7394653 , b = 162158036.04427 , d = 42707972.2066412 - [20250519_005558.]: # CpG-site: CpG#7 + [20250519_010431.]: # CpG-site: CpG#7 Cubic: Using a = -0.000133937721266082 , b = 0.0182624909642956 , c = 0.234528921247686 , d = 7.29090555727988 - [20250519_005600.]: Creating BiasCorrected (hyperbolic) plot No. 8 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG8_corrected_h.png - [20250519_005600.]: # CpG-site: CpG#8 + [20250519_010433.]: Creating BiasCorrected (hyperbolic) plot No. 8 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG8_corrected_h.png + [20250519_010433.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = 54899408.7962443 , a = 51832403.6152701 , b = 54899408.7962443 , d = 53714624.7099072 - [20250519_005600.]: # CpG-site: CpG#8 + [20250519_010433.]: # CpG-site: CpG#8 Cubic: Using a = -5.74524696829791e-05 , b = 0.00998862677056794 , c = 0.502915955578275 , d = 4.90625922077111 - [20250519_005601.]: Creating BiasCorrected (hyperbolic) plot No. 9 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG9_corrected_h.png - [20250519_005601.]: # CpG-site: CpG#9 + [20250519_010435.]: Creating BiasCorrected (hyperbolic) plot No. 9 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG9_corrected_h.png + [20250519_010435.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = 170227220.836014 , a = 37110575.5394583 , b = 170227220.836014 , d = 42946902.7624502 - [20250519_005601.]: # CpG-site: CpG#9 + [20250519_010435.]: # CpG-site: CpG#9 Cubic: Using a = -1.38503636743099e-05 , b = 0.00591895875024497 , c = 0.445742549227169 , d = 8.73330013353405 - [20250519_005603.]: Creating BiasCorrected (hyperbolic) plot No. 10 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_rowmeans_corrected_h.png - [20250519_005603.]: # CpG-site: row_means + [20250519_010437.]: Creating BiasCorrected (hyperbolic) plot No. 10 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_rowmeans_corrected_h.png + [20250519_010437.]: # CpG-site: row_means Hyperbolic: Using bias_weight = 131846991.813373 , a = 45678337.0783695 , b = 131846991.813373 , d = 50198768.4229237 - [20250519_005603.]: # CpG-site: row_means + [20250519_010437.]: # CpG-site: row_means Cubic: Using a = -7.67916401132796e-05 , b = 0.0124890194887903 , c = 0.369098504101218 , d = 6.83740913189534 - [20250519_005605.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG1_corrected_h.png - [20250519_005607.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG2_corrected_h.png - [20250519_005609.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG3_corrected_h.png - [20250519_005611.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG4_corrected_h.png - [20250519_005612.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG5_corrected_h.png - [20250519_005614.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG6_corrected_h.png - [20250519_005616.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG7_corrected_h.png - [20250519_005618.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG8_corrected_h.png - [20250519_005619.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG9_corrected_h.png - [20250519_005621.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_rowmeans_corrected_h.png - [20250519_005623.]: Entered 'solving_equations'-Function - [20250519_005623.]: Solving cubic regression for CpG#1 + [20250519_010438.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG1_corrected_h.png + [20250519_010440.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG2_corrected_h.png + [20250519_010442.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG3_corrected_h.png + [20250519_010444.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG4_corrected_h.png + [20250519_010445.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG5_corrected_h.png + [20250519_010447.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG6_corrected_h.png + [20250519_010449.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG7_corrected_h.png + [20250519_010451.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG8_corrected_h.png + [20250519_010452.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG9_corrected_h.png + [20250519_010454.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_rowmeans_corrected_h.png + [20250519_010456.]: Entered 'solving_equations'-Function + [20250519_010456.]: Solving cubic regression for CpG#1 Coefficients: -1.03617340067344Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_005623.]: Samplename: 0 + [20250519_010456.]: Samplename: 0 Root: 1.334 --> Root in between the borders! Added to results. Coefficients: -8.34150673400678Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_005623.]: Samplename: 12.5 + [20250519_010456.]: Samplename: 12.5 Root: 11.446 --> Root in between the borders! Added to results. Coefficients: -15.3881734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_005623.]: Samplename: 25 + [20250519_010456.]: Samplename: 25 Root: 22.228 --> Root in between the borders! Added to results. Coefficients: -24.2801734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_005623.]: Samplename: 37.5 + [20250519_010456.]: Samplename: 37.5 Root: 36.374 --> Root in between the borders! Added to results. Coefficients: -34.9006734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_005623.]: Samplename: 50 + [20250519_010456.]: Samplename: 50 Root: 52.044 --> Root in between the borders! Added to results. Coefficients: -46.3541734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_005623.]: Samplename: 62.5 + [20250519_010456.]: Samplename: 62.5 Root: 66.144 --> Root in between the borders! Added to results. Coefficients: -55.8931734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_005623.]: Samplename: 75 + [20250519_010456.]: Samplename: 75 Root: 75.864 --> Root in between the borders! Added to results. Coefficients: -63.0981734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_005623.]: Samplename: 87.5 + [20250519_010456.]: Samplename: 87.5 Root: 82.254 --> Root in between the borders! Added to results. Coefficients: -91.0461734006735Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_005623.]: Samplename: 100 + [20250519_010456.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.877 --> '100 < root < 110' --> substitute 100 - [20250519_005623.]: Solving cubic regression for CpG#2 + [20250519_010456.]: Solving cubic regression for CpG#2 Coefficients: -0.283329966329966Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_005623.]: Samplename: 0 + [20250519_010456.]: Samplename: 0 Root: 0.549 --> Root in between the borders! Added to results. Coefficients: -6.33999663299663Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_005623.]: Samplename: 12.5 + [20250519_010456.]: Samplename: 12.5 Root: 11.533 --> Root in between the borders! Added to results. Coefficients: -15.93932996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_005623.]: Samplename: 25 + [20250519_010456.]: Samplename: 25 Root: 26.628 --> Root in between the borders! Added to results. Coefficients: -22.33732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_005623.]: Samplename: 37.5 + [20250519_010456.]: Samplename: 37.5 Root: 35.509 --> Root in between the borders! Added to results. Coefficients: -32.22832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_005623.]: Samplename: 50 + [20250519_010456.]: Samplename: 50 Root: 47.851 --> Root in between the borders! Added to results. Coefficients: -49.96332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_005623.]: Samplename: 62.5 + [20250519_010456.]: Samplename: 62.5 Root: 66.893 --> Root in between the borders! Added to results. Coefficients: -58.96582996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_005623.]: Samplename: 75 + [20250519_010456.]: Samplename: 75 Root: 75.431 --> Root in between the borders! Added to results. Coefficients: -68.8366632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_005623.]: Samplename: 87.5 + [20250519_010456.]: Samplename: 87.5 Root: 84.118 --> Root in between the borders! Added to results. Coefficients: -90.57732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_005623.]: Samplename: 100 + [20250519_010456.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.287 --> '100 < root < 110' --> substitute 100 - [20250519_005623.]: Solving cubic regression for CpG#3 + [20250519_010456.]: Solving cubic regression for CpG#3 Coefficients: -0.90294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_005623.]: Samplename: 0 + [20250519_010456.]: Samplename: 0 Root: 1.441 --> Root in between the borders! Added to results. Coefficients: -6.57294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_005623.]: Samplename: 12.5 + [20250519_010456.]: Samplename: 12.5 Root: 10.568 --> Root in between the borders! Added to results. Coefficients: -15.4289478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_005623.]: Samplename: 25 + [20250519_010456.]: Samplename: 25 Root: 24.796 --> Root in between the borders! Added to results. Coefficients: -22.6129478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_005623.]: Samplename: 37.5 + [20250519_010456.]: Samplename: 37.5 Root: 35.952 --> Root in between the borders! Added to results. Coefficients: -32.7754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_005623.]: Samplename: 50 + [20250519_010456.]: Samplename: 50 Root: 50.684 --> Root in between the borders! Added to results. Coefficients: -43.8889478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_005623.]: Samplename: 62.5 + [20250519_010456.]: Samplename: 62.5 Root: 65.142 --> Root in between the borders! Added to results. Coefficients: -54.9754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_005623.]: Samplename: 75 + [20250519_010456.]: Samplename: 75 Root: 77.905 --> Root in between the borders! Added to results. Coefficients: -57.6562811447812Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_005623.]: Samplename: 87.5 + [20250519_010456.]: Samplename: 87.5 Root: 80.767 --> Root in between the borders! Added to results. Coefficients: -80.6649478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_005623.]: Samplename: 100 + [20250519_010456.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.38 --> '100 < root < 110' --> substitute 100 - [20250519_005623.]: Solving cubic regression for CpG#4 + [20250519_010456.]: Solving cubic regression for CpG#4 Coefficients: -0.597449494949524Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_005623.]: Samplename: 0 + [20250519_010456.]: Samplename: 0 Root: 0.858 --> Root in between the borders! Added to results. Coefficients: -8.25278282828286Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_005623.]: Samplename: 12.5 + [20250519_010456.]: Samplename: 12.5 Root: 12.086 --> Root in between the borders! Added to results. Coefficients: -15.8034494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_005623.]: Samplename: 25 + [20250519_010456.]: Samplename: 25 Root: 23.316 --> Root in between the borders! Added to results. Coefficients: -25.5274494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_005623.]: Samplename: 37.5 + [20250519_010456.]: Samplename: 37.5 Root: 37.383 --> Root in between the borders! Added to results. Coefficients: -33.6369494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_005623.]: Samplename: 50 + [20250519_010456.]: Samplename: 50 Root: 48.353 --> Root in between the borders! Added to results. Coefficients: -50.2554494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_005623.]: Samplename: 62.5 + [20250519_010456.]: Samplename: 62.5 Root: 68.082 --> Root in between the borders! Added to results. Coefficients: -56.5394494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_005623.]: Samplename: 75 + [20250519_010456.]: Samplename: 75 Root: 74.615 --> Root in between the borders! Added to results. Coefficients: -65.5927828282829Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_005623.]: Samplename: 87.5 + [20250519_010456.]: Samplename: 87.5 Root: 83.254 --> Root in between the borders! Added to results. Coefficients: -88.3214494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_005623.]: Samplename: 100 + [20250519_010456.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.715 --> '100 < root < 110' --> substitute 100 - [20250519_005623.]: Solving cubic regression for CpG#5 + [20250519_010456.]: Solving cubic regression for CpG#5 Coefficients: -0.623961279461278Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_005623.]: Samplename: 0 + [20250519_010456.]: Samplename: 0 Root: 1.458 --> Root in between the borders! Added to results. Coefficients: -4.76796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_005623.]: Samplename: 12.5 + [20250519_010456.]: Samplename: 12.5 Root: 10.347 --> Root in between the borders! Added to results. Coefficients: -12.7259612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_005623.]: Samplename: 25 + [20250519_010456.]: Samplename: 25 Root: 24.815 --> Root in between the borders! Added to results. Coefficients: -21.1599612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_005623.]: Samplename: 37.5 + [20250519_010456.]: Samplename: 37.5 Root: 37.902 --> Root in between the borders! Added to results. Coefficients: -30.6954612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_005623.]: Samplename: 50 + [20250519_010456.]: Samplename: 50 Root: 50.977 --> Root in between the borders! Added to results. Coefficients: -39.6579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_005623.]: Samplename: 62.5 + [20250519_010456.]: Samplename: 62.5 Root: 62.126 --> Root in between the borders! Added to results. Coefficients: -51.6829612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_005623.]: Samplename: 75 + [20250519_010456.]: Samplename: 75 Root: 75.852 --> Root in between the borders! Added to results. Coefficients: -61.0146279461279Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_005623.]: Samplename: 87.5 + [20250519_010456.]: Samplename: 87.5 Root: 85.767 --> Root in between the borders! Added to results. Coefficients: -76.0699612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_005623.]: Samplename: 100 + [20250519_010456.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.743 --> '100 < root < 110' --> substitute 100 - [20250519_005623.]: Solving cubic regression for CpG#6 + [20250519_010456.]: Solving cubic regression for CpG#6 Coefficients: -0.196072390572403Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_005623.]: Samplename: 0 + [20250519_010456.]: Samplename: 0 Root: 0.349 --> Root in between the borders! Added to results. Coefficients: -6.73873905723907Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_005623.]: Samplename: 12.5 + [20250519_010456.]: Samplename: 12.5 Root: 11.718 --> Root in between the borders! Added to results. Coefficients: -15.8880723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_005623.]: Samplename: 25 + [20250519_010456.]: Samplename: 25 Root: 26.396 --> Root in between the borders! Added to results. Coefficients: -22.0000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_005623.]: Samplename: 37.5 + [20250519_010456.]: Samplename: 37.5 Root: 35.301 --> Root in between the borders! Added to results. Coefficients: -33.4445723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_005623.]: Samplename: 50 + [20250519_010456.]: Samplename: 50 Root: 50.134 --> Root in between the borders! Added to results. Coefficients: -46.9000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_005623.]: Samplename: 62.5 + [20250519_010456.]: Samplename: 62.5 Root: 64.993 --> Root in between the borders! Added to results. Coefficients: -55.8320723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_005623.]: Samplename: 75 + [20250519_010456.]: Samplename: 75 Root: 73.639 --> Root in between the borders! Added to results. Coefficients: -71.5454057239057Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_005623.]: Samplename: 87.5 + [20250519_010456.]: Samplename: 87.5 Root: 87.043 --> Root in between the borders! Added to results. Coefficients: -89.6560723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_005623.]: Samplename: 100 + [20250519_010456.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.329 --> '100 < root < 110' --> substitute 100 - [20250519_005623.]: Solving cubic regression for CpG#7 + [20250519_010456.]: Solving cubic regression for CpG#7 Coefficients: -1.21495454545456Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_005623.]: Samplename: 0 + [20250519_010456.]: Samplename: 0 Root: 2.13 --> Root in between the borders! Added to results. Coefficients: -5.39562121212123Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_005623.]: Samplename: 12.5 + [20250519_010456.]: Samplename: 12.5 Root: 9.973 --> Root in between the borders! Added to results. Coefficients: -11.2649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_005623.]: Samplename: 25 + [20250519_010456.]: Samplename: 25 Root: 22.206 --> Root in between the borders! Added to results. Coefficients: -17.4509545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_005623.]: Samplename: 37.5 + [20250519_010456.]: Samplename: 37.5 Root: 35.814 --> Root in between the borders! Added to results. Coefficients: -26.0314545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_005623.]: Samplename: 50 + [20250519_010456.]: Samplename: 50 Root: 53.28 --> Root in between the borders! Added to results. Coefficients: -33.9649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_005623.]: Samplename: 62.5 + [20250519_010456.]: Samplename: 62.5 Root: 66.598 --> Root in between the borders! Added to results. Coefficients: -41.1689545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_005623.]: Samplename: 75 + [20250519_010456.]: Samplename: 75 Root: 76.575 --> Root in between the borders! Added to results. Coefficients: -44.1356212121212Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_005623.]: Samplename: 87.5 + [20250519_010456.]: Samplename: 87.5 Root: 80.219 --> Root in between the borders! Added to results. Coefficients: -67.2229545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_005623.]: Samplename: 100 + [20250519_010456.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.506 --> '100 < root < 110' --> substitute 100 - [20250519_005623.]: Solving cubic regression for CpG#8 + [20250519_010456.]: Solving cubic regression for CpG#8 Coefficients: -1.09618518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_005623.]: Samplename: 0 + [20250519_010456.]: Samplename: 0 Root: 2.016 --> Root in between the borders! Added to results. Coefficients: -5.44685185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_005623.]: Samplename: 12.5 + [20250519_010456.]: Samplename: 12.5 Root: 9.458 --> Root in between the borders! Added to results. Coefficients: -16.9301851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_005623.]: Samplename: 25 + [20250519_010456.]: Samplename: 25 Root: 26.35 --> Root in between the borders! Added to results. Coefficients: -23.3501851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_005623.]: Samplename: 37.5 + [20250519_010456.]: Samplename: 37.5 Root: 34.728 --> Root in between the borders! Added to results. Coefficients: -37.6251851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_005623.]: Samplename: 50 + [20250519_010456.]: Samplename: 50 Root: 51.781 --> Root in between the borders! Added to results. Coefficients: -48.8261851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_005623.]: Samplename: 62.5 + [20250519_010456.]: Samplename: 62.5 Root: 64.192 --> Root in between the borders! Added to results. Coefficients: -61.6676851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_005623.]: Samplename: 75 + [20250519_010456.]: Samplename: 75 Root: 77.804 --> Root in between the borders! Added to results. Coefficients: -64.5101851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_005623.]: Samplename: 87.5 + [20250519_010456.]: Samplename: 87.5 Root: 80.758 --> Root in between the borders! Added to results. Coefficients: -86.0601851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_005623.]: Samplename: 100 + [20250519_010456.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.834 --> '100 < root < 110' --> substitute 100 - [20250519_005623.]: Solving cubic regression for CpG#9 + [20250519_010456.]: Solving cubic regression for CpG#9 Coefficients: -0.989865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_005623.]: Samplename: 0 + [20250519_010456.]: Samplename: 0 Root: 1.475 --> Root in between the borders! Added to results. Coefficients: -6.39586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_005623.]: Samplename: 12.5 + [20250519_010456.]: Samplename: 12.5 Root: 10.12 --> Root in between the borders! Added to results. Coefficients: -14.7058653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_005623.]: Samplename: 25 + [20250519_010456.]: Samplename: 25 Root: 24.844 --> Root in between the borders! Added to results. Coefficients: -20.6238653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_005623.]: Samplename: 37.5 + [20250519_010456.]: Samplename: 37.5 Root: 35.327 --> Root in between the borders! Added to results. Coefficients: -31.3958653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_005623.]: Samplename: 50 + [20250519_010456.]: Samplename: 50 Root: 51.855 --> Root in between the borders! Added to results. Coefficients: -42.6858653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_005623.]: Samplename: 62.5 + [20250519_010456.]: Samplename: 62.5 Root: 65.265 --> Root in between the borders! Added to results. Coefficients: -52.9033653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_005623.]: Samplename: 75 + [20250519_010456.]: Samplename: 75 Root: 74.915 --> Root in between the borders! Added to results. Coefficients: -65.492531986532Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_005623.]: Samplename: 87.5 + [20250519_010456.]: Samplename: 87.5 Root: 84.67 --> Root in between the borders! Added to results. Coefficients: -92.9898653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_005623.]: Samplename: 100 + [20250519_010456.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.082 --> '100 < root < 110' --> substitute 100 - [20250519_005623.]: Solving cubic regression for row_means + [20250519_010456.]: Solving cubic regression for row_means Coefficients: -0.771215488215478Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_005623.]: Samplename: 0 + [20250519_010456.]: Samplename: 0 Root: 1.287 --> Root in between the borders! Added to results. Coefficients: -6.47247474747474Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_005623.]: Samplename: 12.5 + [20250519_010456.]: Samplename: 12.5 Root: 10.847 --> Root in between the borders! Added to results. Coefficients: -14.8972154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_005623.]: Samplename: 25 + [20250519_010456.]: Samplename: 25 Root: 24.737 --> Root in between the borders! Added to results. Coefficients: -22.1492154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_005623.]: Samplename: 37.5 + [20250519_010456.]: Samplename: 37.5 Root: 36.02 --> Root in between the borders! Added to results. Coefficients: -32.5259932659933Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_005623.]: Samplename: 50 + [20250519_010456.]: Samplename: 50 Root: 50.639 --> Root in between the borders! Added to results. Coefficients: -44.7218821548821Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_005623.]: Samplename: 62.5 + [20250519_010456.]: Samplename: 62.5 Root: 65.497 --> Root in between the borders! Added to results. Coefficients: -54.4032154882155Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_005623.]: Samplename: 75 + [20250519_010456.]: Samplename: 75 Root: 75.751 --> Root in between the borders! Added to results. Coefficients: -62.4313636363636Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_005623.]: Samplename: 87.5 + [20250519_010456.]: Samplename: 87.5 Root: 83.403 --> Root in between the borders! Added to results. Coefficients: -84.7343265993266Coefficients: 0.600117857944524Coefficients: -0.0006299592752926Coefficients: 2.88923726150392e-05 - [20250519_005623.]: Samplename: 100 + [20250519_010456.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.573 --> '100 < root < 110' --> substitute 100 - [20250519_005623.]: + [20250519_010456.]: ### Starting with regression calculations ### - [20250519_005623.]: Entered 'regression_type1'-Function - [20250519_005623.]: # CpG-site: CpG#1 + [20250519_010456.]: Entered 'regression_type1'-Function + [20250519_010456.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) - [20250519_005624.]: Logging df_agg: CpG#1 - [20250519_005624.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005624.]: c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)[20250519_005624.]: c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)[20250519_005624.]: c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) - [20250519_005624.]: Entered 'hyperbolic_regression'-Function - [20250519_005624.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005624.]: Entered 'cubic_regression'-Function - [20250519_005624.]: 'cubic_regression': minmax = FALSE - [20250519_005624.]: # CpG-site: CpG#2 + [20250519_010456.]: Logging df_agg: CpG#1 + [20250519_010456.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010456.]: c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)[20250519_010456.]: c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)[20250519_010456.]: c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) + [20250519_010456.]: Entered 'hyperbolic_regression'-Function + [20250519_010456.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010456.]: Entered 'cubic_regression'-Function + [20250519_010456.]: 'cubic_regression': minmax = FALSE + [20250519_010456.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) - [20250519_005624.]: Logging df_agg: CpG#2 - [20250519_005624.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005624.]: c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)[20250519_005624.]: c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)[20250519_005624.]: c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) - [20250519_005624.]: Entered 'hyperbolic_regression'-Function - [20250519_005624.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005624.]: Entered 'cubic_regression'-Function - [20250519_005624.]: 'cubic_regression': minmax = FALSE - [20250519_005624.]: # CpG-site: CpG#3 + [20250519_010456.]: Logging df_agg: CpG#2 + [20250519_010456.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010456.]: c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)[20250519_010456.]: c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)[20250519_010456.]: c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) + [20250519_010456.]: Entered 'hyperbolic_regression'-Function + [20250519_010456.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010456.]: Entered 'cubic_regression'-Function + [20250519_010456.]: 'cubic_regression': minmax = FALSE + [20250519_010456.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) - [20250519_005624.]: Logging df_agg: CpG#3 - [20250519_005624.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005624.]: c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)[20250519_005624.]: c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)[20250519_005624.]: c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) - [20250519_005624.]: Entered 'hyperbolic_regression'-Function - [20250519_005624.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005624.]: Entered 'cubic_regression'-Function - [20250519_005624.]: 'cubic_regression': minmax = FALSE - [20250519_005624.]: # CpG-site: CpG#4 + [20250519_010456.]: Logging df_agg: CpG#3 + [20250519_010456.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010456.]: c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)[20250519_010456.]: c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)[20250519_010456.]: c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) + [20250519_010456.]: Entered 'hyperbolic_regression'-Function + [20250519_010456.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010456.]: Entered 'cubic_regression'-Function + [20250519_010456.]: 'cubic_regression': minmax = FALSE + [20250519_010456.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) - [20250519_005624.]: Logging df_agg: CpG#4 - [20250519_005624.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005624.]: c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)[20250519_005624.]: c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)[20250519_005624.]: c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) - [20250519_005624.]: Entered 'hyperbolic_regression'-Function - [20250519_005624.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005624.]: Entered 'cubic_regression'-Function - [20250519_005624.]: 'cubic_regression': minmax = FALSE - [20250519_005624.]: # CpG-site: CpG#5 + [20250519_010456.]: Logging df_agg: CpG#4 + [20250519_010456.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010456.]: c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)[20250519_010456.]: c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)[20250519_010456.]: c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) + [20250519_010456.]: Entered 'hyperbolic_regression'-Function + [20250519_010456.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010456.]: Entered 'cubic_regression'-Function + [20250519_010456.]: 'cubic_regression': minmax = FALSE + [20250519_010456.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) - [20250519_005624.]: Logging df_agg: CpG#5 - [20250519_005624.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005624.]: c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)[20250519_005624.]: c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)[20250519_005624.]: c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) - [20250519_005624.]: Entered 'hyperbolic_regression'-Function - [20250519_005624.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005624.]: Entered 'cubic_regression'-Function - [20250519_005624.]: 'cubic_regression': minmax = FALSE - [20250519_005624.]: # CpG-site: CpG#6 + [20250519_010457.]: Logging df_agg: CpG#5 + [20250519_010457.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010457.]: c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)[20250519_010457.]: c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)[20250519_010457.]: c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) + [20250519_010457.]: Entered 'hyperbolic_regression'-Function + [20250519_010457.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010457.]: Entered 'cubic_regression'-Function + [20250519_010457.]: 'cubic_regression': minmax = FALSE + [20250519_010457.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) - [20250519_005624.]: Logging df_agg: CpG#6 - [20250519_005624.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005624.]: c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)[20250519_005624.]: c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)[20250519_005624.]: c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) - [20250519_005624.]: Entered 'hyperbolic_regression'-Function - [20250519_005624.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005624.]: Entered 'cubic_regression'-Function - [20250519_005624.]: 'cubic_regression': minmax = FALSE - [20250519_005624.]: # CpG-site: CpG#7 + [20250519_010457.]: Logging df_agg: CpG#6 + [20250519_010457.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010457.]: c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)[20250519_010457.]: c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)[20250519_010457.]: c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) + [20250519_010457.]: Entered 'hyperbolic_regression'-Function + [20250519_010457.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010457.]: Entered 'cubic_regression'-Function + [20250519_010457.]: 'cubic_regression': minmax = FALSE + [20250519_010457.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) - [20250519_005624.]: Logging df_agg: CpG#7 - [20250519_005624.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005624.]: c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)[20250519_005624.]: c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)[20250519_005624.]: c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) - [20250519_005624.]: Entered 'hyperbolic_regression'-Function - [20250519_005624.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005624.]: Entered 'cubic_regression'-Function - [20250519_005624.]: 'cubic_regression': minmax = FALSE - [20250519_005624.]: # CpG-site: CpG#8 + [20250519_010457.]: Logging df_agg: CpG#7 + [20250519_010457.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010457.]: c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)[20250519_010457.]: c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)[20250519_010457.]: c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) + [20250519_010457.]: Entered 'hyperbolic_regression'-Function + [20250519_010457.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010457.]: Entered 'cubic_regression'-Function + [20250519_010457.]: 'cubic_regression': minmax = FALSE + [20250519_010457.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) - [20250519_005624.]: Logging df_agg: CpG#8 - [20250519_005624.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005624.]: c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)[20250519_005624.]: c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)[20250519_005624.]: c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) - [20250519_005624.]: Entered 'hyperbolic_regression'-Function - [20250519_005624.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005624.]: Entered 'cubic_regression'-Function - [20250519_005624.]: 'cubic_regression': minmax = FALSE - [20250519_005624.]: # CpG-site: CpG#9 + [20250519_010457.]: Logging df_agg: CpG#8 + [20250519_010457.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010457.]: c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)[20250519_010457.]: c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)[20250519_010457.]: c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) + [20250519_010457.]: Entered 'hyperbolic_regression'-Function + [20250519_010457.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010457.]: Entered 'cubic_regression'-Function + [20250519_010457.]: 'cubic_regression': minmax = FALSE + [20250519_010457.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) - [20250519_005624.]: Logging df_agg: CpG#9 - [20250519_005624.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005624.]: c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)[20250519_005624.]: c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)[20250519_005624.]: c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) - [20250519_005624.]: Entered 'hyperbolic_regression'-Function - [20250519_005624.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005624.]: Entered 'cubic_regression'-Function - [20250519_005624.]: 'cubic_regression': minmax = FALSE - [20250519_005624.]: # CpG-site: row_means + [20250519_010457.]: Logging df_agg: CpG#9 + [20250519_010457.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010457.]: c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)[20250519_010457.]: c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)[20250519_010457.]: c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) + [20250519_010457.]: Entered 'hyperbolic_regression'-Function + [20250519_010457.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010457.]: Entered 'cubic_regression'-Function + [20250519_010457.]: 'cubic_regression': minmax = FALSE + [20250519_010457.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) - [20250519_005624.]: Logging df_agg: row_means - [20250519_005624.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005624.]: c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)[20250519_005624.]: c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)[20250519_005624.]: c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) - [20250519_005624.]: Entered 'hyperbolic_regression'-Function - [20250519_005624.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005624.]: Entered 'cubic_regression'-Function - [20250519_005624.]: 'cubic_regression': minmax = FALSE - [20250519_005624.]: ### Starting with plotting ### - [20250519_005624.]: Creating BiasCorrected (cubic) plot No. 1 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG1_corrected_c.png - [20250519_005624.]: # CpG-site: CpG#1 + [20250519_010457.]: Logging df_agg: row_means + [20250519_010457.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010457.]: c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)[20250519_010457.]: c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)[20250519_010457.]: c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) + [20250519_010457.]: Entered 'hyperbolic_regression'-Function + [20250519_010457.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010457.]: Entered 'cubic_regression'-Function + [20250519_010457.]: 'cubic_regression': minmax = FALSE + [20250519_010457.]: ### Starting with plotting ### + [20250519_010457.]: Creating BiasCorrected (cubic) plot No. 1 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG1_corrected_c.png + [20250519_010457.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -1164.7742156272 , a = 2039.5018241568 , b = -1164.7742156272 , d = 1955.83895682798 - [20250519_005624.]: # CpG-site: CpG#1 + [20250519_010457.]: # CpG-site: CpG#1 Cubic: Using a = -3.42821196240366e-05 , b = 0.00462173155013798 , c = 0.850326258865826 , d = 0.500073874211125 - [20250519_005626.]: Creating BiasCorrected (cubic) plot No. 2 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG2_corrected_c.png - [20250519_005626.]: # CpG-site: CpG#2 + [20250519_010459.]: Creating BiasCorrected (cubic) plot No. 2 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG2_corrected_c.png + [20250519_010459.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = -464.155645361511 , a = 4988.81163391504 , b = -464.155645361511 , d = 4925.28408403839 - [20250519_005626.]: # CpG-site: CpG#2 + [20250519_010459.]: # CpG-site: CpG#2 Cubic: Using a = -1.40811796906314e-05 , b = 0.00190849431754485 , c = 0.933455870941473 , d = 0.363096210099674 - [20250519_005628.]: Creating BiasCorrected (cubic) plot No. 3 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG3_corrected_c.png - [20250519_005628.]: # CpG-site: CpG#3 + [20250519_010501.]: Creating BiasCorrected (cubic) plot No. 3 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG3_corrected_c.png + [20250519_010501.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = -745.602352161431 , a = 2026.54103071808 , b = -745.602352161431 , d = 1953.2909386646 - [20250519_005628.]: # CpG-site: CpG#3 + [20250519_010501.]: # CpG-site: CpG#3 Cubic: Using a = -2.78835051857186e-05 , b = 0.00366863707889089 , c = 0.880665171220893 , d = 0.510779308833653 - [20250519_005630.]: Creating BiasCorrected (cubic) plot No. 4 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG4_corrected_c.png - [20250519_005630.]: # CpG-site: CpG#4 + [20250519_010503.]: Creating BiasCorrected (cubic) plot No. 4 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG4_corrected_c.png + [20250519_010503.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = -833.06710731958 , a = 2871.12609903594 , b = -833.06710731958 , d = 2796.80085807845 - [20250519_005630.]: # CpG-site: CpG#4 + [20250519_010503.]: # CpG-site: CpG#4 Cubic: Using a = -2.30028865643e-05 , b = 0.00309017851940692 , c = 0.897073107777645 , d = 0.443455761841866 - [20250519_005632.]: Creating BiasCorrected (cubic) plot No. 5 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG5_corrected_c.png - [20250519_005632.]: # CpG-site: CpG#5 + [20250519_010505.]: Creating BiasCorrected (cubic) plot No. 5 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG5_corrected_c.png + [20250519_010505.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = 221.879400146024 , a = 12476.5710927987 , b = 221.879400146024 , d = 12431.2329555778 - [20250519_005632.]: # CpG-site: CpG#5 + [20250519_010505.]: # CpG-site: CpG#5 Cubic: Using a = -1.28632696989134e-05 , b = 0.00184822285299014 , c = 0.931001046631176 , d = 0.437963095185599 - [20250519_005634.]: Creating BiasCorrected (cubic) plot No. 6 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG6_corrected_c.png - [20250519_005634.]: # CpG-site: CpG#6 + [20250519_010506.]: Creating BiasCorrected (cubic) plot No. 6 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG6_corrected_c.png + [20250519_010506.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = -700.537394936109 , a = 21298.052707184 , b = -700.537394936109 , d = 21233.4563350159 - [20250519_005634.]: # CpG-site: CpG#6 + [20250519_010506.]: # CpG-site: CpG#6 Cubic: Using a = -5.838335151375e-06 , b = 0.000828447163218526 , c = 0.970047764475166 , d = 0.158074701412251 - [20250519_005636.]: Creating BiasCorrected (cubic) plot No. 7 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG7_corrected_c.png - [20250519_005636.]: # CpG-site: CpG#7 + [20250519_010508.]: Creating BiasCorrected (cubic) plot No. 7 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG7_corrected_c.png + [20250519_010508.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -1144.91075475684 , a = 1477.92088633595 , b = -1144.91075475684 , d = 1394.70940380214 - [20250519_005636.]: # CpG-site: CpG#7 + [20250519_010508.]: # CpG-site: CpG#7 Cubic: Using a = -4.54220535614047e-05 , b = 0.00607993130565131 , c = 0.805411493452389 , d = 0.615125382790254 - [20250519_005637.]: Creating BiasCorrected (cubic) plot No. 8 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG8_corrected_c.png - [20250519_005637.]: # CpG-site: CpG#8 + [20250519_010510.]: Creating BiasCorrected (cubic) plot No. 8 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG8_corrected_c.png + [20250519_010510.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = -349.322022819744 , a = 2377.42555416743 , b = -349.322022819744 , d = 2315.25402084122 - [20250519_005637.]: # CpG-site: CpG#8 + [20250519_010510.]: # CpG-site: CpG#8 Cubic: Using a = -2.64622306669639e-05 , b = 0.00353621593528015 , c = 0.877916844709074 , d = 0.698684606493051 - [20250519_005639.]: Creating BiasCorrected (cubic) plot No. 9 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_CpG9_corrected_c.png - [20250519_005639.]: # CpG-site: CpG#9 + [20250519_010512.]: Creating BiasCorrected (cubic) plot No. 9 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_CpG9_corrected_c.png + [20250519_010512.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -1536.91807795025 , a = 3745.12601816225 , b = -1536.91807795025 , d = 3656.21766932252 - [20250519_005639.]: # CpG-site: CpG#9 + [20250519_010512.]: # CpG-site: CpG#9 Cubic: Using a = -2.70976130611725e-05 , b = 0.00378478247182342 , c = 0.871825263051793 , d = 0.455475740974113 - [20250519_005641.]: Creating BiasCorrected (cubic) plot No. 10 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_rowmeans_corrected_c.png - [20250519_005641.]: # CpG-site: row_means + [20250519_010514.]: Creating BiasCorrected (cubic) plot No. 10 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_rowmeans_corrected_c.png + [20250519_010514.]: # CpG-site: row_means Hyperbolic: Using bias_weight = -820.655299479511 , a = 3198.47118291101 , b = -820.655299479511 , d = 3124.21569118411 - [20250519_005641.]: # CpG-site: row_means + [20250519_010514.]: # CpG-site: row_means Cubic: Using a = -2.34328362100009e-05 , b = 0.00319139073176266 , c = 0.891842168032129 , d = 0.493517689787135 - [20250519_005643.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG1_corrected_c.png - [20250519_005645.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG2_corrected_c.png - [20250519_005646.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG3_corrected_c.png - [20250519_005648.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG4_corrected_c.png - [20250519_005650.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG5_corrected_c.png - [20250519_005652.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG6_corrected_c.png - [20250519_005653.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG7_corrected_c.png - [20250519_005655.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG8_corrected_c.png - [20250519_005657.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_CpG9_corrected_c.png - [20250519_005659.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Testlocus_error_rowmeans_corrected_c.png - [20250519_005700.]: Entered 'solving_equations'-Function - [20250519_005700.]: Solving hyperbolic regression for CpG#1 + [20250519_010515.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG1_corrected_c.png + [20250519_010517.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG2_corrected_c.png + [20250519_010519.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG3_corrected_c.png + [20250519_010521.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG4_corrected_c.png + [20250519_010522.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG5_corrected_c.png + [20250519_010524.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG6_corrected_c.png + [20250519_010526.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG7_corrected_c.png + [20250519_010528.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG8_corrected_c.png + [20250519_010529.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_CpG9_corrected_c.png + [20250519_010531.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Testlocus_error_rowmeans_corrected_c.png + [20250519_010533.]: Entered 'solving_equations'-Function + [20250519_010533.]: Solving hyperbolic regression for CpG#1 Hyperbolic solved: 77.9602582684049 - [20250519_005700.]: Samplename: Sample#1 + [20250519_010533.]: Samplename: Sample#1 Root: 77.96 --> Root in between the borders! Added to results. Hyperbolic solved: 27.1420961794087 - [20250519_005700.]: Samplename: Sample#10 + [20250519_010533.]: Samplename: Sample#10 Root: 27.142 --> Root in between the borders! Added to results. Hyperbolic solved: 36.7640366116659 - [20250519_005700.]: Samplename: Sample#2 + [20250519_010533.]: Samplename: Sample#2 Root: 36.764 --> Root in between the borders! Added to results. Hyperbolic solved: 51.6231208412737 - [20250519_005700.]: Samplename: Sample#3 + [20250519_010533.]: Samplename: Sample#3 Root: 51.623 --> Root in between the borders! Added to results. Hyperbolic solved: 12.873561614208 - [20250519_005700.]: Samplename: Sample#4 + [20250519_010533.]: Samplename: Sample#4 Root: 12.874 --> Root in between the borders! Added to results. Hyperbolic solved: 21.2773642339947 - [20250519_005700.]: Samplename: Sample#5 + [20250519_010533.]: Samplename: Sample#5 Root: 21.277 --> Root in between the borders! Added to results. Hyperbolic solved: 22.1858835788516 - [20250519_005700.]: Samplename: Sample#6 + [20250519_010533.]: Samplename: Sample#6 Root: 22.186 --> Root in between the borders! Added to results. Hyperbolic solved: 40.1506639226714 - [20250519_005700.]: Samplename: Sample#7 + [20250519_010533.]: Samplename: Sample#7 Root: 40.151 --> Root in between the borders! Added to results. Hyperbolic solved: 86.1385429083643 - [20250519_005700.]: Samplename: Sample#8 + [20250519_010533.]: Samplename: Sample#8 Root: 86.139 --> Root in between the borders! Added to results. Hyperbolic solved: 5.22287566816154 - [20250519_005700.]: Samplename: Sample#9 + [20250519_010533.]: Samplename: Sample#9 Root: 5.223 --> Root in between the borders! Added to results. - [20250519_005700.]: Solving hyperbolic regression for CpG#2 + [20250519_010533.]: Solving hyperbolic regression for CpG#2 Hyperbolic solved: 74.4935883316308 - [20250519_005700.]: Samplename: Sample#1 + [20250519_010533.]: Samplename: Sample#1 Root: 74.494 --> Root in between the borders! Added to results. Hyperbolic solved: 28.4239304939459 - [20250519_005700.]: Samplename: Sample#10 + [20250519_010533.]: Samplename: Sample#10 Root: 28.424 --> Root in between the borders! Added to results. Hyperbolic solved: 38.422868577211 - [20250519_005700.]: Samplename: Sample#2 + [20250519_010533.]: Samplename: Sample#2 Root: 38.423 --> Root in between the borders! Added to results. Hyperbolic solved: 54.1812729208768 - [20250519_005700.]: Samplename: Sample#3 + [20250519_010533.]: Samplename: Sample#3 Root: 54.181 --> Root in between the borders! Added to results. Hyperbolic solved: 9.35760412501846 - [20250519_005700.]: Samplename: Sample#4 + [20250519_010533.]: Samplename: Sample#4 Root: 9.358 --> Root in between the borders! Added to results. Hyperbolic solved: 20.0638687855045 - [20250519_005700.]: Samplename: Sample#5 + [20250519_010533.]: Samplename: Sample#5 Root: 20.064 --> Root in between the borders! Added to results. Hyperbolic solved: 18.2691112345675 - [20250519_005700.]: Samplename: Sample#6 + [20250519_010533.]: Samplename: Sample#6 Root: 18.269 --> Root in between the borders! Added to results. Hyperbolic solved: 36.8485384997942 - [20250519_005700.]: Samplename: Sample#7 + [20250519_010533.]: Samplename: Sample#7 Root: 36.849 --> Root in between the borders! Added to results. Hyperbolic solved: 86.7213727244426 - [20250519_005700.]: Samplename: Sample#8 + [20250519_010533.]: Samplename: Sample#8 Root: 86.721 --> Root in between the borders! Added to results. Hyperbolic solved: 8.13467837636389 - [20250519_005700.]: Samplename: Sample#9 + [20250519_010533.]: Samplename: Sample#9 Root: 8.135 --> Root in between the borders! Added to results. - [20250519_005701.]: Solving hyperbolic regression for CpG#3 + [20250519_010533.]: Solving hyperbolic regression for CpG#3 Hyperbolic solved: 72.9754823399013 - [20250519_005701.]: Samplename: Sample#1 + [20250519_010533.]: Samplename: Sample#1 Root: 72.975 --> Root in between the borders! Added to results. Hyperbolic solved: 26.09308106721 - [20250519_005701.]: Samplename: Sample#10 + [20250519_010533.]: Samplename: Sample#10 Root: 26.093 --> Root in between the borders! Added to results. Hyperbolic solved: 38.5204516929959 - [20250519_005701.]: Samplename: Sample#2 + [20250519_010533.]: Samplename: Sample#2 Root: 38.52 --> Root in between the borders! Added to results. Hyperbolic solved: 52.0558039343263 - [20250519_005701.]: Samplename: Sample#3 + [20250519_010533.]: Samplename: Sample#3 Root: 52.056 --> Root in between the borders! Added to results. Hyperbolic solved: 10.6836235174275 - [20250519_005701.]: Samplename: Sample#4 + [20250519_010533.]: Samplename: Sample#4 Root: 10.684 --> Root in between the borders! Added to results. Hyperbolic solved: 23.1384875877206 - [20250519_005701.]: Samplename: Sample#5 + [20250519_010533.]: Samplename: Sample#5 Root: 23.138 --> Root in between the borders! Added to results. Hyperbolic solved: 24.8934322445107 - [20250519_005701.]: Samplename: Sample#6 + [20250519_010533.]: Samplename: Sample#6 Root: 24.893 --> Root in between the borders! Added to results. Hyperbolic solved: 40.6015033285401 - [20250519_005701.]: Samplename: Sample#7 + [20250519_010533.]: Samplename: Sample#7 Root: 40.602 --> Root in between the borders! Added to results. Hyperbolic solved: 86.3873125270529 - [20250519_005701.]: Samplename: Sample#8 + [20250519_010533.]: Samplename: Sample#8 Root: 86.387 --> Root in between the borders! Added to results. Hyperbolic solved: 4.44964604860886 - [20250519_005701.]: Samplename: Sample#9 + [20250519_010533.]: Samplename: Sample#9 Root: 4.45 --> Root in between the borders! Added to results. - [20250519_005701.]: Solving hyperbolic regression for CpG#4 + [20250519_010533.]: Solving hyperbolic regression for CpG#4 Hyperbolic solved: 74.4016735507272 - [20250519_005701.]: Samplename: Sample#1 + [20250519_010533.]: Samplename: Sample#1 Root: 74.402 --> Root in between the borders! Added to results. Hyperbolic solved: 26.4295897789912 - [20250519_005701.]: Samplename: Sample#10 + [20250519_010533.]: Samplename: Sample#10 Root: 26.43 --> Root in between the borders! Added to results. Hyperbolic solved: 39.6008425259518 - [20250519_005701.]: Samplename: Sample#2 + [20250519_010533.]: Samplename: Sample#2 Root: 39.601 --> Root in between the borders! Added to results. Hyperbolic solved: 54.4093205134833 - [20250519_005701.]: Samplename: Sample#3 + [20250519_010533.]: Samplename: Sample#3 Root: 54.409 --> Root in between the borders! Added to results. Hyperbolic solved: 12.6125973016001 - [20250519_005701.]: Samplename: Sample#4 + [20250519_010533.]: Samplename: Sample#4 Root: 12.613 --> Root in between the borders! Added to results. Hyperbolic solved: 21.5537046072918 - [20250519_005701.]: Samplename: Sample#5 + [20250519_010533.]: Samplename: Sample#5 Root: 21.554 --> Root in between the borders! Added to results. Hyperbolic solved: 25.5116357756709 - [20250519_005701.]: Samplename: Sample#6 + [20250519_010533.]: Samplename: Sample#6 Root: 25.512 --> Root in between the borders! Added to results. Hyperbolic solved: 38.1579452647512 - [20250519_005701.]: Samplename: Sample#7 + [20250519_010533.]: Samplename: Sample#7 Root: 38.158 --> Root in between the borders! Added to results. Hyperbolic solved: 86.2182702652395 - [20250519_005701.]: Samplename: Sample#8 + [20250519_010533.]: Samplename: Sample#8 Root: 86.218 --> Root in between the borders! Added to results. Hyperbolic solved: 7.06911234650237 - [20250519_005701.]: Samplename: Sample#9 + [20250519_010533.]: Samplename: Sample#9 Root: 7.069 --> Root in between the borders! Added to results. - [20250519_005701.]: Solving hyperbolic regression for CpG#5 + [20250519_010533.]: Solving hyperbolic regression for CpG#5 Hyperbolic solved: 70.2481376198381 - [20250519_005701.]: Samplename: Sample#1 + [20250519_010533.]: Samplename: Sample#1 Root: 70.248 --> Root in between the borders! Added to results. Hyperbolic solved: 24.9396715205194 - [20250519_005701.]: Samplename: Sample#10 + [20250519_010533.]: Samplename: Sample#10 Root: 24.94 --> Root in between the borders! Added to results. Hyperbolic solved: 40.4614457485807 - [20250519_005701.]: Samplename: Sample#2 + [20250519_010533.]: Samplename: Sample#2 Root: 40.461 --> Root in between the borders! Added to results. Hyperbolic solved: 49.3815751365795 - [20250519_005701.]: Samplename: Sample#3 + [20250519_010533.]: Samplename: Sample#3 Root: 49.382 --> Root in between the borders! Added to results. Hyperbolic solved: 12.353607679335 - [20250519_005701.]: Samplename: Sample#4 + [20250519_010533.]: Samplename: Sample#4 Root: 12.354 --> Root in between the borders! Added to results. Hyperbolic solved: 22.0901459497739 - [20250519_005701.]: Samplename: Sample#5 + [20250519_010533.]: Samplename: Sample#5 Root: 22.09 --> Root in between the borders! Added to results. Hyperbolic solved: 24.5507390437226 - [20250519_005701.]: Samplename: Sample#6 + [20250519_010533.]: Samplename: Sample#6 Root: 24.551 --> Root in between the borders! Added to results. Hyperbolic solved: 39.0947007538418 - [20250519_005701.]: Samplename: Sample#7 + [20250519_010533.]: Samplename: Sample#7 Root: 39.095 --> Root in between the borders! Added to results. Hyperbolic solved: 90.4841321892294 - [20250519_005701.]: Samplename: Sample#8 + [20250519_010533.]: Samplename: Sample#8 Root: 90.484 --> Root in between the borders! Added to results. Hyperbolic solved: 6.91649336851909 - [20250519_005701.]: Samplename: Sample#9 + [20250519_010533.]: Samplename: Sample#9 Root: 6.916 --> Root in between the borders! Added to results. - [20250519_005701.]: Solving hyperbolic regression for CpG#6 + [20250519_010533.]: Solving hyperbolic regression for CpG#6 Hyperbolic solved: 77.4558189028663 - [20250519_005701.]: Samplename: Sample#1 + [20250519_010533.]: Samplename: Sample#1 Root: 77.456 --> Root in between the borders! Added to results. Hyperbolic solved: 27.5880652239037 - [20250519_005701.]: Samplename: Sample#10 + [20250519_010533.]: Samplename: Sample#10 Root: 27.588 --> Root in between the borders! Added to results. Hyperbolic solved: 37.2657605119474 - [20250519_005701.]: Samplename: Sample#2 + [20250519_010533.]: Samplename: Sample#2 Root: 37.266 --> Root in between the borders! Added to results. Hyperbolic solved: 51.469303759821 - [20250519_005701.]: Samplename: Sample#3 + [20250519_010533.]: Samplename: Sample#3 Root: 51.469 --> Root in between the borders! Added to results. Hyperbolic solved: 12.3197919642261 - [20250519_005701.]: Samplename: Sample#4 + [20250519_010533.]: Samplename: Sample#4 Root: 12.32 --> Root in between the borders! Added to results. Hyperbolic solved: 18.9916700132306 - [20250519_005701.]: Samplename: Sample#5 + [20250519_010533.]: Samplename: Sample#5 Root: 18.992 --> Root in between the borders! Added to results. Hyperbolic solved: 27.3757521451495 - [20250519_005701.]: Samplename: Sample#6 + [20250519_010533.]: Samplename: Sample#6 Root: 27.376 --> Root in between the borders! Added to results. Hyperbolic solved: 38.1192529388954 - [20250519_005701.]: Samplename: Sample#7 + [20250519_010533.]: Samplename: Sample#7 Root: 38.119 --> Root in between the borders! Added to results. Hyperbolic solved: 87.7229687796537 - [20250519_005701.]: Samplename: Sample#8 + [20250519_010533.]: Samplename: Sample#8 Root: 87.723 --> Root in between the borders! Added to results. Hyperbolic solved: 7.66260533640959 - [20250519_005701.]: Samplename: Sample#9 + [20250519_010533.]: Samplename: Sample#9 Root: 7.663 --> Root in between the borders! Added to results. - [20250519_005701.]: Solving hyperbolic regression for CpG#7 + [20250519_010533.]: Solving hyperbolic regression for CpG#7 Hyperbolic solved: 75.6858966925732 - [20250519_005701.]: Samplename: Sample#1 + [20250519_010533.]: Samplename: Sample#1 Root: 75.686 --> Root in between the borders! Added to results. Hyperbolic solved: 23.9111476853231 - [20250519_005701.]: Samplename: Sample#10 + [20250519_010533.]: Samplename: Sample#10 Root: 23.911 --> Root in between the borders! Added to results. Hyperbolic solved: 41.6834100561109 - [20250519_005701.]: Samplename: Sample#2 + [20250519_010533.]: Samplename: Sample#2 Root: 41.683 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3038531152796 - [20250519_005701.]: Samplename: Sample#3 + [20250519_010533.]: Samplename: Sample#3 Root: 52.304 --> Root in between the borders! Added to results. Hyperbolic solved: 14.584082616843 - [20250519_005701.]: Samplename: Sample#4 + [20250519_010533.]: Samplename: Sample#4 Root: 14.584 --> Root in between the borders! Added to results. Hyperbolic solved: 23.8322996387272 - [20250519_005701.]: Samplename: Sample#5 + [20250519_010533.]: Samplename: Sample#5 Root: 23.832 --> Root in between the borders! Added to results. Hyperbolic solved: 26.8055282397054 - [20250519_005701.]: Samplename: Sample#6 + [20250519_010533.]: Samplename: Sample#6 Root: 26.806 --> Root in between the borders! Added to results. Hyperbolic solved: 39.1748201958722 - [20250519_005701.]: Samplename: Sample#7 + [20250519_010533.]: Samplename: Sample#7 Root: 39.175 --> Root in between the borders! Added to results. Hyperbolic solved: 86.4968654540458 - [20250519_005701.]: Samplename: Sample#8 + [20250519_010533.]: Samplename: Sample#8 Root: 86.497 --> Root in between the borders! Added to results. Hyperbolic solved: 5.20445580813888 - [20250519_005701.]: Samplename: Sample#9 + [20250519_010533.]: Samplename: Sample#9 Root: 5.204 --> Root in between the borders! Added to results. - [20250519_005701.]: Solving hyperbolic regression for CpG#8 + [20250519_010533.]: Solving hyperbolic regression for CpG#8 Hyperbolic solved: 71.4358559327679 - [20250519_005701.]: Samplename: Sample#1 + [20250519_010533.]: Samplename: Sample#1 Root: 71.436 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5978231834828 - [20250519_005701.]: Samplename: Sample#10 + [20250519_010533.]: Samplename: Sample#10 Root: 26.598 --> Root in between the borders! Added to results. Hyperbolic solved: 32.9687797883659 - [20250519_005701.]: Samplename: Sample#2 + [20250519_010533.]: Samplename: Sample#2 Root: 32.969 --> Root in between the borders! Added to results. Hyperbolic solved: 56.5920244960613 - [20250519_005701.]: Samplename: Sample#3 + [20250519_010533.]: Samplename: Sample#3 Root: 56.592 --> Root in between the borders! Added to results. Hyperbolic solved: 11.2061554830111 - [20250519_005701.]: Samplename: Sample#4 + [20250519_010533.]: Samplename: Sample#4 Root: 11.206 --> Root in between the borders! Added to results. Hyperbolic solved: 19.2580019302788 - [20250519_005701.]: Samplename: Sample#5 + [20250519_010533.]: Samplename: Sample#5 Root: 19.258 --> Root in between the borders! Added to results. Hyperbolic solved: 36.3407423784248 - [20250519_005701.]: Samplename: Sample#6 + [20250519_010533.]: Samplename: Sample#6 Root: 36.341 --> Root in between the borders! Added to results. Hyperbolic solved: 42.2292176199449 - [20250519_005701.]: Samplename: Sample#7 + [20250519_010533.]: Samplename: Sample#7 Root: 42.229 --> Root in between the borders! Added to results. Hyperbolic solved: 85.1479651236815 - [20250519_005701.]: Samplename: Sample#8 + [20250519_010533.]: Samplename: Sample#8 Root: 85.148 --> Root in between the borders! Added to results. Hyperbolic solved: 1.97353330914679 - [20250519_005701.]: Samplename: Sample#9 + [20250519_010533.]: Samplename: Sample#9 Root: 1.974 --> Root in between the borders! Added to results. - [20250519_005701.]: Solving hyperbolic regression for CpG#9 + [20250519_010533.]: Solving hyperbolic regression for CpG#9 Hyperbolic solved: 78.362574368733 - [20250519_005701.]: Samplename: Sample#1 + [20250519_010533.]: Samplename: Sample#1 Root: 78.363 --> Root in between the borders! Added to results. Hyperbolic solved: 24.4725957151698 - [20250519_005701.]: Samplename: Sample#10 + [20250519_010533.]: Samplename: Sample#10 Root: 24.473 --> Root in between the borders! Added to results. Hyperbolic solved: 38.5471090507298 - [20250519_005701.]: Samplename: Sample#2 + [20250519_010533.]: Samplename: Sample#2 Root: 38.547 --> Root in between the borders! Added to results. Hyperbolic solved: 48.850764118569 - [20250519_005701.]: Samplename: Sample#3 + [20250519_010533.]: Samplename: Sample#3 Root: 48.851 --> Root in between the borders! Added to results. Hyperbolic solved: 13.0341042345297 - [20250519_005701.]: Samplename: Sample#4 + [20250519_010533.]: Samplename: Sample#4 Root: 13.034 --> Root in between the borders! Added to results. Hyperbolic solved: 22.1156929084775 - [20250519_005701.]: Samplename: Sample#5 + [20250519_010533.]: Samplename: Sample#5 Root: 22.116 --> Root in between the borders! Added to results. Hyperbolic solved: 33.0730206710577 - [20250519_005701.]: Samplename: Sample#6 + [20250519_010533.]: Samplename: Sample#6 Root: 33.073 --> Root in between the borders! Added to results. Hyperbolic solved: 36.8757560868803 - [20250519_005701.]: Samplename: Sample#7 + [20250519_010533.]: Samplename: Sample#7 Root: 36.876 --> Root in between the borders! Added to results. Hyperbolic solved: 89.6667208986789 - [20250519_005701.]: Samplename: Sample#8 + [20250519_010533.]: Samplename: Sample#8 Root: 89.667 --> Root in between the borders! Added to results. Hyperbolic solved: 8.10593089892949 - [20250519_005701.]: Samplename: Sample#9 + [20250519_010533.]: Samplename: Sample#9 Root: 8.106 --> Root in between the borders! Added to results. - [20250519_005701.]: Solving hyperbolic regression for row_means + [20250519_010533.]: Solving hyperbolic regression for row_means Hyperbolic solved: 74.8404708557895 - [20250519_005701.]: Samplename: Sample#1 + [20250519_010533.]: Samplename: Sample#1 Root: 74.84 --> Root in between the borders! Added to results. Hyperbolic solved: 26.2806125509682 - [20250519_005701.]: Samplename: Sample#10 + [20250519_010533.]: Samplename: Sample#10 Root: 26.281 --> Root in between the borders! Added to results. Hyperbolic solved: 38.1683813928189 - [20250519_005701.]: Samplename: Sample#2 + [20250519_010533.]: Samplename: Sample#2 Root: 38.168 --> Root in between the borders! Added to results. Hyperbolic solved: 52.3554454867083 - [20250519_005701.]: Samplename: Sample#3 + [20250519_010533.]: Samplename: Sample#3 Root: 52.355 --> Root in between the borders! Added to results. Hyperbolic solved: 12.0337443526331 - [20250519_005701.]: Samplename: Sample#4 + [20250519_010533.]: Samplename: Sample#4 Root: 12.034 --> Root in between the borders! Added to results. Hyperbolic solved: 21.2452112654646 - [20250519_005701.]: Samplename: Sample#5 + [20250519_010533.]: Samplename: Sample#5 Root: 21.245 --> Root in between the borders! Added to results. Hyperbolic solved: 26.5661392150144 - [20250519_005701.]: Samplename: Sample#6 + [20250519_010533.]: Samplename: Sample#6 Root: 26.566 --> Root in between the borders! Added to results. Hyperbolic solved: 38.9839362125804 - [20250519_005701.]: Samplename: Sample#7 + [20250519_010533.]: Samplename: Sample#7 Root: 38.984 --> Root in between the borders! Added to results. Hyperbolic solved: 87.2201089452416 - [20250519_005701.]: Samplename: Sample#8 + [20250519_010533.]: Samplename: Sample#8 Root: 87.22 --> Root in between the borders! Added to results. Hyperbolic solved: 6.14149092380673 - [20250519_005701.]: Samplename: Sample#9 + [20250519_010533.]: Samplename: Sample#9 Root: 6.141 --> Root in between the borders! Added to results. - [20250519_005701.]: Entered 'clean_dt'-Function - [20250519_005701.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_005701.]: got experimental data - [20250519_005701.]: Entered 'clean_dt'-Function - [20250519_005701.]: Importing data of type 2: Many loci in one sample (e.g., next-gen seq or microarray data) - [20250519_005701.]: got experimental data - [20250519_005701.]: Entered 'clean_dt'-Function - [20250519_005701.]: Entered 'clean_dt'-Function - [20250519_005701.]: Entered 'clean_dt'-Function - [20250519_005701.]: Entered 'clean_dt'-Function - [20250519_005701.]: Entered 'clean_dt'-Function - [20250519_005701.]: Entered 'clean_dt'-Function - [20250519_005701.]: Entered 'clean_dt'-Function - [20250519_005701.]: Entered 'clean_dt'-Function - [20250519_005701.]: Entered 'clean_dt'-Function - [20250519_005701.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_005701.]: got calibration data + [20250519_010533.]: Entered 'clean_dt'-Function + [20250519_010533.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_010533.]: got experimental data + [20250519_010533.]: Entered 'clean_dt'-Function + [20250519_010533.]: Importing data of type 2: Many loci in one sample (e.g., next-gen seq or microarray data) + [20250519_010533.]: got experimental data + [20250519_010533.]: Entered 'clean_dt'-Function + [20250519_010533.]: Entered 'clean_dt'-Function + [20250519_010533.]: Entered 'clean_dt'-Function + [20250519_010533.]: Entered 'clean_dt'-Function + [20250519_010533.]: Entered 'clean_dt'-Function + [20250519_010533.]: Entered 'clean_dt'-Function + [20250519_010533.]: Entered 'clean_dt'-Function + [20250519_010533.]: Entered 'clean_dt'-Function + [20250519_010534.]: Entered 'clean_dt'-Function + [20250519_010534.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_010534.]: got calibration data ### ERROR 54: first column cannot be parsed to numeric ### - [20250519_005701.]: Entered 'clean_dt'-Function - [20250519_005701.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_005701.]: got calibration data + [20250519_010534.]: Entered 'clean_dt'-Function + [20250519_010534.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_010534.]: got calibration data ### ERROR 54: first column cannot be parsed to numeric ### - [20250519_005701.]: Entered 'clean_dt'-Function - [20250519_005701.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_005701.]: got calibration data - [20250519_005701.]: ### ERROR ### + [20250519_010534.]: Entered 'clean_dt'-Function + [20250519_010534.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_010534.]: got calibration data + [20250519_010534.]: ### ERROR ### The data provided contains less than four calibration steps. At least four distinct calibration steps are required to perform bias correction. - [20250519_005701.]: Entered 'clean_dt'-Function - [20250519_005701.]: Importing data of type 2: Many loci in one sample (e.g., next-gen seq or microarray data) - [20250519_005701.]: got calibration data - [20250519_005701.]: ### ERROR ### + [20250519_010534.]: Entered 'clean_dt'-Function + [20250519_010534.]: Importing data of type 2: Many loci in one sample (e.g., next-gen seq or microarray data) + [20250519_010534.]: got calibration data + [20250519_010534.]: ### ERROR ### The data provided contains locus ids with heterogeneous counts of CpG-sites. - [20250519_005701.]: Entered 'clean_dt'-Function - [20250519_005701.]: Importing data of type 2: Many loci in one sample (e.g., next-gen seq or microarray data) - [20250519_005701.]: got experimental data - [20250519_005701.]: ### ERROR ### + [20250519_010534.]: Entered 'clean_dt'-Function + [20250519_010534.]: Importing data of type 2: Many loci in one sample (e.g., next-gen seq or microarray data) + [20250519_010534.]: got experimental data + [20250519_010534.]: ### ERROR ### The data provided contains locus ids with heterogeneous counts of CpG-sites. - [20250519_005701.]: Entered 'clean_dt'-Function - [20250519_005701.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_005701.]: got calibration data - [20250519_005702.]: Entered 'clean_dt'-Function - [20250519_005702.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_005702.]: got calibration data - [20250519_005702.]: Entered 'cubic_regression'-Function - [20250519_005702.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005702.]: Entered 'cubic_regression'-Function - [20250519_005702.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005702.]: Entered 'clean_dt'-Function - [20250519_005702.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_005702.]: got calibration data - [20250519_005702.]: Entered 'hyperbolic_regression'-Function - [20250519_005702.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005702.]: Entered 'hyperbolic_regression'-Function - [20250519_005702.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental - [20250519_005702.]: on_start: using future::plan("sequential") - [20250519_005702.]: Entered 'clean_dt'-Function - [20250519_005702.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_005702.]: got experimental data - [20250519_005702.]: Entered 'clean_dt'-Function - [20250519_005702.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_005702.]: got calibration data - [20250519_005702.]: + [20250519_010534.]: Entered 'clean_dt'-Function + [20250519_010534.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_010534.]: got calibration data + [20250519_010534.]: Entered 'clean_dt'-Function + [20250519_010534.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_010534.]: got calibration data + [20250519_010534.]: Entered 'cubic_regression'-Function + [20250519_010534.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010534.]: Entered 'cubic_regression'-Function + [20250519_010534.]: 'cubic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010534.]: Entered 'clean_dt'-Function + [20250519_010534.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_010534.]: got calibration data + [20250519_010534.]: Entered 'hyperbolic_regression'-Function + [20250519_010534.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010534.]: Entered 'hyperbolic_regression'-Function + [20250519_010534.]: 'hyperbolic_regression': minmax = TRUE --> WARNING: this is experimental + [20250519_010534.]: on_start: using future::plan("sequential") + [20250519_010534.]: Entered 'clean_dt'-Function + [20250519_010534.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_010534.]: got experimental data + [20250519_010534.]: Entered 'clean_dt'-Function + [20250519_010534.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_010534.]: got calibration data + [20250519_010534.]: ### Starting with regression calculations ### - [20250519_005702.]: Entered 'regression_type1'-Function - [20250519_005702.]: # CpG-site: CpG#1 + [20250519_010534.]: Entered 'regression_type1'-Function + [20250519_010534.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_005702.]: Logging df_agg: CpG#1 - [20250519_005702.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005702.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005702.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005702.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005702.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_005702.]: Entered 'hyperbolic_regression'-Function - [20250519_005702.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005702.]: Entered 'cubic_regression'-Function - [20250519_005702.]: 'cubic_regression': minmax = FALSE - [20250519_005702.]: # CpG-site: CpG#2 + [20250519_010534.]: Logging df_agg: CpG#1 + [20250519_010534.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010534.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_010534.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_010534.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_010534.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) + [20250519_010534.]: Entered 'hyperbolic_regression'-Function + [20250519_010534.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010534.]: Entered 'cubic_regression'-Function + [20250519_010534.]: 'cubic_regression': minmax = FALSE + [20250519_010534.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_005702.]: Logging df_agg: CpG#2 - [20250519_005702.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005702.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005702.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005702.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005702.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_005702.]: Entered 'hyperbolic_regression'-Function - [20250519_005702.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005702.]: Entered 'cubic_regression'-Function - [20250519_005702.]: 'cubic_regression': minmax = FALSE - [20250519_005702.]: # CpG-site: CpG#3 + [20250519_010534.]: Logging df_agg: CpG#2 + [20250519_010534.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010534.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_010534.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_010534.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_010534.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) + [20250519_010534.]: Entered 'hyperbolic_regression'-Function + [20250519_010534.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010534.]: Entered 'cubic_regression'-Function + [20250519_010534.]: 'cubic_regression': minmax = FALSE + [20250519_010534.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_005702.]: Logging df_agg: CpG#3 - [20250519_005702.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005702.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005702.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005702.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005702.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_005702.]: Entered 'hyperbolic_regression'-Function - [20250519_005702.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005702.]: Entered 'cubic_regression'-Function - [20250519_005702.]: 'cubic_regression': minmax = FALSE - [20250519_005702.]: # CpG-site: CpG#4 + [20250519_010534.]: Logging df_agg: CpG#3 + [20250519_010534.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010534.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_010534.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_010534.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_010534.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) + [20250519_010534.]: Entered 'hyperbolic_regression'-Function + [20250519_010534.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010534.]: Entered 'cubic_regression'-Function + [20250519_010534.]: 'cubic_regression': minmax = FALSE + [20250519_010534.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_005702.]: Logging df_agg: CpG#4 - [20250519_005702.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005702.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005702.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005702.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005702.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_005702.]: Entered 'hyperbolic_regression'-Function - [20250519_005702.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005702.]: Entered 'cubic_regression'-Function - [20250519_005702.]: 'cubic_regression': minmax = FALSE - [20250519_005702.]: # CpG-site: CpG#5 + [20250519_010534.]: Logging df_agg: CpG#4 + [20250519_010534.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010534.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_010534.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_010534.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_010534.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) + [20250519_010534.]: Entered 'hyperbolic_regression'-Function + [20250519_010534.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010534.]: Entered 'cubic_regression'-Function + [20250519_010534.]: 'cubic_regression': minmax = FALSE + [20250519_010535.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_005702.]: Logging df_agg: CpG#5 - [20250519_005702.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005702.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005702.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005702.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005702.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_005702.]: Entered 'hyperbolic_regression'-Function - [20250519_005702.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005702.]: Entered 'cubic_regression'-Function - [20250519_005702.]: 'cubic_regression': minmax = FALSE - [20250519_005702.]: # CpG-site: CpG#6 + [20250519_010535.]: Logging df_agg: CpG#5 + [20250519_010535.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010535.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_010535.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_010535.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_010535.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) + [20250519_010535.]: Entered 'hyperbolic_regression'-Function + [20250519_010535.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010535.]: Entered 'cubic_regression'-Function + [20250519_010535.]: 'cubic_regression': minmax = FALSE + [20250519_010535.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_005702.]: Logging df_agg: CpG#6 - [20250519_005702.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005702.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005702.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005702.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005702.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_005702.]: Entered 'hyperbolic_regression'-Function - [20250519_005702.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005702.]: Entered 'cubic_regression'-Function - [20250519_005702.]: 'cubic_regression': minmax = FALSE - [20250519_005702.]: # CpG-site: CpG#7 + [20250519_010535.]: Logging df_agg: CpG#6 + [20250519_010535.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010535.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_010535.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_010535.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_010535.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) + [20250519_010535.]: Entered 'hyperbolic_regression'-Function + [20250519_010535.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010535.]: Entered 'cubic_regression'-Function + [20250519_010535.]: 'cubic_regression': minmax = FALSE + [20250519_010535.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_005702.]: Logging df_agg: CpG#7 - [20250519_005702.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005702.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005702.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005702.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005702.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_005702.]: Entered 'hyperbolic_regression'-Function - [20250519_005702.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005702.]: Entered 'cubic_regression'-Function - [20250519_005702.]: 'cubic_regression': minmax = FALSE - [20250519_005702.]: # CpG-site: CpG#8 + [20250519_010535.]: Logging df_agg: CpG#7 + [20250519_010535.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010535.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_010535.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_010535.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_010535.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) + [20250519_010535.]: Entered 'hyperbolic_regression'-Function + [20250519_010535.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010535.]: Entered 'cubic_regression'-Function + [20250519_010535.]: 'cubic_regression': minmax = FALSE + [20250519_010535.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_005702.]: Logging df_agg: CpG#8 - [20250519_005702.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005702.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005702.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005702.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005702.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_005702.]: Entered 'hyperbolic_regression'-Function - [20250519_005702.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005702.]: Entered 'cubic_regression'-Function - [20250519_005702.]: 'cubic_regression': minmax = FALSE - [20250519_005702.]: # CpG-site: CpG#9 + [20250519_010535.]: Logging df_agg: CpG#8 + [20250519_010535.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010535.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_010535.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_010535.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_010535.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) + [20250519_010535.]: Entered 'hyperbolic_regression'-Function + [20250519_010535.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010535.]: Entered 'cubic_regression'-Function + [20250519_010535.]: 'cubic_regression': minmax = FALSE + [20250519_010535.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_005702.]: Logging df_agg: CpG#9 - [20250519_005702.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005702.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005702.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005702.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005702.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_005702.]: Entered 'hyperbolic_regression'-Function - [20250519_005702.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005703.]: Entered 'cubic_regression'-Function - [20250519_005703.]: 'cubic_regression': minmax = FALSE - [20250519_005703.]: # CpG-site: row_means + [20250519_010535.]: Logging df_agg: CpG#9 + [20250519_010535.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010535.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_010535.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_010535.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_010535.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) + [20250519_010535.]: Entered 'hyperbolic_regression'-Function + [20250519_010535.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010535.]: Entered 'cubic_regression'-Function + [20250519_010535.]: 'cubic_regression': minmax = FALSE + [20250519_010535.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_005703.]: Logging df_agg: row_means - [20250519_005703.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005703.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005703.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_005703.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005703.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_005703.]: Entered 'hyperbolic_regression'-Function - [20250519_005703.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005703.]: Entered 'cubic_regression'-Function - [20250519_005703.]: 'cubic_regression': minmax = FALSE - [20250519_005703.]: ### Starting with plotting ### - [20250519_005703.]: Creating plot No. 1 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Test_CpG1.png - [20250519_005703.]: # CpG-site: CpG#1 + [20250519_010535.]: Logging df_agg: row_means + [20250519_010535.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010535.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_010535.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_010535.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_010535.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) + [20250519_010535.]: Entered 'hyperbolic_regression'-Function + [20250519_010535.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010535.]: Entered 'cubic_regression'-Function + [20250519_010535.]: 'cubic_regression': minmax = FALSE + [20250519_010535.]: ### Starting with plotting ### + [20250519_010535.]: Creating plot No. 1 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Test_CpG1.png + [20250519_010535.]: # CpG-site: CpG#1 Hyperbolic: Using bias_weight = -123249135.40483 , a = 49589952.0989949 , b = -123249135.40483 , d = 59280789.0121927 - [20250519_005703.]: # CpG-site: CpG#1 + [20250519_010535.]: # CpG-site: CpG#1 Cubic: Using a = 6.53413423120091e-05 , b = -0.0055806968734969 , c = 0.784061853455188 , d = 1.93182659932656 - [20250519_005705.]: Creating plot No. 2 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Test_CpG2.png - [20250519_005705.]: # CpG-site: CpG#2 + [20250519_010538.]: Creating plot No. 2 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Test_CpG2.png + [20250519_010538.]: # CpG-site: CpG#2 Hyperbolic: Using bias_weight = 167929575.171327 , a = 42299444.0962795 , b = 167929575.171327 , d = 47897274.2220611 - [20250519_005705.]: # CpG-site: CpG#2 + [20250519_010538.]: # CpG-site: CpG#2 Cubic: Using a = 8.04237934904601e-06 , b = 0.00293158941798942 , c = 0.514821742825076 , d = 9.27667003367003 - [20250519_005708.]: Creating plot No. 3 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Test_CpG3.png - [20250519_005708.]: # CpG-site: CpG#3 + [20250519_010540.]: Creating plot No. 3 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Test_CpG3.png + [20250519_010540.]: # CpG-site: CpG#3 Hyperbolic: Using bias_weight = 31004745.3756238 , a = 31050253.3779659 , b = 31004745.3756238 , d = 40665231.758814 - [20250519_005708.]: # CpG-site: CpG#3 + [20250519_010540.]: # CpG-site: CpG#3 Cubic: Using a = 2.30555869809204e-05 , b = -0.000797009331409346 , c = 0.62783129228796 , d = 3.88705218855218 - [20250519_005710.]: Creating plot No. 4 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Test_CpG4.png - [20250519_005710.]: # CpG-site: CpG#4 + [20250519_010542.]: Creating plot No. 4 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Test_CpG4.png + [20250519_010542.]: # CpG-site: CpG#4 Hyperbolic: Using bias_weight = 128022258.276155 , a = 42779497.5538086 , b = 128022258.276155 , d = 50999439.9227065 - [20250519_005710.]: # CpG-site: CpG#4 + [20250519_010542.]: # CpG-site: CpG#4 Cubic: Using a = 3.2166356902357e-05 , b = -0.0015913142857143 , c = 0.697398364598366 , d = 6.17055050505048 - [20250519_005712.]: Creating plot No. 5 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Test_CpG5.png - [20250519_005712.]: # CpG-site: CpG#5 + [20250519_010544.]: Creating plot No. 5 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Test_CpG5.png + [20250519_010544.]: # CpG-site: CpG#5 Hyperbolic: Using bias_weight = 29447513.6184781 , a = 41864399.8673718 , b = 29447513.6184781 , d = 55382346.8470806 - [20250519_005712.]: # CpG-site: CpG#5 + [20250519_010544.]: # CpG-site: CpG#5 Cubic: Using a = -4.48459708193036e-06 , b = 0.00375371236171235 , c = 0.422446384479718 , d = 5.17203872053872 - [20250519_005714.]: Creating plot No. 6 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Test_CpG6.png - [20250519_005714.]: # CpG-site: CpG#6 + [20250519_010546.]: Creating plot No. 6 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Test_CpG6.png + [20250519_010546.]: # CpG-site: CpG#6 Hyperbolic: Using bias_weight = 17150080.5960938 , a = 40932834.1078748 , b = 17150080.5960938 , d = 46723471.6686748 - [20250519_005714.]: # CpG-site: CpG#6 + [20250519_010546.]: # CpG-site: CpG#6 Cubic: Using a = 2.38852884399552e-05 , b = 0.000918637037037021 , c = 0.561022132435467 , d = 6.1479276094276 - [20250519_005716.]: Creating plot No. 7 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Test_CpG7.png - [20250519_005716.]: # CpG-site: CpG#7 + [20250519_010548.]: Creating plot No. 7 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Test_CpG7.png + [20250519_010548.]: # CpG-site: CpG#7 Hyperbolic: Using bias_weight = -57204506.2862076 , a = 29554652.6579632 , b = -57204506.2862076 , d = 48548425.6483363 - [20250519_005716.]: # CpG-site: CpG#7 + [20250519_010548.]: # CpG-site: CpG#7 Cubic: Using a = 4.98010505050505e-05 , b = -0.00436152150072151 , c = 0.579588917748918 , d = 1.72104545454544 - [20250519_005719.]: Creating plot No. 8 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Test_CpG8.png - [20250519_005719.]: # CpG-site: CpG#8 + [20250519_010550.]: Creating plot No. 8 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Test_CpG8.png + [20250519_010550.]: # CpG-site: CpG#8 Hyperbolic: Using bias_weight = 263871870.982016 , a = 38159683.1948542 , b = 263871870.982016 , d = 45284547.6704654 - [20250519_005719.]: # CpG-site: CpG#8 + [20250519_010550.]: # CpG-site: CpG#8 Cubic: Using a = -1.50060965207632e-05 , b = 0.00447978143338143 , c = 0.534897737694404 , d = 9.56981481481482 - [20250519_005721.]: Creating plot No. 9 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Test_CpG9.png - [20250519_005721.]: # CpG-site: CpG#9 + [20250519_010552.]: Creating plot No. 9 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Test_CpG9.png + [20250519_010552.]: # CpG-site: CpG#9 Hyperbolic: Using bias_weight = -224404013.660658 , a = 51672526.9851193 , b = -224404013.660658 , d = 60201205.3472543 - [20250519_005721.]: # CpG-site: CpG#9 + [20250519_010552.]: # CpG-site: CpG#9 Cubic: Using a = 7.72300426487093e-05 , b = -0.00542281173641174 , c = 0.67899229597563 , d = 2.72413468013467 - [20250519_005723.]: Creating plot No. 10 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Test_rowmeans.png - [20250519_005723.]: # CpG-site: row_means + [20250519_010555.]: Creating plot No. 10 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Test_rowmeans.png + [20250519_010555.]: # CpG-site: row_means Hyperbolic: Using bias_weight = 29838275.3876239 , a = 33244770.7608578 , b = 29838275.3876239 , d = 41191067.4974404 - [20250519_005723.]: # CpG-site: row_means + [20250519_010555.]: # CpG-site: row_means Cubic: Using a = 2.88923726150392e-05 , b = -0.000629959275292592 , c = 0.600117857944524 , d = 5.17789562289563 - [20250519_005725.]: on_start: using future::plan("sequential") - [20250519_005725.]: Entered 'clean_dt'-Function - [20250519_005725.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_005725.]: got experimental data - [20250519_005725.]: Entered 'clean_dt'-Function - [20250519_005725.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) - [20250519_005725.]: got calibration data - [20250519_005725.]: + [20250519_010557.]: on_start: using future::plan("sequential") + [20250519_010557.]: Entered 'clean_dt'-Function + [20250519_010557.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_010557.]: got experimental data + [20250519_010557.]: Entered 'clean_dt'-Function + [20250519_010557.]: Importing data of type 1: One locus in many samples (e.g., pyrosequencing data) + [20250519_010557.]: got calibration data + [20250519_010557.]: ### Starting with regression calculations ### - [20250519_005725.]: Entered 'regression_type1'-Function - [20250519_005725.]: # CpG-site: CpG#1 + [20250519_010557.]: Entered 'regression_type1'-Function + [20250519_010557.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_005725.]: Logging df_agg: CpG#1 - [20250519_005725.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005725.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_005725.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_005725.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_005725.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) - [20250519_005725.]: Entered 'hyperbolic_regression'-Function - [20250519_005725.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005725.]: Entered 'cubic_regression'-Function - [20250519_005725.]: 'cubic_regression': minmax = FALSE - [20250519_005725.]: # CpG-site: CpG#2 + [20250519_010557.]: Logging df_agg: CpG#1 + [20250519_010557.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010557.]: c(2.968, 10.2733333333333, 17.32, 26.212, 36.8325, 48.286, 57.825, 65.03, 92.978)[20250519_010557.]: c(0.784136467714645, 3.04161689456995, 1.76398696140306, 3.58204829671516, 2.74881034873877, 2.65410625258297, 4.52087380934261, 2.01106936727702, 0.717892749649975)[20250519_010557.]: c(2.968, 2.22666666666667, 7.68, 11.288, 13.1675, 14.214, 17.175, 22.47, 7.02199999999999)[20250519_010557.]: c(NA, 17.8133333333333, 30.72, 30.1013333333333, 26.335, 22.7424, 22.9, 25.68, 7.02199999999999) + [20250519_010557.]: Entered 'hyperbolic_regression'-Function + [20250519_010557.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010557.]: Entered 'cubic_regression'-Function + [20250519_010557.]: 'cubic_regression': minmax = FALSE + [20250519_010557.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_005725.]: Logging df_agg: CpG#2 - [20250519_005725.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005725.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_005725.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_005725.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_005725.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) - [20250519_005725.]: Entered 'hyperbolic_regression'-Function - [20250519_005725.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005725.]: Entered 'cubic_regression'-Function - [20250519_005725.]: 'cubic_regression': minmax = FALSE - [20250519_005725.]: # CpG-site: CpG#3 + [20250519_010557.]: Logging df_agg: CpG#2 + [20250519_010557.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010557.]: c(9.56, 15.6166666666667, 25.216, 31.614, 41.505, 59.24, 68.2425, 78.1133333333333, 99.854)[20250519_010557.]: c(1.73731114081502, 3.47246790241945, 5.1327702072078, 7.8896913754595, 6.75096783184949, 5.34066943369462, 6.12841673408937, 3.68391548943965, 0.326465924714971)[20250519_010557.]: c(9.56, 3.11666666666667, 0.216000000000001, 5.886, 8.495, 3.26, 6.75749999999999, 9.38666666666667, 0.146000000000001)[20250519_010557.]: c(NA, 24.9333333333333, 0.864000000000004, 15.696, 16.99, 5.216, 9.00999999999999, 10.7276190476191, 0.146000000000001) + [20250519_010557.]: Entered 'hyperbolic_regression'-Function + [20250519_010557.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010557.]: Entered 'cubic_regression'-Function + [20250519_010557.]: 'cubic_regression': minmax = FALSE + [20250519_010557.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_005725.]: Logging df_agg: CpG#3 - [20250519_005725.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005725.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_005725.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_005725.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_005725.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) - [20250519_005725.]: Entered 'hyperbolic_regression'-Function - [20250519_005725.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005725.]: Entered 'cubic_regression'-Function - [20250519_005725.]: 'cubic_regression': minmax = FALSE - [20250519_005725.]: # CpG-site: CpG#4 + [20250519_010557.]: Logging df_agg: CpG#3 + [20250519_010557.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010557.]: c(4.79, 10.46, 19.316, 26.5, 36.6625, 47.776, 58.8625, 61.5433333333333, 84.552)[20250519_010557.]: c(1.09893129903557, 3.07434545879281, 2.06842452122382, 5.15145125183186, 2.05736684461798, 1.72031392484047, 3.61472336424241, 1.32869610270119, 2.92314385550899)[20250519_010557.]: c(4.79, 2.04, 5.684, 11, 13.3375, 14.724, 16.1375, 25.9566666666667, 15.448)[20250519_010557.]: c(NA, 16.32, 22.736, 29.3333333333333, 26.675, 23.5584, 21.5166666666667, 29.6647619047619, 15.448) + [20250519_010557.]: Entered 'hyperbolic_regression'-Function + [20250519_010557.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010557.]: Entered 'cubic_regression'-Function + [20250519_010557.]: 'cubic_regression': minmax = FALSE + [20250519_010557.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_005725.]: Logging df_agg: CpG#4 - [20250519_005725.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005725.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_005725.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_005725.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_005725.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) - [20250519_005725.]: Entered 'hyperbolic_regression'-Function - [20250519_005725.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005726.]: Entered 'cubic_regression'-Function - [20250519_005726.]: 'cubic_regression': minmax = FALSE - [20250519_005726.]: # CpG-site: CpG#5 + [20250519_010557.]: Logging df_agg: CpG#4 + [20250519_010557.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010557.]: c(6.768, 14.4233333333333, 21.974, 31.698, 39.8075, 56.426, 62.71, 71.7633333333333, 94.492)[20250519_010557.]: c(2.01053972853063, 1.4578865982419, 2.5033038169587, 4.72756491229893, 2.63379795479203, 3.99248418907326, 9.01051977783006, 4.24883905712294, 3.62526136988769)[20250519_010557.]: c(6.768, 1.92333333333333, 3.026, 5.802, 10.1925, 6.074, 12.29, 15.7366666666667, 5.508)[20250519_010557.]: c(NA, 15.3866666666667, 12.104, 15.472, 20.385, 9.7184, 16.3866666666667, 17.9847619047619, 5.508) + [20250519_010557.]: Entered 'hyperbolic_regression'-Function + [20250519_010557.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010557.]: Entered 'cubic_regression'-Function + [20250519_010557.]: 'cubic_regression': minmax = FALSE + [20250519_010557.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_005726.]: Logging df_agg: CpG#5 - [20250519_005726.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005726.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_005726.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_005726.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_005726.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) - [20250519_005726.]: Entered 'hyperbolic_regression'-Function - [20250519_005726.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005726.]: Entered 'cubic_regression'-Function - [20250519_005726.]: 'cubic_regression': minmax = FALSE - [20250519_005726.]: # CpG-site: CpG#6 + [20250519_010557.]: Logging df_agg: CpG#5 + [20250519_010557.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010557.]: c(5.796, 9.94, 17.898, 26.332, 35.8675, 44.83, 56.855, 66.1866666666667, 81.242)[20250519_010557.]: c(1.07867047794959, 2.24581833637541, 2.57018870902508, 3.58584996897528, 5.496025078788, 6.08278308013692, 5.04371886607491, 2.40791057419775, 5.77276969919986)[20250519_010557.]: c(5.796, 2.56, 7.102, 11.168, 14.1325, 17.67, 18.145, 21.3133333333333, 18.758)[20250519_010557.]: c(NA, 20.48, 28.408, 29.7813333333333, 28.265, 28.272, 24.1933333333333, 24.3580952380952, 18.758) + [20250519_010557.]: Entered 'hyperbolic_regression'-Function + [20250519_010557.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010557.]: Entered 'cubic_regression'-Function + [20250519_010557.]: 'cubic_regression': minmax = FALSE + [20250519_010557.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_005726.]: Logging df_agg: CpG#6 - [20250519_005726.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005726.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_005726.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_005726.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_005726.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) - [20250519_005726.]: Entered 'hyperbolic_regression'-Function - [20250519_005726.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005726.]: Entered 'cubic_regression'-Function - [20250519_005726.]: 'cubic_regression': minmax = FALSE - [20250519_005726.]: # CpG-site: CpG#7 + [20250519_010557.]: Logging df_agg: CpG#6 + [20250519_010557.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010557.]: c(6.344, 12.8866666666667, 22.036, 28.148, 39.5925, 53.048, 61.98, 77.6933333333333, 95.804)[20250519_010557.]: c(1.10974321354086, 4.54314135079829, 1.96134647627593, 5.4722545627922, 4.77866351608899, 7.74987225701172, 5.11307474356999, 3.32800741185072, 5.10348214457541)[20250519_010557.]: c(6.344, 0.386666666666667, 2.964, 9.352, 10.4075, 9.452, 13.02, 9.80666666666667, 4.196)[20250519_010557.]: c(NA, 3.09333333333333, 11.856, 24.9386666666667, 20.815, 15.1232, 17.36, 11.2076190476191, 4.196) + [20250519_010557.]: Entered 'hyperbolic_regression'-Function + [20250519_010557.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010557.]: Entered 'cubic_regression'-Function + [20250519_010557.]: 'cubic_regression': minmax = FALSE + [20250519_010557.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_005726.]: Logging df_agg: CpG#7 - [20250519_005726.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005726.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_005726.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_005726.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_005726.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) - [20250519_005726.]: Entered 'hyperbolic_regression'-Function - [20250519_005726.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005726.]: Entered 'cubic_regression'-Function - [20250519_005726.]: 'cubic_regression': minmax = FALSE - [20250519_005726.]: # CpG-site: CpG#8 + [20250519_010557.]: Logging df_agg: CpG#7 + [20250519_010557.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010557.]: c(2.936, 7.11666666666667, 12.986, 19.172, 27.7525, 35.686, 42.89, 45.8566666666667, 68.944)[20250519_010557.]: c(1.12025889864799, 1.95152077450724, 1.75196461151474, 2.51024301612414, 3.79406883613534, 5.03340143441788, 9.26798791539998, 2.22086319554657, 4.43931638881484)[20250519_010557.]: c(2.936, 5.38333333333333, 12.014, 18.328, 22.2475, 26.814, 32.11, 41.6433333333333, 31.056)[20250519_010557.]: c(NA, 43.0666666666667, 48.056, 48.8746666666667, 44.495, 42.9024, 42.8133333333333, 47.5923809523809, 31.056) + [20250519_010557.]: Entered 'hyperbolic_regression'-Function + [20250519_010557.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010557.]: Entered 'cubic_regression'-Function + [20250519_010557.]: 'cubic_regression': minmax = FALSE + [20250519_010557.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_005726.]: Logging df_agg: CpG#8 - [20250519_005726.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005726.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_005726.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_005726.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_005726.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) - [20250519_005726.]: Entered 'hyperbolic_regression'-Function - [20250519_005726.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005726.]: Entered 'cubic_regression'-Function - [20250519_005726.]: 'cubic_regression': minmax = FALSE - [20250519_005726.]: # CpG-site: CpG#9 + [20250519_010557.]: Logging df_agg: CpG#8 + [20250519_010557.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010557.]: c(10.666, 15.0166666666667, 26.5, 32.92, 47.195, 58.396, 71.2375, 74.08, 95.63)[20250519_010557.]: c(2.09203967457599, 1.0042576030747, 6.57304723853404, 9.3202655541567, 13.0680615752044, 12.1853736093728, 8.65580104130557, 4.16176645188074, 6.5153434291678)[20250519_010557.]: c(10.666, 2.51666666666667, 1.5, 4.58, 2.805, 4.104, 3.7625, 13.42, 4.37)[20250519_010557.]: c(NA, 20.1333333333333, 6, 12.2133333333333, 5.61, 6.5664, 5.01666666666667, 15.3371428571429, 4.37) + [20250519_010557.]: Entered 'hyperbolic_regression'-Function + [20250519_010557.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010557.]: Entered 'cubic_regression'-Function + [20250519_010557.]: 'cubic_regression': minmax = FALSE + [20250519_010557.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_005726.]: Logging df_agg: CpG#9 - [20250519_005726.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005726.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_005726.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_005726.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_005726.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) - [20250519_005726.]: Entered 'hyperbolic_regression'-Function - [20250519_005726.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005726.]: Entered 'cubic_regression'-Function - [20250519_005726.]: 'cubic_regression': minmax = FALSE - [20250519_005726.]: # CpG-site: row_means + [20250519_010557.]: Logging df_agg: CpG#9 + [20250519_010557.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010557.]: c(3.714, 9.12, 17.43, 23.348, 34.12, 45.41, 55.6275, 68.2166666666667, 95.714)[20250519_010557.]: c(1.02119048174178, 2.16589473428419, 5.20318171891007, 6.16761055839293, 7.42462120245875, 9.14966392825442, 15.0005163800006, 4.36616918285736, 4.89711956153819)[20250519_010557.]: c(3.714, 3.38, 7.57, 14.152, 15.88, 17.09, 19.3725, 19.2833333333333, 4.286)[20250519_010557.]: c(NA, 27.04, 30.28, 37.7386666666667, 31.76, 27.344, 25.83, 22.0380952380952, 4.286) + [20250519_010557.]: Entered 'hyperbolic_regression'-Function + [20250519_010557.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010557.]: Entered 'cubic_regression'-Function + [20250519_010557.]: 'cubic_regression': minmax = FALSE + [20250519_010557.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_005726.]: Logging df_agg: row_means - [20250519_005726.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005726.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_005726.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_005726.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_005726.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) - [20250519_005726.]: Entered 'hyperbolic_regression'-Function - [20250519_005726.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005726.]: Entered 'cubic_regression'-Function - [20250519_005726.]: 'cubic_regression': minmax = FALSE - [20250519_005726.]: Entered 'solving_equations'-Function - [20250519_005726.]: Solving cubic regression for CpG#1 + [20250519_010557.]: Logging df_agg: row_means + [20250519_010557.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010557.]: c(5.94911111111111, 11.6503703703704, 20.0751111111111, 27.3271111111111, 37.7038888888889, 49.8997777777778, 59.5811111111111, 67.6092592592593, 89.9122222222222)[20250519_010557.]: c(0.633290154473514, 1.0960165433854, 1.72385972331829, 4.20633088405713, 3.39104782475775, 2.96189059992531, 5.65352273733499, 0.926454367723125, 2.47760998435345)[20250519_010557.]: c(5.94911111111111, 0.849629629629627, 4.92488888888889, 10.1728888888889, 12.2961111111111, 12.6002222222222, 15.4188888888889, 19.8907407407407, 10.0877777777778)[20250519_010557.]: c(NA, 6.79703703703702, 19.6995555555556, 27.1277037037037, 24.5922222222222, 20.1603555555556, 20.5585185185185, 22.7322751322751, 10.0877777777778) + [20250519_010557.]: Entered 'hyperbolic_regression'-Function + [20250519_010557.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010557.]: Entered 'cubic_regression'-Function + [20250519_010557.]: 'cubic_regression': minmax = FALSE + [20250519_010558.]: Entered 'solving_equations'-Function + [20250519_010558.]: Solving cubic regression for CpG#1 Coefficients: -1.03617340067344Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_005726.]: Samplename: 0 + [20250519_010558.]: Samplename: 0 Root: 1.334 --> Root in between the borders! Added to results. Coefficients: -8.34150673400678Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_005726.]: Samplename: 12.5 + [20250519_010558.]: Samplename: 12.5 Root: 11.446 --> Root in between the borders! Added to results. Coefficients: -15.3881734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_005726.]: Samplename: 25 + [20250519_010558.]: Samplename: 25 Root: 22.228 --> Root in between the borders! Added to results. Coefficients: -24.2801734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_005726.]: Samplename: 37.5 + [20250519_010558.]: Samplename: 37.5 Root: 36.374 --> Root in between the borders! Added to results. Coefficients: -34.9006734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_005726.]: Samplename: 50 + [20250519_010558.]: Samplename: 50 Root: 52.044 --> Root in between the borders! Added to results. Coefficients: -46.3541734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_005726.]: Samplename: 62.5 + [20250519_010558.]: Samplename: 62.5 Root: 66.144 --> Root in between the borders! Added to results. Coefficients: -55.8931734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_005726.]: Samplename: 75 + [20250519_010558.]: Samplename: 75 Root: 75.864 --> Root in between the borders! Added to results. Coefficients: -63.0981734006734Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_005726.]: Samplename: 87.5 + [20250519_010558.]: Samplename: 87.5 Root: 82.254 --> Root in between the borders! Added to results. Coefficients: -91.0461734006735Coefficients: 0.784061853455188Coefficients: -0.0055806968734969Coefficients: 6.53413423120091e-05 - [20250519_005726.]: Samplename: 100 + [20250519_010558.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.877 --> '100 < root < 110' --> substitute 100 - [20250519_005726.]: Solving cubic regression for CpG#2 + [20250519_010558.]: Solving cubic regression for CpG#2 Coefficients: -0.283329966329966Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_005726.]: Samplename: 0 + [20250519_010558.]: Samplename: 0 Root: 0.549 --> Root in between the borders! Added to results. Coefficients: -6.33999663299663Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_005726.]: Samplename: 12.5 + [20250519_010558.]: Samplename: 12.5 Root: 11.533 --> Root in between the borders! Added to results. Coefficients: -15.93932996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_005726.]: Samplename: 25 + [20250519_010558.]: Samplename: 25 Root: 26.628 --> Root in between the borders! Added to results. Coefficients: -22.33732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_005726.]: Samplename: 37.5 + [20250519_010558.]: Samplename: 37.5 Root: 35.509 --> Root in between the borders! Added to results. Coefficients: -32.22832996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_005726.]: Samplename: 50 + [20250519_010558.]: Samplename: 50 Root: 47.851 --> Root in between the borders! Added to results. Coefficients: -49.96332996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_005726.]: Samplename: 62.5 + [20250519_010558.]: Samplename: 62.5 Root: 66.893 --> Root in between the borders! Added to results. Coefficients: -58.96582996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_005726.]: Samplename: 75 + [20250519_010558.]: Samplename: 75 Root: 75.431 --> Root in between the borders! Added to results. Coefficients: -68.8366632996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_005726.]: Samplename: 87.5 + [20250519_010558.]: Samplename: 87.5 Root: 84.118 --> Root in between the borders! Added to results. Coefficients: -90.57732996633Coefficients: 0.514821742825076Coefficients: 0.00293158941798942Coefficients: 8.04237934904601e-06 - [20250519_005726.]: Samplename: 100 + [20250519_010558.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.287 --> '100 < root < 110' --> substitute 100 - [20250519_005726.]: Solving cubic regression for CpG#3 + [20250519_010558.]: Solving cubic regression for CpG#3 Coefficients: -0.90294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_005726.]: Samplename: 0 + [20250519_010558.]: Samplename: 0 Root: 1.441 --> Root in between the borders! Added to results. Coefficients: -6.57294781144782Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_005726.]: Samplename: 12.5 + [20250519_010558.]: Samplename: 12.5 Root: 10.568 --> Root in between the borders! Added to results. Coefficients: -15.4289478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_005726.]: Samplename: 25 + [20250519_010558.]: Samplename: 25 Root: 24.796 --> Root in between the borders! Added to results. Coefficients: -22.6129478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_005726.]: Samplename: 37.5 + [20250519_010558.]: Samplename: 37.5 Root: 35.952 --> Root in between the borders! Added to results. Coefficients: -32.7754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_005726.]: Samplename: 50 + [20250519_010558.]: Samplename: 50 Root: 50.684 --> Root in between the borders! Added to results. Coefficients: -43.8889478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_005727.]: Samplename: 62.5 + [20250519_010558.]: Samplename: 62.5 Root: 65.142 --> Root in between the borders! Added to results. Coefficients: -54.9754478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_005727.]: Samplename: 75 + [20250519_010558.]: Samplename: 75 Root: 77.905 --> Root in between the borders! Added to results. Coefficients: -57.6562811447812Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_005727.]: Samplename: 87.5 + [20250519_010558.]: Samplename: 87.5 Root: 80.767 --> Root in between the borders! Added to results. Coefficients: -80.6649478114478Coefficients: 0.62783129228796Coefficients: -0.000797009331409346Coefficients: 2.30555869809204e-05 - [20250519_005727.]: Samplename: 100 + [20250519_010558.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.38 --> '100 < root < 110' --> substitute 100 - [20250519_005727.]: Solving cubic regression for CpG#4 + [20250519_010558.]: Solving cubic regression for CpG#4 Coefficients: -0.597449494949524Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_005727.]: Samplename: 0 + [20250519_010558.]: Samplename: 0 Root: 0.858 --> Root in between the borders! Added to results. Coefficients: -8.25278282828286Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_005727.]: Samplename: 12.5 + [20250519_010558.]: Samplename: 12.5 Root: 12.086 --> Root in between the borders! Added to results. Coefficients: -15.8034494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_005727.]: Samplename: 25 + [20250519_010558.]: Samplename: 25 Root: 23.316 --> Root in between the borders! Added to results. Coefficients: -25.5274494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_005727.]: Samplename: 37.5 + [20250519_010558.]: Samplename: 37.5 Root: 37.383 --> Root in between the borders! Added to results. Coefficients: -33.6369494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_005727.]: Samplename: 50 + [20250519_010558.]: Samplename: 50 Root: 48.353 --> Root in between the borders! Added to results. Coefficients: -50.2554494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_005727.]: Samplename: 62.5 + [20250519_010558.]: Samplename: 62.5 Root: 68.082 --> Root in between the borders! Added to results. Coefficients: -56.5394494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_005727.]: Samplename: 75 + [20250519_010558.]: Samplename: 75 Root: 74.615 --> Root in between the borders! Added to results. Coefficients: -65.5927828282829Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_005727.]: Samplename: 87.5 + [20250519_010558.]: Samplename: 87.5 Root: 83.254 --> Root in between the borders! Added to results. Coefficients: -88.3214494949495Coefficients: 0.697398364598366Coefficients: -0.0015913142857143Coefficients: 3.2166356902357e-05 - [20250519_005727.]: Samplename: 100 + [20250519_010558.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.715 --> '100 < root < 110' --> substitute 100 - [20250519_005727.]: Solving cubic regression for CpG#5 + [20250519_010558.]: Solving cubic regression for CpG#5 Coefficients: -0.623961279461278Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_005727.]: Samplename: 0 + [20250519_010558.]: Samplename: 0 Root: 1.458 --> Root in between the borders! Added to results. Coefficients: -4.76796127946128Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_005727.]: Samplename: 12.5 + [20250519_010558.]: Samplename: 12.5 Root: 10.347 --> Root in between the borders! Added to results. Coefficients: -12.7259612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_005727.]: Samplename: 25 + [20250519_010558.]: Samplename: 25 Root: 24.815 --> Root in between the borders! Added to results. Coefficients: -21.1599612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_005727.]: Samplename: 37.5 + [20250519_010558.]: Samplename: 37.5 Root: 37.902 --> Root in between the borders! Added to results. Coefficients: -30.6954612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_005727.]: Samplename: 50 + [20250519_010558.]: Samplename: 50 Root: 50.977 --> Root in between the borders! Added to results. Coefficients: -39.6579612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_005727.]: Samplename: 62.5 + [20250519_010558.]: Samplename: 62.5 Root: 62.126 --> Root in between the borders! Added to results. Coefficients: -51.6829612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_005727.]: Samplename: 75 + [20250519_010558.]: Samplename: 75 Root: 75.852 --> Root in between the borders! Added to results. Coefficients: -61.0146279461279Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_005727.]: Samplename: 87.5 + [20250519_010558.]: Samplename: 87.5 Root: 85.767 --> Root in between the borders! Added to results. Coefficients: -76.0699612794613Coefficients: 0.422446384479718Coefficients: 0.00375371236171235Coefficients: -4.48459708193036e-06 - [20250519_005727.]: Samplename: 100 + [20250519_010558.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.743 --> '100 < root < 110' --> substitute 100 - [20250519_005727.]: Solving cubic regression for CpG#6 + [20250519_010558.]: Solving cubic regression for CpG#6 Coefficients: -0.196072390572403Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_005727.]: Samplename: 0 + [20250519_010558.]: Samplename: 0 Root: 0.349 --> Root in between the borders! Added to results. Coefficients: -6.73873905723907Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_005727.]: Samplename: 12.5 + [20250519_010558.]: Samplename: 12.5 Root: 11.718 --> Root in between the borders! Added to results. Coefficients: -15.8880723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_005727.]: Samplename: 25 + [20250519_010558.]: Samplename: 25 Root: 26.396 --> Root in between the borders! Added to results. Coefficients: -22.0000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_005727.]: Samplename: 37.5 + [20250519_010558.]: Samplename: 37.5 Root: 35.301 --> Root in between the borders! Added to results. Coefficients: -33.4445723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_005727.]: Samplename: 50 + [20250519_010558.]: Samplename: 50 Root: 50.134 --> Root in between the borders! Added to results. Coefficients: -46.9000723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_005727.]: Samplename: 62.5 + [20250519_010558.]: Samplename: 62.5 Root: 64.993 --> Root in between the borders! Added to results. Coefficients: -55.8320723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_005727.]: Samplename: 75 + [20250519_010558.]: Samplename: 75 Root: 73.639 --> Root in between the borders! Added to results. Coefficients: -71.5454057239057Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_005727.]: Samplename: 87.5 + [20250519_010558.]: Samplename: 87.5 Root: 87.043 --> Root in between the borders! Added to results. Coefficients: -89.6560723905724Coefficients: 0.561022132435467Coefficients: 0.000918637037037021Coefficients: 2.38852884399552e-05 - [20250519_005727.]: Samplename: 100 + [20250519_010558.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 100.329 --> '100 < root < 110' --> substitute 100 - [20250519_005727.]: Solving cubic regression for CpG#7 + [20250519_010558.]: Solving cubic regression for CpG#7 Coefficients: -1.21495454545456Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_005727.]: Samplename: 0 + [20250519_010558.]: Samplename: 0 Root: 2.13 --> Root in between the borders! Added to results. Coefficients: -5.39562121212123Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_005727.]: Samplename: 12.5 + [20250519_010558.]: Samplename: 12.5 Root: 9.973 --> Root in between the borders! Added to results. Coefficients: -11.2649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_005727.]: Samplename: 25 + [20250519_010558.]: Samplename: 25 Root: 22.206 --> Root in between the borders! Added to results. Coefficients: -17.4509545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_005727.]: Samplename: 37.5 + [20250519_010558.]: Samplename: 37.5 Root: 35.814 --> Root in between the borders! Added to results. Coefficients: -26.0314545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_005727.]: Samplename: 50 + [20250519_010558.]: Samplename: 50 Root: 53.28 --> Root in between the borders! Added to results. Coefficients: -33.9649545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_005727.]: Samplename: 62.5 + [20250519_010558.]: Samplename: 62.5 Root: 66.598 --> Root in between the borders! Added to results. Coefficients: -41.1689545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_005727.]: Samplename: 75 + [20250519_010558.]: Samplename: 75 Root: 76.575 --> Root in between the borders! Added to results. Coefficients: -44.1356212121212Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_005727.]: Samplename: 87.5 + [20250519_010558.]: Samplename: 87.5 Root: 80.219 --> Root in between the borders! Added to results. Coefficients: -67.2229545454546Coefficients: 0.579588917748918Coefficients: -0.00436152150072151Coefficients: 4.98010505050505e-05 - [20250519_005727.]: Samplename: 100 + [20250519_010558.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.506 --> '100 < root < 110' --> substitute 100 - [20250519_005727.]: Solving cubic regression for CpG#8 + [20250519_010558.]: Solving cubic regression for CpG#8 Coefficients: -1.09618518518518Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_005727.]: Samplename: 0 + [20250519_010558.]: Samplename: 0 Root: 2.016 --> Root in between the borders! Added to results. Coefficients: -5.44685185185185Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_005727.]: Samplename: 12.5 + [20250519_010558.]: Samplename: 12.5 Root: 9.458 --> Root in between the borders! Added to results. Coefficients: -16.9301851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_005727.]: Samplename: 25 + [20250519_010558.]: Samplename: 25 Root: 26.35 --> Root in between the borders! Added to results. Coefficients: -23.3501851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_005727.]: Samplename: 37.5 + [20250519_010558.]: Samplename: 37.5 Root: 34.728 --> Root in between the borders! Added to results. Coefficients: -37.6251851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_005727.]: Samplename: 50 + [20250519_010558.]: Samplename: 50 Root: 51.781 --> Root in between the borders! Added to results. Coefficients: -48.8261851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_005727.]: Samplename: 62.5 + [20250519_010558.]: Samplename: 62.5 Root: 64.192 --> Root in between the borders! Added to results. Coefficients: -61.6676851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_005727.]: Samplename: 75 + [20250519_010558.]: Samplename: 75 Root: 77.804 --> Root in between the borders! Added to results. Coefficients: -64.5101851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_005727.]: Samplename: 87.5 + [20250519_010558.]: Samplename: 87.5 Root: 80.758 --> Root in between the borders! Added to results. Coefficients: -86.0601851851852Coefficients: 0.534897737694404Coefficients: 0.00447978143338143Coefficients: -1.50060965207632e-05 - [20250519_005727.]: Samplename: 100 + [20250519_010558.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 102.834 --> '100 < root < 110' --> substitute 100 - [20250519_005727.]: Solving cubic regression for CpG#9 + [20250519_010558.]: Solving cubic regression for CpG#9 Coefficients: -0.989865319865327Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_005727.]: Samplename: 0 + [20250519_010558.]: Samplename: 0 Root: 1.475 --> Root in between the borders! Added to results. Coefficients: -6.39586531986533Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_005727.]: Samplename: 12.5 + [20250519_010558.]: Samplename: 12.5 Root: 10.12 --> Root in between the borders! Added to results. Coefficients: -14.7058653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_005727.]: Samplename: 25 + [20250519_010558.]: Samplename: 25 Root: 24.844 --> Root in between the borders! Added to results. Coefficients: -20.6238653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_005727.]: Samplename: 37.5 + [20250519_010558.]: Samplename: 37.5 Root: 35.327 --> Root in between the borders! Added to results. Coefficients: -31.3958653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_005727.]: Samplename: 50 + [20250519_010558.]: Samplename: 50 Root: 51.855 --> Root in between the borders! Added to results. Coefficients: -42.6858653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_005727.]: Samplename: 62.5 + [20250519_010558.]: Samplename: 62.5 Root: 65.265 --> Root in between the borders! Added to results. Coefficients: -52.9033653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_005727.]: Samplename: 75 + [20250519_010558.]: Samplename: 75 Root: 74.915 --> Root in between the borders! Added to results. Coefficients: -65.492531986532Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_005727.]: Samplename: 87.5 + [20250519_010558.]: Samplename: 87.5 Root: 84.67 --> Root in between the borders! Added to results. Coefficients: -92.9898653198653Coefficients: 0.67899229597563Coefficients: -0.00542281173641174Coefficients: 7.72300426487093e-05 - [20250519_005727.]: Samplename: 100 + [20250519_010558.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.082 --> '100 < root < 110' --> substitute 100 - [20250519_005727.]: Solving cubic regression for row_means + [20250519_010558.]: Solving cubic regression for row_means Coefficients: -0.771215488215478Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 - [20250519_005727.]: Samplename: 0 + [20250519_010558.]: Samplename: 0 Root: 1.287 --> Root in between the borders! Added to results. Coefficients: -6.47247474747474Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 - [20250519_005727.]: Samplename: 12.5 + [20250519_010558.]: Samplename: 12.5 Root: 10.847 --> Root in between the borders! Added to results. Coefficients: -14.8972154882155Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 - [20250519_005727.]: Samplename: 25 + [20250519_010558.]: Samplename: 25 Root: 24.737 --> Root in between the borders! Added to results. Coefficients: -22.1492154882155Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 - [20250519_005727.]: Samplename: 37.5 + [20250519_010558.]: Samplename: 37.5 Root: 36.02 --> Root in between the borders! Added to results. Coefficients: -32.5259932659933Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 - [20250519_005727.]: Samplename: 50 + [20250519_010558.]: Samplename: 50 Root: 50.639 --> Root in between the borders! Added to results. Coefficients: -44.7218821548821Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 - [20250519_005727.]: Samplename: 62.5 + [20250519_010558.]: Samplename: 62.5 Root: 65.497 --> Root in between the borders! Added to results. Coefficients: -54.4032154882155Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 - [20250519_005727.]: Samplename: 75 + [20250519_010558.]: Samplename: 75 Root: 75.751 --> Root in between the borders! Added to results. Coefficients: -62.4313636363636Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 - [20250519_005727.]: Samplename: 87.5 + [20250519_010558.]: Samplename: 87.5 Root: 83.403 --> Root in between the borders! Added to results. Coefficients: -84.7343265993266Coefficients: 0.600117857944524Coefficients: -0.000629959275292592Coefficients: 2.88923726150392e-05 - [20250519_005727.]: Samplename: 100 + [20250519_010558.]: Samplename: 100 ## WARNING ## No fitting root within the borders found. Positive numeric root found: Root: 101.573 --> '100 < root < 110' --> substitute 100 - [20250519_005727.]: + [20250519_010558.]: ### Starting with regression calculations ### - [20250519_005727.]: Entered 'regression_type1'-Function - [20250519_005727.]: # CpG-site: CpG#1 + [20250519_010558.]: Entered 'regression_type1'-Function + [20250519_010558.]: # CpG-site: CpG#1 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) - [20250519_005727.]: Logging df_agg: CpG#1 - [20250519_005727.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005727.]: c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)[20250519_005727.]: c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)[20250519_005727.]: c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) - [20250519_005727.]: Entered 'hyperbolic_regression'-Function - [20250519_005727.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005727.]: Entered 'cubic_regression'-Function - [20250519_005727.]: 'cubic_regression': minmax = FALSE - [20250519_005727.]: # CpG-site: CpG#2 + [20250519_010558.]: Logging df_agg: CpG#1 + [20250519_010558.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010558.]: c(1.33401421032361, 11.4464168649749, 22.2276337872205, 36.3736525123061, 52.0438002576114, 66.1443249010516, 75.864353455204, 82.2543632311352, 100)[20250519_010558.]: c(1.33401421032361, 1.0535831350251, 2.7723662127795, 1.1263474876939, 2.0438002576114, 3.6443249010516, 0.864353455203997, 5.2456367688648, 0)[20250519_010558.]: c(NA, 8.4286650802008, 11.089464851118, 3.00359330051706, 4.0876005152228, 5.83091984168257, 1.15247127360533, 5.9950134501312, 0) + [20250519_010558.]: Entered 'hyperbolic_regression'-Function + [20250519_010558.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010558.]: Entered 'cubic_regression'-Function + [20250519_010558.]: 'cubic_regression': minmax = FALSE + [20250519_010559.]: # CpG-site: CpG#2 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) - [20250519_005727.]: Logging df_agg: CpG#2 - [20250519_005727.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005727.]: c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)[20250519_005727.]: c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)[20250519_005727.]: c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) - [20250519_005727.]: Entered 'hyperbolic_regression'-Function - [20250519_005727.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005727.]: Entered 'cubic_regression'-Function - [20250519_005727.]: 'cubic_regression': minmax = FALSE - [20250519_005727.]: # CpG-site: CpG#3 + [20250519_010559.]: Logging df_agg: CpG#2 + [20250519_010559.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010559.]: c(0.548629212600373, 11.5334942360619, 26.6282428579604, 35.509046298922, 47.8509004857888, 66.8931714037845, 75.4313106569591, 84.1184829423144, 100)[20250519_010559.]: c(0.548629212600373, 0.9665057639381, 1.6282428579604, 1.990953701078, 2.1490995142112, 4.39317140378451, 0.431310656959099, 3.3815170576856, 0)[20250519_010559.]: c(NA, 7.7320461115048, 6.51297143184161, 5.30920986954133, 4.2981990284224, 7.02907424605521, 0.575080875945465, 3.86459092306926, 0) + [20250519_010559.]: Entered 'hyperbolic_regression'-Function + [20250519_010559.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010559.]: Entered 'cubic_regression'-Function + [20250519_010559.]: 'cubic_regression': minmax = FALSE + [20250519_010559.]: # CpG-site: CpG#3 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) - [20250519_005727.]: Logging df_agg: CpG#3 - [20250519_005727.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005727.]: c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)[20250519_005727.]: c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)[20250519_005727.]: c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) - [20250519_005727.]: Entered 'hyperbolic_regression'-Function - [20250519_005727.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005727.]: Entered 'cubic_regression'-Function - [20250519_005727.]: 'cubic_regression': minmax = FALSE - [20250519_005727.]: # CpG-site: CpG#4 + [20250519_010559.]: Logging df_agg: CpG#3 + [20250519_010559.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010559.]: c(1.44072654766676, 10.5677206698424, 24.7956529081379, 35.9519154174756, 50.6840128730794, 65.1415439321287, 77.905329956603, 80.767122912268, 100)[20250519_010559.]: c(1.44072654766676, 1.9322793301576, 0.2043470918621, 1.5480845825244, 0.684012873079404, 2.6415439321287, 2.905329956603, 6.73287708773201, 0)[20250519_010559.]: c(NA, 15.4582346412608, 0.817388367448402, 4.12822555339841, 1.36802574615881, 4.22647029140592, 3.87377327547066, 7.69471667169372, 0) + [20250519_010559.]: Entered 'hyperbolic_regression'-Function + [20250519_010559.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010559.]: Entered 'cubic_regression'-Function + [20250519_010559.]: 'cubic_regression': minmax = FALSE + [20250519_010559.]: # CpG-site: CpG#4 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) - [20250519_005727.]: Logging df_agg: CpG#4 - [20250519_005727.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005727.]: c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)[20250519_005727.]: c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)[20250519_005727.]: c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) - [20250519_005727.]: Entered 'hyperbolic_regression'-Function - [20250519_005727.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005727.]: Entered 'cubic_regression'-Function - [20250519_005727.]: 'cubic_regression': minmax = FALSE - [20250519_005727.]: # CpG-site: CpG#5 + [20250519_010559.]: Logging df_agg: CpG#4 + [20250519_010559.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010559.]: c(0.858335161098707, 12.0855313705714, 23.3164186343997, 37.3830070750476, 48.3526815121735, 68.0824341519511, 74.6152796890845, 83.2536964524017, 100)[20250519_010559.]: c(0.858335161098707, 0.414468629428599, 1.6835813656003, 0.116992924952399, 1.6473184878265, 5.58243415195111, 0.384720310915498, 4.2463035475983, 0)[20250519_010559.]: c(NA, 3.31574903542879, 6.7343254624012, 0.311981133206397, 3.29463697565301, 8.93189464312177, 0.512960414553997, 4.85291834011235, 0) + [20250519_010559.]: Entered 'hyperbolic_regression'-Function + [20250519_010559.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010559.]: Entered 'cubic_regression'-Function + [20250519_010559.]: 'cubic_regression': minmax = FALSE + [20250519_010559.]: # CpG-site: CpG#5 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) - [20250519_005727.]: Logging df_agg: CpG#5 - [20250519_005727.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005727.]: c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)[20250519_005727.]: c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)[20250519_005727.]: c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) - [20250519_005727.]: Entered 'hyperbolic_regression'-Function - [20250519_005727.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005727.]: Entered 'cubic_regression'-Function - [20250519_005727.]: 'cubic_regression': minmax = FALSE - [20250519_005727.]: # CpG-site: CpG#6 + [20250519_010559.]: Logging df_agg: CpG#5 + [20250519_010559.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010559.]: c(1.45815885872158, 10.3470047908467, 24.8150085082726, 37.902202434763, 50.9768599213374, 62.1264944886855, 75.8515940245021, 85.766767827257, 100)[20250519_010559.]: c(1.45815885872158, 2.1529952091533, 0.184991491727398, 0.402202434762998, 0.9768599213374, 0.373505511314498, 0.851594024502106, 1.733232172743, 0)[20250519_010559.]: c(NA, 17.2239616732264, 0.739965966909594, 1.07253982603466, 1.9537198426748, 0.597608818103197, 1.13545869933614, 1.98083676884914, 0) + [20250519_010559.]: Entered 'hyperbolic_regression'-Function + [20250519_010559.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010559.]: Entered 'cubic_regression'-Function + [20250519_010559.]: 'cubic_regression': minmax = FALSE + [20250519_010559.]: # CpG-site: CpG#6 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) - [20250519_005727.]: Logging df_agg: CpG#6 - [20250519_005727.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005727.]: c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)[20250519_005727.]: c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)[20250519_005727.]: c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) - [20250519_005727.]: Entered 'hyperbolic_regression'-Function - [20250519_005727.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005727.]: Entered 'cubic_regression'-Function - [20250519_005727.]: 'cubic_regression': minmax = FALSE - [20250519_005727.]: # CpG-site: CpG#7 + [20250519_010559.]: Logging df_agg: CpG#6 + [20250519_010559.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010559.]: c(0.349289777689709, 11.718186424346, 26.3959840124278, 35.3009019621403, 50.1335677299922, 64.9927731962402, 73.6385743787925, 87.0433563205787, 100)[20250519_010559.]: c(0.349289777689709, 0.781813575654001, 1.3959840124278, 2.1990980378597, 0.133567729992201, 2.49277319624019, 1.3614256212075, 0.456643679421305, 0)[20250519_010559.]: c(NA, 6.25450860523201, 5.5839360497112, 5.86426143429253, 0.267135459984402, 3.98843711398431, 1.81523416161, 0.521878490767205, 0) + [20250519_010559.]: Entered 'hyperbolic_regression'-Function + [20250519_010559.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010559.]: Entered 'cubic_regression'-Function + [20250519_010559.]: 'cubic_regression': minmax = FALSE + [20250519_010559.]: # CpG-site: CpG#7 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) - [20250519_005727.]: Logging df_agg: CpG#7 - [20250519_005727.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005727.]: c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)[20250519_005727.]: c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)[20250519_005727.]: c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) - [20250519_005727.]: Entered 'hyperbolic_regression'-Function - [20250519_005727.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005727.]: Entered 'cubic_regression'-Function - [20250519_005727.]: 'cubic_regression': minmax = FALSE - [20250519_005727.]: # CpG-site: CpG#8 + [20250519_010559.]: Logging df_agg: CpG#7 + [20250519_010559.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010559.]: c(2.12953119975094, 9.97257098314617, 22.2059559709309, 35.8143078912917, 53.2798169545709, 66.5977007121001, 76.5753720723248, 80.2192820049015, 100)[20250519_010559.]: c(2.12953119975094, 2.52742901685383, 2.7940440290691, 1.6856921087083, 3.2798169545709, 4.09770071210011, 1.5753720723248, 7.2807179950985, 0)[20250519_010559.]: c(NA, 20.2194321348306, 11.1761761162764, 4.49517895655547, 6.55963390914179, 6.55632113936017, 2.10049609643306, 8.32082056582686, 0) + [20250519_010559.]: Entered 'hyperbolic_regression'-Function + [20250519_010559.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010559.]: Entered 'cubic_regression'-Function + [20250519_010559.]: 'cubic_regression': minmax = FALSE + [20250519_010559.]: # CpG-site: CpG#8 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) - [20250519_005727.]: Logging df_agg: CpG#8 - [20250519_005727.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005727.]: c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)[20250519_005727.]: c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)[20250519_005727.]: c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) - [20250519_005727.]: Entered 'hyperbolic_regression'-Function - [20250519_005727.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005727.]: Entered 'cubic_regression'-Function - [20250519_005727.]: 'cubic_regression': minmax = FALSE - [20250519_005727.]: # CpG-site: CpG#9 + [20250519_010559.]: Logging df_agg: CpG#8 + [20250519_010559.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010559.]: c(2.01554288922103, 9.4575966611002, 26.3496745529898, 34.7279879576046, 51.7805031081493, 64.1918409049086, 77.803935663705, 80.7580214011447, 100)[20250519_010559.]: c(2.01554288922103, 3.0424033388998, 1.3496745529898, 2.7720120423954, 1.7805031081493, 1.6918409049086, 2.803935663705, 6.7419785988553, 0)[20250519_010559.]: c(NA, 24.3392267111984, 5.3986982119592, 7.39203211305441, 3.56100621629859, 2.70694544785376, 3.73858088494001, 7.70511839869177, 0) + [20250519_010559.]: Entered 'hyperbolic_regression'-Function + [20250519_010559.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010559.]: Entered 'cubic_regression'-Function + [20250519_010559.]: 'cubic_regression': minmax = FALSE + [20250519_010559.]: # CpG-site: CpG#9 c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) - [20250519_005727.]: Logging df_agg: CpG#9 - [20250519_005727.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005727.]: c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)[20250519_005727.]: c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)[20250519_005727.]: c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) - [20250519_005727.]: Entered 'hyperbolic_regression'-Function - [20250519_005727.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005727.]: Entered 'cubic_regression'-Function - [20250519_005727.]: 'cubic_regression': minmax = FALSE - [20250519_005727.]: # CpG-site: row_means + [20250519_010559.]: Logging df_agg: CpG#9 + [20250519_010559.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010559.]: c(1.4748520772151, 10.1196517054927, 24.843641290935, 35.3267260117639, 51.8546848506009, 65.2652545321194, 74.9150847744697, 84.6698630277555, 100)[20250519_010559.]: c(1.4748520772151, 2.3803482945073, 0.156358709065, 2.1732739882361, 1.8546848506009, 2.7652545321194, 0.0849152255302954, 2.8301369722445, 0)[20250519_010559.]: c(NA, 19.0427863560584, 0.625434836259998, 5.79539730196293, 3.70936970120179, 4.42440725139104, 0.113220300707061, 3.23444225399372, 0) + [20250519_010559.]: Entered 'hyperbolic_regression'-Function + [20250519_010559.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010559.]: Entered 'cubic_regression'-Function + [20250519_010559.]: 'cubic_regression': minmax = FALSE + [20250519_010559.]: # CpG-site: row_means c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) - [20250519_005727.]: Logging df_agg: row_means - [20250519_005727.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_005727.]: c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)[20250519_005727.]: c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)[20250519_005727.]: c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) - [20250519_005727.]: Entered 'hyperbolic_regression'-Function - [20250519_005727.]: 'hyperbolic_regression': minmax = FALSE - [20250519_005727.]: Entered 'cubic_regression'-Function - [20250519_005727.]: 'cubic_regression': minmax = FALSE - [20250519_005728.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Test_error_CpG1_corrected_h.png - [20250519_005730.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Test_error_CpG2_corrected_h.png - [20250519_005731.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Test_error_CpG3_corrected_h.png - [20250519_005733.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Test_error_CpG4_corrected_h.png - [20250519_005735.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Test_error_CpG5_corrected_h.png - [20250519_005737.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Test_error_CpG6_corrected_h.png - [20250519_005738.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Test_error_CpG7_corrected_h.png - [20250519_005740.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Test_error_CpG8_corrected_h.png - [20250519_005742.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Test_error_CpG9_corrected_h.png - [20250519_005744.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/Rtmp6ERd32/plotdir/Test_error_rowmeans_corrected_h.png - [20250519_005746.]: on_start: using future::plan("sequential") - [20250519_005747.]: on_start: using future::plan("sequential") + [20250519_010559.]: Logging df_agg: row_means + [20250519_010559.]: c(0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100)[20250519_010559.]: c(1.28674218034491, 10.8474062821721, 24.737384351039, 36.0200815402329, 50.6393222494118, 65.4974814516656, 75.7507242961973, 83.4027053898488, 100)[20250519_010559.]: c(1.28674218034491, 1.6525937178279, 0.262615648960999, 1.4799184597671, 0.639322249411798, 2.99748145166561, 0.750724296197305, 4.0972946101512, 0)[20250519_010559.]: c(NA, 13.2207497426232, 1.050462595844, 3.9464492260456, 1.2786444988236, 4.79597032266497, 1.00096572826307, 4.68262241160137, 0) + [20250519_010559.]: Entered 'hyperbolic_regression'-Function + [20250519_010559.]: 'hyperbolic_regression': minmax = FALSE + [20250519_010559.]: Entered 'cubic_regression'-Function + [20250519_010559.]: 'cubic_regression': minmax = FALSE + [20250519_010559.]: Creating barplot No. 1 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Test_error_CpG1_corrected_h.png + [20250519_010601.]: Creating barplot No. 2 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Test_error_CpG2_corrected_h.png + [20250519_010603.]: Creating barplot No. 3 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Test_error_CpG3_corrected_h.png + [20250519_010605.]: Creating barplot No. 4 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Test_error_CpG4_corrected_h.png + [20250519_010606.]: Creating barplot No. 5 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Test_error_CpG5_corrected_h.png + [20250519_010608.]: Creating barplot No. 6 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Test_error_CpG6_corrected_h.png + [20250519_010610.]: Creating barplot No. 7 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Test_error_CpG7_corrected_h.png + [20250519_010612.]: Creating barplot No. 8 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Test_error_CpG8_corrected_h.png + [20250519_010613.]: Creating barplot No. 9 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Test_error_CpG9_corrected_h.png + [20250519_010615.]: Creating barplot No. 10 - filename: /tmp/th798/17369309/Rtmp3aMbm2/plotdir/Test_error_rowmeans_corrected_h.png + [20250519_010617.]: on_start: using future::plan("sequential") + [20250519_010619.]: on_start: using future::plan("sequential") [ FAIL 4 | WARN 209 | SKIP 12 | PASS 42 ] == Skipped tests (12) ========================================================== > library(data.table, lib.loc=R.home("library")) > (sig.diff.dt <- myDiff(Rvers)) Key: Empty data.table (0 rows and 3 cols): checking,master,release > > ## If there are significant differences, use git bisect to find when > ## they started. > if(nrow(sig.diff.dt)){ + dt.git <- file.path(task.dir, "data.table.git") + system(paste("cd ~/R/data.table && git fetch --tags")) + system(paste("git clone ~/R/data.table", dt.git)) + release.tag <- gsub(".tar.gz|.*_", "", cargs[["release"]]) + rev.parse.cmd <- paste( + "cd", dt.git, "&& git rev-parse master") + master.sha <- system(rev.parse.cmd, intern=TRUE) + merge.base.cmd <- paste( + "cd", dt.git, "&& git merge-base master", release.tag) + merge.base.sha <- system(merge.base.cmd, intern=TRUE) + old.sha <- merge.base.sha + run_R <- file.path(proj.dir, "install_dt_then_check_dep.R") + sig.diff.dt[, first.bad.commit := NA_character_] + sig.diff.dt[, comments := NA_character_] + for(diff.i in 1:nrow(sig.diff.dt)){ + sig.diff.row <- sig.diff.dt[diff.i] + bisect.cmd <- paste( + "cd", dt.git, "&&", + "git bisect start &&", + "git bisect old", old.sha, "&&", + "git bisect new master &&", + "git bisect run", + R.home('bin/Rscript'), + run_R, + shQuote(sig.diff.row$checking), + sig.diff.row$release, + rev.dep.release.tar.gz, + release.tag) + print(bisect.cmd) + bisect.out <- system(bisect.cmd, intern=TRUE) + cat(bisect.out,sep="\n") + if(is.null(attr(bisect.out,"status"))){ + first.bad.sha <- nc::capture_all_str( + bisect.out, + sha="[0-9a-f]+", + " is the first new commit")$sha + parent.cmd <- paste( + "cd ~/R/data.table && git log --pretty=%P -n 1", + first.bad.sha) + parent.sha <- system(parent.cmd, intern=TRUE) + sig.diff.dt[diff.i, first.bad.commit := first.bad.sha] + parent.msg <- paste0("parent=", parent.sha) + this.comment <- if(parent.sha==old.sha){ + paste(parent.msg, "same as git bisect old") + }else if(first.bad.sha==master.sha){ + paste("same as git bisect new=master,", parent.msg) + }else{ + parent.msg + } + sig.diff.dt[diff.i, comments := this.comment] + } + } + ## add CRAN column. + sig.diff.dt[, CRAN := { + flavor <- get_flavor(Rvers) + details <- data.table(flavor=unique(flavor))[, { + base <- "https://www.r-project.org/nosvn/R.check/" + u <- paste0(base, flavor, "/", rev.dep, "-00check.txt") + check.txt <- tempfile() + tryCatch({ + download.file(u, check.txt, quiet=TRUE) + }, error=function(e){ + NULL + }) + check.lines <- if(file.exists(check.txt)){ + readLines(check.txt,encoding="UTF-8") + }else{ + "" + } + repl.lines <- gsub("[\u2018\u2019]", "'", check.lines) + ##gsub("[‘’]", "'", check.lines) does not work with LC_ALL=C. + myStatus(line.vec=repl.lines) + }, by=flavor] + select.dt <- data.table(flavor, checking) + details[select.dt, msg, on=.(flavor, checking)] + }] + dir.create(file.path(job.dir, Rvers)) + diffs.csv <- file.path(job.dir, Rvers, "significant_differences.csv") + data.table::fwrite(sig.diff.dt, diffs.csv) + print(sig.diff.dt) + } >